Digital services can be:

Note, however, that (especially in the current digitally transforming market) a service previously thought of as “back office” (when run internally) becomes “market-facing” when developed as a profit-seeking offering. For example, a human resources system built internally is “back office”, but Workday is a directly market-facing product, even though the two services may be similar in functionality.

In positioning a digital offering, one must consider the likelihood of its being adopted. Is it part of a broader “movement” of technological innovation? Where is the customer base in terms of its willingness to adopt the innovation? A well-known approach is the idea of "diffusion theory”, first researched by Everett Rogers and proposed in his Diffusion of Innovations, [236].

Rogers' research proposed the idea of “Adopter Categorization on the Basis of Innovativeness”, with a well-known graphic (see Figure 7, “Technology Adoption Categories (Rogers)”, similar to [236] Figure 7-3, p.281).

Rogers went on to characterize the various stages:

Steve Blank, in The Four Steps to Epiphany [36], argues there are four categories for startups (p.31):

Understanding which category you are attempting is critical, because “the four types of startups have very different rates of customer adoption and acceptance”.

Another related and well-known categorization of competitive strategies comes from Michael Treacy and Fred Wiersma [287]:

It is not difficult to categorize well-known brands in this way:

However, deciding which strategy to pursue as a startup may require some experimentation.

In understanding IT value, it is essential to clarify the definitions of user, customer, and sponsor, and understand their perspectives and motivations. Sometimes, the user is the customer. But more often, the user and the customer are different, and the role of system or service sponsor may additionally need to be distinguished.

The following definitions may help:

Depending on the service type, these roles can be filled by the same or different people. Here are some examples:

So, who paid for the user’s enjoyment? The bank and restaurant both had clear motivation for supporting a better online experience, and people now expect that service organizations provide this. The bank experiences less customer turnover and increased likelihood that customers add additional services. The restaurant sees increased traffic and smoother flow from more efficient reservations. Both see increased competitiveness.

The traffic application is a somewhat different story. While it is an engineering marvel, there is still some question as to how to fund it long term. It requires a large user base to operate, and yet end consumers of the service are unlikely to pay for it. At this writing, the service draws on advertising dollars from businesses wishing to advertise to passersby, and also sells its real-time data on traffic patterns to a variety of customers, such as developers considering investments along given routes.

This last example illustrates the maxim (attributed to media theorist and writer Douglas Rushkoff [265]) that “if you don’t know how the product is making money, you are the product”.

Evidence of Notability

The context for the existence and operation of a digital system is fundamental to its existence; the digital system in fact typically operates as part of a larger sociotechnical system. The cybernetics literature [301, 25] provides theoretical grounding. The IT Service Management (ITSM) literature is also concerned with the context for IT services, in terms of the desired outcomes they provide to end consumers [282].

Limitations

Understanding a product context is important; however, there is feedback between a product and its context, so no amount of initial analysis will be able to accurately predict the ultimate outcome of fielding a given digital product (internally or externally). A concrete example is the concept of a network effect, in which a product becomes more valuable due to the size of its user base [117].

Related Topics

One recent book that has been influential among entrepreneurs is Alex Osterwalder’s Business Model Generation [215]. This document is perhaps best known for introducing the concept of the Business Model Canvas, which it defines as: “a shared language for describing, visualizing, assessing, and changing business models”. The Business Model Canvas uses nine major categories to describe the business model:

and suggests they be visualized as in Figure 8, “Business Model Canvas” (similar to [215], p.44).

The canvas is then used in collaborative planning; e.g., as a large format wall poster where the business team can brainstorm, discuss, and fill in the boxes (e.g., what is the main “Value Proposition"? Mobile bank account access?).

Osterwalder and his colleagues, in Business Model Generation and the follow-up Value Proposition Design [216], suggest a wide variety of imaginative and creative approaches to developing business models and value propositions, in terms of patterns, processes, design approaches, and overall strategy.

There are a wide variety of analysis techniques for making a business case at a more detailed level. Donald Reifer, in Making the Software Business Case [228], lists:

Empirical, experimental approaches are essential to digital management. Any analysis, carried to an extreme without a sound basis in real data, risks becoming a “castle in the air”. But when real money is on the line (even the opportunity costs of the time you are spending on your startup), it is advisable to look at the decision from various perspectives. These techniques can be useful for that purpose. However, once you have some indication there might be business value in a given idea, applying Lean Startup techniques may be more valuable than continuing to analyze.

Lean Startup is a philosophy of entrepreneurship developed by Eric Ries [232]. It is not specific to IT; rather, it is broadly applicable to all attempts to understand a product and its market. (According to our definition of product management a workable market position is essential to any product.)

The idea of the Lean Startup has had profound influence on product design, including market-facing and even internal IT systems. It is grounded in Agile concepts such as:

“Do the simplest thing that could possibly work.”

Lean Startup calls for an iterative, “Build-Measure-Learn” cycle (see Figure 9, “Lean Startup Flowchart”, summary of ideas in [232]). Repeating this cycle frequently is the essential process of building a successful startup (whatever the digital proportion):

Flowcharts such as Figure 9, “Lean Startup Flowchart” are often seen to describe the Lean Startup process.

A Digital Practitioner often starts by thinking about value creation through their digital products or services. However, now is also the time to think about protecting that digital value. Your customers will be sharing data about themselves, their preferences, and even financial information; e.g., credit cards to make purchases. Failure to protect that data can irreparably damage any other digital value you manage to create; it will certainly damage your or your organization’s reputation, and may have financial consequences.

Architects of buildings need to appreciate that the physical materials that will implement their creations need to be strong enough to produce the envisioned construct. Similarly, a Digital Practitioner needs to understand that software can be weak and they need to appreciated how to examine the software artifacts for the precursors of those weaknesses that would threaten the operational capabilities and integrity of their envisioned constructs.

Attack surface analysis, design reviews for security weaknesses, static source code analysis for weaknesses, dynamic fuzz testing, adversary-based pen testing, and binary analysis are all techniques used to gain confidence that dangerous failure modes of the software-based system are not rampant and that some evidence-based argument can be made about the adequacy of the rigor used to create the software.

In the building of buildings, this is done by the engineering elements of a team, along with building code inspectors and material scientists, but the analogous activities are not the norm in software systems. So, as you go through both the analysis and description phase and the construction phase, keep in mind that that all software has strengths and weaknesses and needs to be checked for weaknesses that would impact the intended functionality, reasoning, and logic. See the [Common Weakness Enumeration (Mitre)] for examples of weaknesses with security, performance, reliability, and maintainability consequences.

As software-enabled elements become more entwined in our physical lives, both at work and not, there needs to be attention paid to this line of thinking in the education of the software-based systems work force.

A deeper treatment of this subject can be found in the later chapter on Security and in The Open Group Guide to Integrating Risk and Security Within a TOGAF® Enterprise Architecture.

Evidence of Notability

The complex process of discovering and supporting digital value is covered in industry work on Digital Transformation [298, 81]. It is also addressed as an important sub-topic within the product management literature, especially at its intersection with Agile. Product management is a large and growing professional community, with a major professional organization (the Product Development and Marketing Association) and many less formalized meetings and groups. It has a correspondingly rich body of professional literature [36, 53, 114]. Product management is also a major topic at Agile conferences.

Limitations

Some digital efforts are more instrumental, and provide value in the same way that a cog provides value to a machine. They have little independence. Discussions of value imply greater autonomy to act on the analysis. Business case analysis would rarely be applied in the engineering of a small component; similarly, business case analysis makes less sense with digital systems whose existence is required by a larger whole. It is at the level of that larger whole that value analysis should take place.

Related Topics

Any human-facing digital service can be seen as delivering a “moment of truth”. In terms of digital systems, this English-language cliche (elaborated into an important service concept by SAS group president Jan Carlzon [55]) represents the user’s outcome, their experience of value. All discussions of digital value should either start or end there.

In order to view a bank balance, a user may use an application downloaded from a “store” of applications made available to her device. On her device, this “app” is part of an intricate set of components performing functions such as:

The application, or “app”, downloaded to the phone plays a primary role, but is enabled by:

Of course, without the banking systems on the other end, there is no bank balance to transmit. These systems are similar, but on a much larger scale than the end user’s device:

Consider: what does all this mean to the user? Does she care about cell phone towers, or middleware, or triply redundant industrial-strength Power Distribution Units ? Usually, not in the least. Therefore, as we study this world, we need to maintain awareness of her perspective. The user is seeking some value that digital technology uniquely can enable, but does not want to consider all the complexity that goes into it. She just wants to go out with friends. The moment of truth (see Figure 10, “The Digital Stack Supports the Moment of Truth”) depends on the service; the service may contain great complexity, but part of its success lies in shielding the user from that complexity.

The outcome of a digital service (e.g., an account balance lookup for an online banking application) is supported by a complex, layered structure of technology. For example, a simple systems architecture might be represented in layers as:

Architecture also uses a layered method, but in a different way that is more abstracted from the particular and concrete to the conceptual; for example:

Evidence of Notability

The use of layered abstractions in digital systems engineering is well established. Such abstractions may be highly technical, such as the OSI stack describing layered networking protocols [154], or more conceptual, such as the Zachman Framework [313].

Limitations

Sometimes, organizations attempt to structure themselves around the stack, with separate functional units for user interface, middleware, data management, network, and so forth. This approach may result in monolithic, hard to change systems. See Conway’s Law.

Related Topics

Description

The digital or IT service is based on a complex stack of technology, from local devices to global networks to massive data centers. Software and hardware are layered together in endlessly inventive ways to solve problems people did not even know they had ten years ago. However, these IT service systems must come from somewhere. They must be designed, built, and operated, and continually improved over time. A simple representation of the IT service lifecycle is:

The digital service/product evolves over time, through many repetitions ("iterations") of the improvement cycle. An expanding spiral is a useful visualization:

This entire process, from idea to decommissioning (“inspire to retire”) can be understood as the service lifecycle (see Figure 12, “The Digital Service Lifecycle”). Sometimes, the service lifecycle is simplified as "plan, build, run"; however, this can lead to the assumption that only one iteration is required, which is in general incorrect in digital systems. Multiple iterations should be assumed as the product is fine-tuned and evolves to meet changing demand.

We can combine the service experience (moment of truth) with the service/product lifecycle into the “dual-axis value chain” (originally presented in [32]):

The dual-axis value chain can be seen in many representations of IT and digital delivery systems. Product evolution flows from right to left, while day-to-day value flows up, through the technical stack. It provides a basis for (among other things) thinking about the IT user, customer, and sponsor, which we will cover in the next section.

DevOps is discussed in depth later in this document. At this point, however, as originally conceived in The Phoenix Project [165], there are three core DevOps principles applicable at the earliest stages of the digital product:

DevOps emphasizes speeding up the flow of value in the product lifecycle (left to right), and the feedback of learning (right to left) "at all stages of the value stream". When this is done consistently at scale over time, DevOps advocates argue that the result is a: "generative, high-trust culture that supports a dynamic, disciplined, and scientific approach to experimentation and risk-taking, facilitating the creation of organizational learning, both from our successes and failures. Furthermore, by continually shortening and amplifying our feedback loops, we create ever-safer systems of work and are better able to take risks and perform experiments that help us learn faster than our competition and win in the marketplace." [166 p. 12].

Limitations

The limitations of relying on a lifecycle model as a basis for systems implementation are by now well known. Attempting to fully understand a system’s "requirements" prior to any development can lead to project failure, and deferring integration of sub-modules until late in a project also is risky. See Agile canon for further discussion (e.g., [250, 220, 230, 68]). Nevertheless, the concept of the lifecycle remains a useful model; these state transitions exist and drive organizational behavior.

Evidence of Notability

The evidence for this topic’s importance is seen across much guidance for software and systems professionals. Examples include the original statement of "waterfall" development [241], the overlapping phases of the Rational Unified Process [227], the ITIL Service Lifecycle’s "strategy/design/transition/operate/improve," [282], the clear reach and influence of the DevOps movement, and many more.

Related Topics

Compute is the resource that performs the rapid, clock-driven digital logic that transforms data inputs to outputs.

Software is the thing that structures the logic and reasoning of the “compute” and allows for the dynamic use of inputs to vary the output following the logic and reasoning laid down by the software developer. While the computers process instructions at the level of “true” and “false”, represented as binary “1s” and “0s”, because humans cannot easily understand binary data and processing, higher-level abstractions of machine code and programming languages are used.

It is critical to understand that computers, traditionally understood, can only operate in precise, "either-or" ways. Computers are often used to automate business processes, but in order to do so, the process needs to be carefully defined, with no ambiguity. Complications and nuances, intuitive understandings, judgment calls — in general, computers can’t do any of this, unless and until you program them to — at which point the logic is no longer intuitive or a judgment call.

Creating programs for a specific functionality is challenging in two different ways:

Computer processing is not free. Moving data from one point to another — the fundamental transmission of information — requires matter and energy, and is bound up in physical reality and the laws of thermodynamics. The same applies for changing the state of data, which usually involves moving it somewhere, operating on it, and returning it to its original location. In the real world, even running the simplest calculation has physical and therefore economic cost, and so we must pay for computing.

Storage is the act of computation that is bound up with the concept of state, but they are also distinct. Computation is a process; state is a condition. Many technologies have been used for digital storage [71]. Increasingly, the IT professional need not be concerned with the physical infrastructure used for storing data. Storage increasingly is experienced as a virtual resource, accessed through executing programmed logic on cloud platforms. “Underneath the covers” the cloud provider might be using various forms of storage, from Random Access Memory (RAM) to solid state drives to tapes, but the end user is, ideally, shielded from the implementation details (part of the definition of a service).

In general, storage follows a hierarchy. Just as we might “store” a document by holding it in our hands, setting it on a desktop, filing it in a cabinet, or archiving it in a banker’s box in an offsite warehouse, so computer storage also has different levels of speed and accessibility:

With a computing process, one can change the state of some data, store it, or move it. The last is the basic concern of networking, to transmit data (or information) from one location to another. We see evidence of networking every day; coaxial cables for cable TV, or telephone lines strung from pole to pole in many areas. However, like storage, there is also a hierarchy of networking:

Like storage and compute, networking as a service increasingly is independent of implementation. The developer uses programmatic tools to define expected information transmission, and (ideally) need not be concerned with the specific networking technologies or architectures serving their needs.

Evidence of Notability

Limitations

Related Topics

Description

Assume a simple, physical computer such as a laptop. When the laptop is first turned on, the OS loads; the OS is itself software, but is able to directly control the computer’s physical resources: its Central Processing Unit (CPU), memory, screen, and any interfaces such as WiFi, USB, and Bluetooth. The OS (in a traditional approach) then is used to run “applications” such as web browsers, media players, word processors, spreadsheets, and the like. Many such programs can also be run as applications within the browser, but the browser still needs to be run as an application.

In the simplest form of virtualization, a specialized application known as a hypervisor is loaded like any other application. The purpose of this hypervisor is to emulate the hardware computer in software. Once the hypervisor is running, it can emulate any number of “virtual” computers, each of which can have its own OS (see Figure 14, “Virtualization is Computers within a Computer”). The hypervisor mediates the "virtual machine" access to the actual, physical hardware of the laptop; the virtual machine can take input from the USB port, and output to the Bluetooth interface, just like the master OS that launched when the laptop was turned on.

There are two different kinds of hypervisors. The example we just discussed was an example of a Type 2 hypervisor, which runs on top of a host OS. In a Type 1 hypervisor, a master host OS is not used; the hypervisor runs on the “bare metal” of the computer and in turn “hosts” multiple virtual machines.

Paravirtualization, e.g., containers, is another form of virtualization found in the marketplace. In a paravirtualized environment, a core OS is able to abstract hardware resources for multiple virtual guest environments without having to virtualize hardware for each guest. The benefit of this type of virtualization is increased Input/Output (I/O) efficiency and performance for each of the guest environments.

However, while hypervisors can support a diverse array of virtual machines with different OSs on a single computing node, guest environments in a paravirtualized system generally share a single OS. See Figure 15, “Virtualization Types” for an overview of all the types.

Virtualization attracted business attention as a means to consolidate computing workloads. For years, companies would purchase servers to run applications of various sizes, and in many cases the computers were badly underutilized. Because of configuration issues and (arguably) an overabundance of caution, average utilization in a pre-virtualization data center might average 10-20%. That’s up to 90% of the computer’s capacity being wasted (see Figure 16, “Inefficient Utilization”).

The above figure is a simplification. Computing and storage infrastructure supporting each application stack in the business were sized to support each workload. For example, a payroll server might run on a different infrastructure configuration than a Data Warehouse (DW) server. Large enterprises needed to support hundreds of different infrastructure configurations, increasing maintenance and support costs.

The adoption of virtualization allowed businesses to compress multiple application workloads onto a smaller number of physical servers (see Figure 17, “Efficiency through Virtualization”).

In most virtualized architectures, the physical servers supporting workloads share a consistent configuration, which makes it easy to add and remove resources from the environment. The virtual machines may still vary greatly in configuration, but the fact of virtualization makes managing that easier — the virtual machines can be easily copied and moved, and increasingly can be defined as a form of code.

Virtualization thus introduced a new design pattern into the enterprise where computing and storage infrastructure became commoditized building blocks supporting an ever-increasing array of services. But what about where the application is large and virtualization is mostly overhead? Virtualization still may make sense in terms of management consistency and ease of system recovery.

Containers (paravirtualization) have emerged as a powerful and convenient technology for managing various workloads. Architectures based on containers running in Cloud platforms, with strong API provisioning and integrated support for load balancing and autoscaling, are called "cloud-native". The perceived need for a standardized control plane for containers resulted in various initiatives in the 2010s: Docker Swarm, Apache Mesos®, and (emerging as the de facto standard), the Cloud Native Computing Foundation’s Kubernetes.

Kubernetes is an open source orchestration platform based on the following primitives (see Figure 18, “Kubernetes Infrastructure, Pods, and Services”):

Kubernetes management (see Figure 19, “Kubernetes Cluster Architecture”) is performed via a Master controller which supervisees the nodes. This consists of:

The nodes run:

Much other additional functionality is available and under development; the Kubernetes ecosystem as of 2019 is growing rapidly.

Graphics similar to those presented in [305+]+.

Competency Category "Virtualization" Example Competencies

Evidence of Notability

Virtualization was predicted in the earliest theories that led to the development of computers. Turing and Church realized that any general-purpose computer could emulate any other. Virtual systems have existed in some form since at latest 1967 — only 20 years after the first fully functional computers.

Virtualization is discussed extensively in core computer science and engineering texts and is an essential foundation of cloud computing.

The Cloud-Native community is at this writing (2019) one of the most active communities in computing.

Limitations

Virtualization is mainly relevant to production computing. It is less relevant to edge devices.

Related Topics

Two computers may both run the same version of an OS, and yet exhibit vastly different behaviors. This is due to how they are configured. One may have web serving software installed; the other may run a database. One may be accessible to the public via the Internet; access to the other may be tightly restricted to an internal network. The parameters and options for configuring general-purpose computers are effectively infinite. Mis-configurations are a common cause of outages and other issues.

In years past, infrastructure administrators relied on the ad hoc issuance of commands either at an operations console or via a GUI-based application. Such commands could also be listed in text files; i.e., "batch files" or "shell scripts" to be used for various repetitive processes, but systems administrators by tradition and culture were empowered to issue arbitrary commands to alter the state of the running system directly.

However, it is becoming more and more rare for a systems administrator to actually “log in” to a server and execute configuration-changing commands in an ad hoc manner. Increasingly, all actual server configuration is based on pre-developed specification.

Because virtualization is becoming so powerful, servers increasingly are destroyed and rebuilt at the first sign of any trouble. In this way, it is certain that the server’s configuration is as intended. This again is a relatively new practice.

Previously, because of the expense and complexity of bare-metal servers, and the cost of having them offline, great pains were taken to fix troubled servers. Systems administrators would spend hours or days troubleshooting obscure configuration problems, such as residual settings left by removed software. Certain servers might start to develop “personalities”. Industry practice has changed dramatically here since around 2010.

As cloud infrastructures have scaled, there has been an increasing need to configure many servers identically. Auto-scaling (adding more servers in response to increasing load) has become a widely used strategy as well. Both call for increased automation in the provisioning of IT infrastructure. It is simply not possible for a human being to be hands on at all times in configuring and enabling such infrastructures, so automation is called for.

Sophisticated Infrastructure as Code techniques are an essential part of modern SRE practices such as those used by Google®. Auto-scaling, self-healing systems, and fast deployments of new features all require that infrastructure be represented as code for maximum speed and reliability of creation.

Infrastructure as Code is defined by Morris as: an approach to infrastructure automation based on practices from software development. It emphasizes consistent, repeatable routines for provisioning and changing systems and their configuration. Changes are made to definitions and then rolled out to systems through unattended processes that include thorough validation. [203+]+

In presenting Infrastructure as Code at its simplest, we will start with the concept of a shell script. Consider the following set of commands:

What does this do? It tells the computer:

A user with the appropriate permissions at a UNIX® or Linux® command prompt who runs those commands will wind up with a configuration that could be visualized as in Figure 21, “Simple Directory/File Structure Script”. Directory and file layouts count as configuration and in some cases are critical.

Assume further that the same set of commands is entered into a text file thus:

The file might be named iac.sh, and with its permissions set correctly, it could be run so that the computer executes all the commands, rather than a person running them one at a time at the console. If we did so in an empty directory, we would again wind up with that same configuration.

Beyond creating directories and files shell scripts can create and destroy virtual servers and containers, install and remove software, set up and delete users, check on the status of running processes, and much more.

Consider again the iac.sh file. It is valuable. It documents intentions for how a given configuration should look. It can be run reliably on thousands of machines, and it will always give us two directories and six files. In terms of the previous section, we might choose to run it on every new server we create. Perhaps it should be established it as a known resource in our technical ecosystem. This is where version control and the broader concept of configuration management come in.

For example, a configuration file may be developed specifying the capacity of a virtual server, and what software is to be installed on it. This artifact can be checked into version control and used to re-create an equivalent server on-demand.

Tracking and controlling such work products as they evolve through change after change is important for companies of any size. The practice applies to computer code, configurations, and, increasingly, documentation, which is often written in a lightweight markup language like Markdown or Asciidoc. In terms of infrastructure, configuration management requires three capabilities:

Version control is critical for any kind of system with complex, changing content, especially when many people are working on that content. Version control provides the capability of seeing the exact sequence of a complex system’s evolution and isolating any particular moment in its history or providing detailed analysis on how two versions differ. With version control, we can understand what changed and when – which is essential to coping with complexity.

While version control was always deemed important for software artifacts, it has only recently become the preferred paradigm for managing infrastructure state as well. Because of this, version control is possibly the first IT management system you should acquire and implement (perhaps as a cloud service, such as Github, Gitlab, or Bitbucket).

Version control in recent years increasingly distinguishes between source control and package management (see Figure 22, “Types of Version Control” and Figure 27, “Configuration Management and its Components” below): the management of binary files, as distinct from human-understandable symbolic files. It is also important to understand what versions are installed on what computers; this can be termed “deployment management”. (With the advent of containers, this is a particularly fast-changing area.)

Version control works like an advanced file system with a memory. (Actual file systems that do this are called versioning file systems.) It can remember all the changes you make to its contents, tell you the differences between any two versions, and also bring back the version you had at any point in time.

Survey research presented in the annual State of DevOps report indicates that version control is one of the most critical practices associated with high-performing IT organizations [44]. Forsgren [98] summarizes the practice of version control as:

Digital systems start with text files; e.g., those encoded in ASCII or Unicode. Text editors create source code, scripts, and configuration files. These will be transformed in defined ways (e.g., by compilers and build tools) but the human-understandable end of the process is mostly based on text files. In the previous section, we described a simple script that altered the state of a computer system. We care very much about when such a text file changes. One wrong character can completely alter the behavior of a large, complex system. Therefore, our configuration management approach must track to that level of detail.

Source control is at its most powerful when dealing with textual data. It is less useful in dealing with binary data, such as image files. Text files can be analyzed for their differences in an easy to understand way (see Figure 23, “Source Control”). If “abc” is changed to “abd”, then it is clear that the third character has been changed from “c” to “d”. On the other hand, if we start with a digital image (e.g., a *.png file), alter one pixel, and compare the resulting before and after binary files in terms of their data, it would be more difficult to understand what had changed. We might be able to tell that they are two different files easily, but they would look very similar, and the difference in the binary data might be difficult to understand.

The “Commit” Concept

Although implementation details may differ, all version control systems have some concept of “commit”. As stated in Version Control with Git [181]:

In Git, a commit is used to record changes to a repository …​ Every Git commit represents a single, atomic changeset with respect to the previous state. Regardless of the number of directories, files, lines, or bytes that change with a commit … either all changes apply, or none do. [emphasis added]

The concept of a version or source control “commit” serves as a foundation for IT management and governance. It both represents the state of the computing system as well as providing evidence of the human activity affecting it. The “commit” identifier can be directly referenced by the build activity, which in turn is referenced by the release activity, which typically visible across the IT value chain.

Also, the concept of an atomic “commit” is essential to the concept of a “branch” — the creation of an experimental version, completely separate from the main version, so that various alterations can be tried without compromising the overall system stability. Starting at the point of a “commit”, the branched version also becomes evidence of human activity around a potential future for the system. In some environments, the branch is automatically created with the assignment of a requirement or story. In other environments, the very concept of branching is avoided. The human-understandable, contextual definitions of IT resources is sometimes called metadata.

Much if not most software, once created as some kind of text-based artifact suitable for source control, must be compiled and further organized into deployable assets, often called “packages” (see Figure 24, “Building Software”).

In some organizations, it was once common for compiled binaries to be stored in the same repositories as source code (see Figure 25, “Common Version Control”). However, this is no longer considered a best practice. Source and package management are now viewed as two separate things (see Figure 26, “Source versus Package Repos”). Source repositories should be reserved for text-based artifacts whose differences can be made visible in a human-understandable way. Package repositories in contrast are for binary artifacts that can be deployed.

Package repositories also can serve as a proxy to the external world of downloadable software. That is, they are a cache, an intermediate store of the software provided by various external or “upstream” sources. For example, developers may be told to download the approved Ruby on Rails version from the local package repository, rather than going to get the latest version, which may not be suitable for the environment.

Package repositories furthermore are used to enable collaboration between teams working on large systems. Teams can check in their built components into the package repository for other teams to download. This is more efficient than everyone always building all parts of the application from the source repository.

The boundary between source and package is not hard and fast, however. We sometimes sees binary files in source repositories, such as images used in an application. Also, when interpreted languages (such as JavaScript™) are “packaged”, they still appear in the package as text files, perhaps compressed or otherwise incorporated into some larger containing structure.

While in earlier times, systems would be compiled for the target platform (e.g., compiled in a development environment, and then re-compiled for subsequent environments such as quality assurance and production) the trend today is decisively towards immutability. With the standardization brought by container-based architecture, current preference increasingly is to compile once into an immutable artifact that is deployed unchanged to all environments, with any necessary differences managed by environment-specific configuration such as source-managed text artifacts and shared secrets repositories.

Version control is an important part of the overall concept of configuration management. But configuration management also covers the matter of how artifacts under version control are combined with other IT resources (such as virtual machines) to deliver services. Figure 27, “Configuration Management and its Components” elaborates on Figure 22, “Types of Version Control” to depict the relationships.

Resources in version control in general are not yet active in any value-adding sense. In order for them to deliver experiences, they must be combined with computing resources: servers (physical or virtual), storage, networking, and the rest, whether owned by the organization or leased as cloud services. The process of doing so is called deployment. Version control manages the state of the artifacts; meanwhile, deployment management (as another configuration management practice) manages the combination of those artifacts with the needed resources for value delivery.

Before we turned to source control, we looked at a simple script that changed the configuration of a computer. It did so in an imperative fashion. Imperative and declarative are two important terms from computer science.

In an imperative approach, one tells the computer specifically how we want to accomplish a task; e.g.:

Many traditional programming languages take an imperative approach. A script such as our iac.sh example is executed line by line; i.e., it is imperative.

In configuring infrastructure, scripting is in general considered “imperative”, but state-of-the-art infrastructure automation frameworks are built using a “declarative”, policy-based approach, in which the object is to define the desired end state of the resource, not the steps needed to get there. With such an approach, instead of defining a set of steps, we simply define the proper configuration as a target, saying (in essence) that “this computer should always have a directory structure thus; do what you need to do to make it so and keep it this way”.

Declarative approaches are used to ensure that the proper versions of software are always present on a system and that configurations such as Internet ports and security settings do not vary from the intended specification.

This is a complex topic, and there are advantages and disadvantages to each approach [47].

Evidence of Notability

Andrew Clay Shafer, credited as one of the originators of DevOps, stated: "In software development, version control is the foundation of every other Agile technical practice. Without version control, there is no build, no test-driven development, no continuous integration" [14 p. 99]. It is one of the four foundational areas of Agile, according to the Agile Alliance [10].

Limitations

Older platforms and approaches relied on direct command line intervention and (in the 1990s and 2000s) on GUI-based configuration tools. Organizations still relying on these approaches may struggle to adopt the principles discussed here.

Competency Category "Configuration Management and Infrastructure as Code" Example Competencies

Related Topics

Since the advent of cloud computing, securing cloud infrastructure has been a key concern. Most of the security issues that exist in non-cloud environments exist in cloud services as well. In other words, access control, user authentication, vulnerability management, patching, securing network access, anti-malware capabilities, data loss prevention, encryption of data, and a host of other security controls that we deploy in on-premises computing require careful consideration in cloud services. The security concerns around cloud computing vary depending on whether the cloud service is SaaS, PaaS, or IaaS.

There are also fundamental differences in security controls deployed in on-premises infrastructure (security controls may be physical or virtual), and those deployed in cloud infrastructure (which is purely virtual). These differences follow on from the shift brought by cloud computing. In on-premises computing, security architects and security solution providers had access to the physical computing networks, so physical security devices could be deployed in-line. The most common security design patterns leverage this physical access. In cloud services, there is no ability to insert security components which are physically in-line. This means that in cloud computing, we may have to utilize virtual security appliances, and virtual network segmentation solutions such as VLANs and Software-Defined Networks (SDNs) versus physical security approaches.

Another difference in securing physical versus cloud infrastructure arises in defining and implementing microsegmentation (small zones of access control). In physical networks, multiple hardware firewalls are required to achieve this. In cloud computing, VLANs and SDNs may be used to deliver equivalent capability, with some unique advantages (they are more manageable, at a lower capital expense).

In addition, the responsibility for securing cloud infrastructure varies considerably based upon the service model as well. While early focus on cloud security tended to focus on potential security concerns and gaps in security capabilities, the security community today generally acknowledges that while security concerns relating to cloud computing persist, there is also an opportunity for cloud services to “raise the bar”, improving upon baseline security for many customer organizations. Hybrid cloud computing combining public cloud services with private cloud infrastructure brings further complexity to infrastructure security.

Evidence of Notability

The need to secure computing infrastructure has been obvious and self-evident for decades, and has evolved alongside changes in popular computing paradigms, including the mainframe era, client/server computing, and now cloud computing. The need for specific, unique guidance relating to securing cloud services of various types emerged in 2009, when the Cloud Security Alliance (CSA) was first formed, and when they published Version 1 of their Security Guidance for Critical Areas of Focus in Cloud Computing. The CSA guidance is now on Version 4, and includes 14 different security domains.

Limitations

Organizations accustomed to deploying physical security capabilities on their own infrastructure may find it difficult to adapt to the challenges of securing cloud infrastructure in the various types cloud services. They may also have challenges adapting to the changes in responsibilities that are brought by the use of cloud services, where the Cloud Service Provider (CSP) is responsible for delivering many security capabilities, especially in SaaS services, and as a result the customer organization needs to specify needed security capabilities in Request for Proposals (RFPs). In addition, incident response management routines will require change.

Related Topics

The application or digital product development process starts with a concept of intended outcome.

In order to design and build a digital product, the Digital Practitioner needs to express what theory needs the product to do. The conceptual tool used to do this has historically been termed the Requirement. The literal word “Requirement” has fallen out of favor with the rise of Agile [217], and has a number of synonyms and variations:

While these may differ in terms of focus and scope, the basic concept is the same — the requirement, however named, expresses some outcome, intent, or constraint the system must fulfill. This intent calls for work to be performed.

Requirements management is classically taught using the "shall" format. For example, the system shall provide …​, the system shall be capable of …​, etc.

More recently, Agile-aligned teams sometimes prefer user story mapping [217]. Here is an example from [68]:

“As a shopper, I can select how I want items shipped based on the actual costs of shipping to my address so that I can make the best decision.”

The basic format is:

As a <type of user>, I want <goal>, so that <some value>.

The story concept is flexible and can be aggregated and decomposed in various ways, as we will discuss in Section 6.2.1, “Product Management”. Our interest here is in the basic stimulus for application development work that it represents.

The analysis and design of software-based systems itself employs a variety of techniques. Starting from the documented system intent, in general, the thought process will seek to answer questions such as:

A variety of tools and approaches may be used in analysis and design. Sometimes, the analysis and design is entirely internal to the person building the system. Sometimes, it may be sketched on paper or a whiteboard. There are a wide variety of more formalized approaches (process models, data models, systems models) used as these systems and organizations scale up; these will be discussed in future Competency Areas.

When an apparently feasible approach is determined, construction may commence. How formalized "apparently feasible" is depends greatly on the organization and scale of the system. "You start coding and I’ll go find out what the users want" is an old joke in IT development. It represents a long-standing pair of questions: Are we ready to start building? Are we engaged in excessive analysis - sometimes called "analysis paralysis"? Actually writing source code and executing it, preferably with knowledgeable stakeholders evaluating the results, provides unambiguous confirmation of whether a given approach is feasible.

Actual construction techniques will typically center around the creation of text files in specialized computing languages such as C++, Javascript, Java®, Ruby on Rails, or Python. These languages are the fundamental mechanisms for accessing the core digital infrastructure services of compute, transmission, and storage discussed previously. There is a vast variety of instructional material available on the syntax and appropriate techniques for using such languages.

Evaluating whether a developed system fulfills the intended outcome is generally called testing. There is a wide variety of testing types, such as:

Although testing is logically distinct from construction, in modern practices they are tightly integrated and automated, as will be discussed below.

Finally, the system completes construction and testing activities - it must be made available (delivered or transitioned) into a state where it can fulfill its intended outcomes. This is sometimes called the state of "production", discussed below. Delivery may take two forms:

Delivery is increasingly automated, as will be covered in the section on DevOps technical practices.

Evidence of Notability

The basic concepts of the "software lifecycle" as expressed here are broadly discussed in software engineering; e.g., [140, 276, 266].

Limitations

Application construction, including programming source code, is not necessary (in general) when consuming SaaS. Many companies prefer to avoid development as much as possible, relying on commercially available services. Such companies still may be pursuing a digital strategy in important regards.

Related Topics

When a new analyst would join a large systems integrator Andersen Consulting (now Accenture) in 1998, they would be schooled in something called the Business Integration Method (BIM). The BIM was a classic expression of what is called “waterfall development".

What is waterfall development? It is a controversial question. Walker Royce, the original theorist who coined the term named it in order to critique it [241]. Military contracting and management consultancy practices, however, embraced it, as it provided an illusion of certainty. The fact that computer systems until recently included a substantial component of hardware systems engineering may also have contributed.

Waterfall development as a term has become associated with a number of practices. The original illustration was similar to Figure 29, “Waterfall Lifecycle” (similar to [241]):

First, requirements need to be extensively captured and analyzed before the work of development can commence. So, the project team would develop enormous spreadsheets of requirements, spending weeks on making sure that they represented what “the customer” wanted. The objective was to get the customer’s signature. Any further alterations could be profitably billed as “change requests”.

The analysis phase was used to develop a more structured understanding of the requirements; e.g., conceptual and logical data models, process models, business rules, and so forth.

In the design phase, the actual technical platforms would be chosen; major subsystems determined with their connection points, initial capacity analysis (volumetrics) translated into system sizing, and so forth. (Perhaps hardware would not be ordered until this point, leading to issues with developers now being “ready”, but hardware not being available for weeks or months yet.)

Only after extensive requirements, analysis, and design would coding take place (implementation). Furthermore, there was a separation of duties between developers and testers. Developers would write code and testers would try to break it, filing bug reports to which the developers would then need to respond.

Another model sometimes encountered at this time was the V-model (see Figure 30, “V-Model”^[2]). This was intended to better represent the various levels of abstraction operating in the systems delivery activity. Requirements operate at various levels, from high-level business intent through detailed specifications. It is all too possible that a system is “successfully” implemented at lower levels of specification, but fails to satisfy the original higher-level intent.

The failures of these approaches at scale are by now well known. Large distributed teams would wrestle with thousands of requirements. The customer would “sign off” on multiple large binders, with widely varying degrees of understanding of what they were agreeing to. Documentation became an end in itself and did not meet its objectives of ensuring continuity if staff turned over. The development team would design and build extensive product implementations without checking the results with customers. They would also defer testing that various component parts would effectively interoperate until the very end of the project, when the time came to assemble the whole system.

Failure after failure of this approach is apparent in the historical record [111]. Recognition of such failures, dating from the 1960s, led to the perception of a “software crisis”.

However, many large systems were effectively constructed and operated during the “waterfall years", and there are reasonable criticisms of the concept of a “software crisis” [39].

Successful development efforts existed back to the earliest days of computing (otherwise, there probably wouldn’t be computers, or at least not so many). Many of these successful efforts used prototypes and other means of building understanding and proving out approaches. But highly publicized failures continued, and a substantial movement against “waterfall” development started to take shape.

By the 1990s, a number of thought leaders in software development had noticed some common themes with what seemed to work and what didn’t. Kent Beck developed a methodology known as “eXtreme Programming” (XP) [24]. XP pioneered the concepts of iterative, fast-cycle development with ongoing stakeholder feedback, coupled with test-driven development, ongoing refactoring, pair programming, and other practices. (More on the specifics of these in the next section.)

Various authors assembled in 2001 and developed the Agile Manifesto [8], which further emphasized an emergent set of values and practices:

The Manifesto authors further stated:

See http://agilemanifesto.org/.

Agile methodologists emphasize that software development is a learning process. In general, learning (and the value derived from it) is not complete until the system is functioning to some degree of capability. As such, methods that postpone the actual, integrated verification of the system increase risk. Alistair Cockburn visualizes risk as the gap between the ongoing expenditure of funds and the lag in demonstrating valuable learning (see Figure 31, “Waterfall Risk”, similar to [66]).

Because Agile approaches emphasize delivering smaller batches of complete functionality, this risk gap is minimized (see Figure 32, “Agile Risk”, similar to [66]).

The Agile models for developing software aligned with the rise of cloud and web-scale IT. As new customer-facing sites like Flickr®, Amazon, Netflix, Etsy®, and Facebook scaled to massive proportions, it became increasingly clear that waterfall approaches were incompatible with their needs. Because these systems were directly user-facing, delivering monetized value in fast-moving competitive marketplaces, they required a degree of responsiveness previously not seen in “back-office” IT or military-aerospace domains (the major forms that large-scale system development had taken to date). We will talk more of product-centricity and the overall DevOps movement in the next section.

This new world did not think in terms of large requirements specifications. Capturing a requirement, analyzing and designing to it, implementing it, testing that implementation, and deploying the result to the end user for feedback became something that needed to happen at speed, with high repeatability. Requirements “backlogs” were (and are) never “done”, and increasingly were the subject of ongoing re-prioritization, without high-overhead project “change” barriers.

These user-facing, web-based systems integrate the SDLC tightly with operational concerns. The sheer size and complexity of these systems required much more incremental and iterative approaches to delivery, as the system can never be taken offline for the “next major release” to be installed. New functionality is moved rapidly in small chunks into a user-facing, operational status, as opposed to previous models where vendors would develop software on an annual or longer version cycle, to be packaged onto media for resale to distant customers.

Contract software development never gained favor in the Silicon Valley web-scale community; developers and operators are typically part of the same economic organization. So, it was possible to start breaking down the walls between “development” and “operations”, and that is just what happened.

Large-scale systems are complex and unpredictable. New features are never fully understood until they are deployed at scale to the real end user base. Therefore, large-scale web properties also started to “test in production” (more on this in the Operations Competency Area) in the sense that they would deploy new functionality to only some of their users. Rather than trying to increase testing to understand things before deployment better, these new firms accepted a seemingly higher-level of risk in exposing new functionality sooner. (Part of their belief is that it actually is lower risk because the impacts are never fully understood in any event.)

Evidence of Notability

See [174] for a thorough history of Agile and its antecedents. Agile is recognized as notable in leading industry and academic guidance [276, 140] and has a large, active, and highly visible community (see http://www.agilealliance.org). It is increasingly influential on non-software activities as well [234, 233].

Limitations

Agile development is not as relevant when packaged software is acquired. Such software has a more repeatable pattern of implementation, and more up-front planning may be appropriate.

Related Topics

“DevOps” is a broad term, encompassing product management, continuous delivery, organization structure, team behaviors, and culture (see Figure 33, “DevOps Definition”). Some of these topics will not be covered until Contexts II and III in this document. At an execution level, the fundamental goal of moving smaller changes more quickly through the pipeline is a common theme. Other guiding principles include: “If it hurts, do it more frequently”. (This is in part a response to the poor practice, or antipattern, of deferring integration testing and deployment until those tasks are so big as to be unmanageable.) There is a great deal written on the topic of DevOps currently; the Humble/Farley book is recommended as an introduction. Let’s go into a little detail on some essential Agile/DevOps practices:

The infrastructure Competency Area suggests that the Digital Practitioner may need to select:

The assumption is that the Digital Practitioner is going to start immediately with a continuous delivery pipeline.

What is meant by a continuous delivery pipeline? Figure 34, “A Simple Continuous Delivery Toolchain” presents a simplified, starting overview.

First, some potential for value is identified. It is refined through product management techniques into a feature — some specific set of functionality that when complete will enable the value proposition (i.e., as a moment of truth).

Testing software and systems is a critically important part of digital product development. The earliest concepts of waterfall development called for it explicitly, and “software tester” as a role and “software quality assurance” as a practice have long histories. Evolutionary approaches to software have a potential major issue with software testing:

This issue was and is well known to thought leaders in Agile software development. The key response has been the concept of automated testing so that any change in the software can be immediately validated before more development along those lines continues. One pioneering tool was JUnit:

From the reality that regression testing was “very costly” (as stated by Brooks in the above quote), the emergence of tools like JUnit (coupled with increasing computer power and availability) changed the face of software development, allowing the ongoing evolution of software systems in ways not previously possible.

In test-driven development, the idea essence is to write code that tests itself, and in fact to write the test before writing any code. This is done through the creation of test harnesses and the tight association of tests with requirements. The logical culmination of test-driven development was expressed by Kent Beck in eXtreme Programming: write the test first [24]. Thus:

Employing test-driven development completely and correctly requires thought and experience. But it has emerged as a practice in the largest-scale systems in the world. Google runs many millions of automated tests daily [300]. It has even been successfully employed in hardware development [118].

Test-driven development enables the next major practice, that of refactoring. Refactoring is how technical debt is addressed. What is technical debt? Technical debt is a term coined by Ward Cunningham and is now defined by Wikipedia as:

…​ the eventual consequences of poor system design, software architecture, or software development within a codebase. The debt can be thought of as work that needs to be done before a particular job can be considered complete or proper. If the debt is not repaid, then it will keep on accumulating interest, making it hard to implement changes later on …​ Analogous to monetary debt, technical debt is not necessarily a bad thing, and sometimes technical debt is required to move projects forward. [303]

Test-driven development ensures that the system’s functionality remains consistent, while refactoring provides a means to address technical debt as part of ongoing development activities. Prioritizing the relative investment of repaying technical debt versus developing new functionality will be examined in future sections.

Technical debt is covered further in here.

As systems engineering approaches transform to cloud and Infrastructure as Code, a large and increasing percentage of IT work takes the form of altering text files and tracking their versions. This was covered in the discussion of configuration management with artifacts such as scripts being created to drive the provisioning and configuring of computing resources. Approaches which encourage ongoing development and evolution are increasingly recognized as less risky since systems do not respond well to big “batches” of change. An important concept is that of “continuous integration”, popularized by Paul Duvall in his book of the same name [92].

In order to understand why continuous integration is important, it is necessary to discuss further the concept of source control and how it is employed in real-world settings. Imagine Mary has been working for some time with her partner Aparna in their startup (or on a small team) and they have three code modules (see Figure 35, “File B being Worked on by Two People”). Mary is writing the web front end (file A), Aparna is writing the administrative tools and reporting (file C), and they both partner on the data access layer (file B). The conflict, of course, arises on file B that they both need to work on. A and C are mostly independent of each other, but changes to any part of B can have an impact on both their modules.

If changes are frequently needed to B, and yet they cannot split it into logically separate modules, they have a problem; they cannot both work on the same file at the same time. They are each concerned that the other does not introduce changes into B that “break” the code in their own modules A and C.

In smaller environments, or under older practices, perhaps there is no conflict, or perhaps they can agree to take turns. But even if they are taking turns, Mary still needs to test her code in A to make sure it has not been broken by changes Aparna made in B. And what if they really both need to work on B (see Figure 35, “File B being Worked on by Two People”) at the same time?

Given that they have version control in place, each of them works on a “local” copy of the file (see illustration “File B being worked on by two people”).

That way, they can move ahead on their local workstations. But when the time comes to combine both of their work, they may find themselves in “merge hell”. They may have chosen very different approaches to solving the same problem, and code may need massive revision to settle on one codebase. For example, in the accompanying illustration, Mary’s changes to B are represented by triangles and Aparna’s are represented by circles. They each had a local version on their workstation for far too long, without talking to each other.

The diagrams represent the changes graphically; of course, with real code, the different graphics represent different development approaches each person took. For example, Mary had certain needs for how errors were handled, while Aparna had different needs.

In Figure 36, “Merge Hell”, where triangles and circles overlap, Mary and Aparna painstakingly have to go through and put in a consolidated error handling approach, so that the code supports both of their needs. The problem, of course, is there are now three ways errors are being handled in the code. This is not good, but they did not have time to go back and fix all the cases. This is a classic example of technical debt.

Suppose instead that they had been checking in every day. They can identify the first collision quickly (see Figure 37, “Catching Errors Quickly is Valuable”), and have a conversation about what the best error handling approach is. This saves them both the rework of fixing the collisions, and the technical debt they might have otherwise accepted:

These problems have driven the evolution of software configuration management for decades. In previous methods, to develop a new release, the code would be copied into a very long-lived “branch” (a version of the code to receive independent enhancement). Ongoing “maintenance” fixes of the existing codebase would also continue, and the two codebases would inevitably diverge. Switching over to the “new” codebase might mean that once-fixed bugs (bugs that had been addressed by maintenance activities) would show up again, and, logically, this would not be acceptable. So, when the newer development was complete, it would need to be merged back into the older line of code, and this was rarely if ever easy (again, “merge hell”). In a worst-case scenario, the new development might have to be redone.

Enter continuous integration (see Figure 38, “Big Bang versus Continuous Integration”). As presented in [92] the key practices (note similarities to the pipeline discussion) include:

Rather than locking a file so that only one person can work on it at a time, it has been found that the best approach is to allow developers to actually make multiple copies of such a file or file set and work on them simultaneously.

This is the principle of continuous integration at work. If the developers are continually pulling each other’s work into their own working copies, and continually testing that nothing has broken, then distributed development can take place. So, for a developer, the day’s work might be as follows:

8 AM: check out files from master source repository to a local branch on the workstation. Because files are not committed unless they pass all tests, the code is clean. The developer selects or is assigned a user story (requirement) that they will now develop.

8:30 AM: The developer defines a test and starts developing the code to fulfill it.

10 AM: The developer is close to wrapping up the first requirement. They check the source repository. Their partner has checked in some new code, so they pull it down to their local repository. They run all the automated tests and nothing breaks, so all is good.

10:30 AM: They complete their first update of the day; it passes all tests on the local workstation. They commit it to the master repository. The master repository is continually monitored by the build server, which takes the code created and deploys it, along with all necessary configurations, to a dedicated build server (which might be just a virtual machine or transient container). All tests pass there (the test defined as indicating success for the module, as well as a host of older tests that are routinely run whenever the code is updated).

11:00 AM: Their partner pulls these changes into their working directory. Unfortunately, some changes made conflict with some work the partner is doing. They briefly consult and figure out a mutually-acceptable approach.

Controlling simultaneous changes to a common file is only one benefit of continuous integration. When software is developed by teams, even if each team has its own artifacts, the system often fails to “come together” for higher-order testing to confirm that all the parts are working correctly together. Discrepancies are often found in the interfaces between components; when component A calls component B, it may receive output it did not expect and processing halts. Continuous integration ensures that such issues are caught early.

DevOps and continuous delivery call for automating everything that can be automated. This goal led to the creation of continuous integration managers such as Hudson, Jenkins, Travis CI, and Bamboo. Build managers may control any or all of the following steps:

Build managers play a critical, central role in the modern, automated pipeline and will likely be a center of attention for the new Digital Practitioner in their career.

Once the software is compiled and built, the executable files that can be installed and run operationally should be checked into a package manager. At that point, the last mile steps can be taken, and the now tested and built software can be deployed to pre-production or production environments (see Figure 39, “Deployment”). The software can undergo usability testing, load testing, integration testing, and so forth. Once those tests are passed, it can be deployed to production.

Moving new code into production has always been a risky procedure. Changing a running system always entails some uncertainty. However, the practice of Infrastructure as Code coupled with increased virtualization has reduced the risk. Often, a rolling release strategy is employed so that code is deployed to small sets of servers while other servers continue to service the load. This requires careful design to allow the new and old code to co-exist at least for a brief time.

This is important so that the versions of software used in production are well controlled and consistent. The package manager can then be associated with some kind of deploy tool that keeps track of what versions are associated with which infrastructure.

Timing varies by organization. Some strive for true “continuous deployment”, in which the new code flows seamlessly from developer commit through build, test, package, and deploy. Others put gates in between the developer and check-in to mainline, or source-to-build, or package-to-deploy so that some human governance remains in the toolchain. This document goes into more detail on these topics in the section on digital operations.

Release management, and the concept of a “release”, are among the most important and widely seen concepts in all forms of digital management. Regardless of working in a cutting-edge Agile startup with two people or one of the largest banks with a portfolio of thousands of applications, releases for coordination and communication are likely being used.

What is a “release?”. Betz defined it this way in other work: “A significant evolution in an IT service, often based on new systems development, coordinated with affected services and stakeholders”. Release management’s role is to “Coordinate the assembly of IT functionality into a coherent whole and deliver this package into a state in which the customer is getting the intended value” [31 p. 68, 31 p. 119].

Evidence of Notability

DevOps has not yet been fully recognized for its importance in academic guidance or peer-reviewed literature. Nevertheless, its influence is broad and notable. Significant publications include [92, 13, 136, 165, 166, 99]. Large international conferences (notably the DevOps Enterprise Summit, https://itrevolution.com/devops_events/) are dedicated to the event, as well as many smaller local events under the banner of "DevOpsDays" (https://www.devopsdays.org/).

Limitations

Like Agile, DevOps is primarily valuable in the development of new digital functionality. It has less relevance for organizations that choose to purchase digital functionality; e.g., as SaaS offerings. While it includes the fragment "Ops", it does not cover the full range of operational topics covered in the Operations Competency Area, such as help desk and field services.

Related Topics

Three smaller software modules may be able to do the job of one monolithic program; however, those three modules must then communicate in some form. There are a variety of ways that this can occur; for example, communication may be via a shared data store. The preferred approach, however, is that communication occur over APIs.

An API is the public entry point in and out of a softwre component. It can be understood as a sort of contract; it represents an expectation that if you send a message or signal in a precisely specified format to the API, you will receive a specific, expected response. For example, your online banking service can be seen as having an API. You send it your name, password, and an indication that you want your account balance, and it will return your account balance. In pseudocode, the API might look like:

GetAccountBalance(user_name, password, account_number) returns amount

The modern digital world runs on APIs; they are pervasive throughout digital interactions. They operate at different levels of the digital stack; your bank balance request might be transmitted by HTTP, which is a lower-level set of APIs for all web traffic. At scale, APIs present a challenge of management: how do you cope with thousands of APIs? Mechanisms must be created for standardizing, inventorying, reporting on status, and many other concerns.

APIs can be accessed in various ways. For example, a developer might incorporate a "library" in a program she is writing. The library (for example, one that supports trigonometric functions) has a set of APIs, that are only available if the library is compiled into the developer’s program and is only accessible if the program itself is running. Also, in this case, the API is dependent on the programming language; in general, a C++ library will not work in Java.

Increasingly, however, with the rise of the Internet, programs are continually "up" and running, and available to any other program that can communicate over the Internet, or an organization’s internal network. Programs that are continually run in this fashion, with attention to their availability and performance, are called "services". In some cases, a program or service may only be available as a visual web page. While this is still an API of a sort, many other services are available as direct API access; no web browser is required. Rather, services are accessed through protocols such as REST over HTTP. In this manner, a program written in Java can easily communicate with one written in C++. This idea is not new; many organizations started moving towards Service-Oriented Architecture (SOA) in the late 1990s and early 2000s. More recently, discussions of SOA have largely been replaced by attention to microservices.

Sam Newman, in Building Microservices, provides the following definition: "Microservices are small, autonomous services that work together" [208]. Breaking this down:

Newman endorses an heuristic that it should be possible to rewrite the microservice in two weeks. Matthew Skelton and Manuel Pais in Team Topologies [262] emphasize that optimally-sized teams have an upper bound to their "cognitive capacity"; this is also a pragmatic limit on the size of an effective microservice.

Each microservice runs independently, typically on its own virtual machines or containers.

Newman observes that microservices provide the following benefits:

A number of good practices are associated with microservices success. One notable representation of this broader set of concerns is known as the "twelve-factor app" (see https://12factor.net/). To quote [302]:

Excerpts from the Twelve-Factor App Website

It is strongly recommended that the reader review the unabridged set of guidelines at 12Factor.net.

Evidence of Notability

Cloud-native approaches are at the time of publication receiving enormous industry attention. Kubecon (the leading conference of the CNCF) attracts wide interest, and all major cloud providers and many smaller firms participate in the ecosystem. All major cloud providers and scores of smaller firms participate in the CNCF ecosystem.

Limitations

Trillions of dollars of IT investment have been made in older architectures: bare-metal computing, tightly-coupled systems, stateful applications, and every imaginable permutation of not following guidance such as the twelve-factor methodology. The Digital Practitioner should be prepared to encounter this messy real world.

Related Topics

In order to define product management, the product first needs to be defined. Previously, it was established that products are goods, services, or some combination, with some feature that provides value to some consumer. BusinessDictionary.com defines it as follows:

Product management, according to the same source, is:

Product management in the general sense often reports to the Chief Marketing Officer (CMO). It represents the fundamental strategy of the firm, in terms of its value proposition and viability. The product needs to reflect the enterprise’s strategy for creating and maintaining customers.

Product strategy for internally-facing products is usually not defined by the enterprise CMO. If it is a back-office product, then “business within a business” thinking may be appropriate. (Even the payroll system run by IT for human resources is a “product”, in this view.) In such cases, there still is a need for someone to function as an “internal CMO” to the external “customers".

With Digital Transformation, all kinds of products have increasing amounts of “IT” in them. This means that an understanding of IT, and ready access to any needed IT specialty skills, is increasingly important to the general field of product management. Product management includes R&D, which means that there is considerable uncertainty. This is of course also true of IT systems development.

Perhaps the most important aspect of product design is focusing on the user, and what they need. The concept of outcome is key. This is easier said than done. The general problem area is considered marketing, a core business school topic. Entire books have been written about the various tools and techniques for doing this, from focus groups to ethnographic analysis.

However, Marty Cagan recommends distinguishing product management from product marketing. He defines the two as follows:

The product manager is responsible for defining — in detail — the product to be built and validating that product with real customers and users. The product marketing person is responsible for telling the world about that product, including positioning, messaging and pricing, managing the product launch, providing tools for the sales channel to market and sell the product, and for leading key programs such as online marketing and influencer marketing programs [53 pp. 10-11].

Criticisms of overly marketing-driven approaches are discussed below.

In the remainder of this document, we will continually encounter three major topics:

They have an important commonality: all of them are concepts for driving results across organizations. Here are some of the key differences between process, project, and product management in the context of digital services and systems:

The above distinctions are deliberately exaggerated, and there are of course exceptions (short projects, processes that take years). However, it is in the friction between these perspectives we see some of the major problems in modern IT management. For example, an activity which may be a one-time task or a repeatable process results in some work product - perhaps an artifact (see Figure 41, “Activities Create Work Products”).

The consumer or stakeholder of that work product might be a project manager.

Project management includes concern for both the activities and the resources (people, assets, software) required to produce some deliverable (see Figure 42, “Projects Create Deliverables with Resources and Activities”).

The consumer of that deliverable might be a product manager. Product management includes concern for projects and their deliverables, and their ultimate outcomes, either in the external market or internally (see Figure 43, “Product Management may Use Projects”).

Notice that product management may directly access activities and resources. In fact, earlier-stage companies often do not formalize project management (see Figure 44, “Product Management Sometimes does not Use Projects”).

In our scenario, you are now on a tight-knit, collaborative team. You should think in terms of developing and sustaining a product. However, projects still exist, and sometimes you may find yourself on a team that is funded and operated on that basis. You also will encounter the concept of “process” even on a single team; more on that in Section 6.2.2, “Work Management”. We will go further into projects and process management in Context III.

Amazon (the online retailer) is an important influence in the modern trend towards product-centric IT management. First, all Amazon teams were told to assume that the functionality being built might at some point be offered to external customers [3].

Second, a widely reported practice at Amazon.com is the limitation of product teams to between five and eight people, the number that can be fed by “two pizzas” (depending on how hungry they are) [110]. It has long been recognized in software and IT management that larger teams do not necessarily result in higher productivity. The best known statement of this is "Brooks' Law” from The Mythical Man-Month, that “adding people to a late project will make it later” [42].

The reasons for “Brooks' Law” have been studied and analyzed (see, for example, [183, 59]) but in general, it is due to the increased communication overhead of expanded teams. Product design work (of which software development is one form) is creative and highly dependent on tacit knowledge, interpersonal interactions, organizational culture, and other “soft” factors. Products, especially those with a significant IT component, can be understood as socio-technical systems, often complex. This means that small changes to their components or interactions can have major effects on their overall behavior and value.

This, in turn, means that newcomers to a product development organization can have a profound impact on the product. Getting them “up to speed” with the culture, mental models, and tacit assumptions of the existing team can be challenging and rarely is simple. And the bigger the team, the bigger the problem. The net result of these two practices at Amazon (and now General Electric and many other companies) is the creation of multiple nimble services that are decoupled from each other, constructed and supported by teams appropriately sized for optimal high-value interactions.

Finally, Amazon founder Jeff Bezos mandated that all software development should be service-oriented. That means that some form of standard API was required for all applications to communicate with each other. Amazon’s practices are a clear expression of cloud-native development.

Evidence of Notability

Product management has a dedicated professional association, the Product Development and Management Association (www.pdma.org.) Notable authors include Steve Blank, Marty Cagan, and Jeff Gothelf. The topic as a whole is closely related to the general topic of R&D. There are many meetups, conferences, and other events held under various banners such as Agile development.

Limitations

Product management tends to assume the existence of a market, and customers whose reaction is unpredictable. This is not always the case in digital systems. Sometimes, digital artifacts and capabilities have greater constraints, and must follow established specifications.

Related Topics

In Section 6.1.3, “Application Delivery”, we needed to consider the means for describing system intent. Even as a bare-bones startup, some formalization of this starts to emerge, at the very least in the form of test-driven development (see Figure 45, “Product Discovery Tacit”).

But, the assumption in our emergence model is that more formalized product management emerges with the formation of a team. As a team, we now need to expand “upstream” of the core delivery pipeline, so that we can collaborate and discover more effectively. Notice the grey box in Figure 46, “Product Discovery Explicit”.

The most important foundation for your newly formalized product discovery capability is that it must be empirical and hypothesis-driven. Too often, product strategy is based on the Highest Paid Person’s Opinion (HiPPO).

The problem with relying on “gut feeling” or personal opinions is that people — regardless of experience or seniority — perform poorly in assessing the likely outcome of their product ideas. A well-known case is the initial rejection of Amazon shopping cart suggestions [179]. Some well-known research on this topic was conducted by Microsoft’s Ronny Kohavi. In this research, Kohavi and team determined that “only about 1/3 of ideas improve the metrics they were designed to improve” [170]. As background, the same report cites that:

It is, therefore, critical to establish a strong practice of data-driven experimentation when forming a product team and avoid any cultural acceptance of “gut feel” or deferring to HiPPOs. This can be a difficult transition for the company founder, who has until now served as the de facto product manager.

A useful framework, similar to Lean Startup is proposed by Spotify™, in the “DIBB” model:

Data leads to insight, which leads to a hypothesis that can be tested (i.e., “bet” on — testing hypotheses is not free). We discuss issues of prioritization further in Section 6.2.2, “Work Management”, in the section on cost of delay.

Don Reinertsen (whom we will read more about in Competency Area 5) emphasizes that such experimentation is inherently variable. We can’t develop experiments with any sort of expectation that they will always succeed. We might run 50 experiments, and only have two succeed. But if the cost of each experiment is $10,000, and the two that succeeded earned us $1 million each, we gained:

Not a bad return on investment! (see [230], Section 6.2.1, “Product Management”, for a detailed, mathematical discussion, based on options and information theory). Roman Pichler, in Agile Product Management with Scrum, describes “old-school” versus “new-school” product management as in Table 4, “Old School versus New School Product Management” (summarized from [219], p.xii).

There are a wide variety of techniques and even “schools” of product discovery and design. This section considers a few representatives. At the team level, such techniques must be further formalized. The techniques are not mutually-exclusive; they may be complementary. User Story Mapping was previously mentioned. In product discovery terms, User Story Mapping is a form of persona analysis. But that is only one of many techniques. Roman Pichler mentions “Vision Box and Trade Journal Review” and the “Kano Model” [219 p. 39]. Here, let’s discuss:

Design thinking is a recent trend with various definitions, but in general, combines a design sensibility with problem solving at significant scale. It usually is understood to include a significant component of systems thinking.

Design thinking is the logical evolution of disciplines such as user interface design when such designs encounter constraints and issues beyond their usual span of concern. Although it has been influential on Lean UX and related works, it is not an explicitly digital discipline.

There are many design failures in digital product delivery. What is often overlooked is that the entire customer experience of the product is a form of design.

Consider for example Apple. Their products are admired worldwide and cited as examples of “good design”. Often, however, this is only understood in terms of the physical product; for example, an iPhone® or a MacBook Air®. But there is more to the experience. Suppose you have technical difficulties with your iPhone, or you just want to get more value out of it. Apple created its popular Genius Bar support service, where you can get support and instruction in using the technology.

Notice that the product you are using is no longer just the phone or computer. It is the combination of the device PLUS your support experience. This is essential to understanding the modern practices of design thinking and Lean UX.

The following table condensed from Lotta Hassi and Miko Laakso [125] provides a useful overview of design thinking:

The concept of hypothesis testing is key to product discovery and design. The power of scalable cloud architectures and fast continuous delivery pipelines has made it possible to test product hypotheses against real-world customers at scale and in real time. Companies like Netflix and Facebook have pioneered techniques like "canary deployments” and "A/B testing”.

In these approaches, two different features are tried out simultaneously, and the business results are measured. For example, are customers more likely to click on a green button or a yellow one? Testing such questions in the era of packaged software would have required lab-based usability engineering approaches, which risked being invalid because of their small sample size. Testing against larger numbers is possible, now that software is increasingly delivered as a service.

At a lower level than the holistic concerns of design thinking, we have practices such as usability engineering. These take many forms. There are many systematic and well-researched approaches to:

and related topics. All such approaches, however, should be used in the overall Lean Startup/Lean UX framework of hypothesis generation and testing. If we subscribe to design thinking and take a whole-systems view, designing for ease of operations is also part of the design process. We will discuss this further in Section 6.2.3, “Operations Management”. Developing documentation of the product’s characteristics, from the perspective of those who will run it on a day-to-day basis, is also an aspect of product delivery.

Some of the most contentious discussions related to IT management and Agile come at the intersection of software and systems engineering, especially when large investments are at stake. We call this the “discovery versus design” problem.

Frequent criticisms of Lean Startup and its related digital practices are:

There are two very different product development worlds. Some product development ("cogs") is constrained by the overall system it takes place within. Other product development ("flowers") has more freedom to grow in different directions — to “discover” the customer.

The cog represents the world of classic systems engineering — a larger objective frames the effort, and the component occupies a certain defined place within it. And yet, it may still be challenging to design and build the component, which can be understood as a product in and of itself. Fast feedback is still required for the design and development process, even when the product is only a small component with a very specific set of requirements.

The flower represents the market-facing digital product that may “pivot”, grow, and adapt according to conditions. It also is constrained, by available space and energy, but within certain boundaries has greater adaptability.

Neither is better than the other, but they do require different approaches. In general, we are coming from a world that viewed digital systems strictly as cogs. Subsequently, we are moving towards a world in which digital systems are more flexible, dynamic, and adaptable.

When digital components have very well-understood requirements, usually we purchase them from specialist providers (increasingly “as a service”). This results in increasing attention to the “flowers” of digital product design since acquiring the “cogs” is relatively straightforward (more on this in the section on sourcing).

Evidence of Notability

Product discovery techniques are widely discussed in the product management community and are frequent topics of presentation at notable industry events such as Agile Alliance conferences.

Limitations

In organizations that are primarily purchasing software and not building it, product discovery techniques may be less applicable. However, internal "products" understood as business capabilities may still benefit from a design/discovery approach, even if they are based on (for example) a SaaS offering.

Related Topics

Team collaboration is one of the key values of Agile. The Agile Alliance states that:

A “team” in the Agile sense is a small group of people, assigned to the same project or effort, nearly all of them on a full-time basis.

Teams are multi-skilled, share accountability, and individuals on the team may play multiple roles [9]:

Face-to-face interactions, usually enabled by giving the team its own space, are essential for collaboration. While there are various approaches to Agile, all concur that tight-knit, collaborative teams deliver the highest value outcomes. However, collaboration does not happen just because people are fed pizzas and work in a room together. Google has established that the most significant predictor of team performance is a sense of psychological safety. Research by Anita Woolley and colleagues suggests that three factors driving team performance are [309]:

Other research shows that diverse teams and organizations are more innovative and deliver better results; such teams may tend to focus more on facts (as opposed to groupthink) [235]. Certainly, a sense of psychological safety is critical to the success of diverse teams, who may come from different cultures and backgrounds that don’t inherently trust each other.

Two current schools of thought with much to say about collaboration are Lean UX and Scrum.

Lean UX is a term coined by author and consultant Jeff Gothelf [114], which draws on three major influences:

We briefly discussed Lean Startup in Section 6.1.1, “Digital Fundamentals”, and the history and motivations for Agile software development in Section 6.1.3, “Application Delivery”. We will look in more depth at product discovery techniques, and design and design thinking subsequently. However, Lean UX has much to say about forming the product team, suggesting (among others) the following principles for forming and sustaining teams:

(Other Lean UX principles such as small batch sizes and visualizing work will be discussed elsewhere; there is significant overlap between Lean UX and other schools of thought covered in this document).

Lean UX is an influential work among digital firms and summarizes modern development practices well, especially for small, team-based organizations with minimal external dependencies. It is a broad and conceptual, principles-based framework open for interpretation in multiple ways. We continue with more “prescriptive” methods and techniques, such as Scrum.

One of the first prescriptive Agile methodologies you are likely to encounter as a practitioner is Scrum. There are many books, classes, and websites where you can learn more about this framework; [260] is a good brief introduction, and [243] is well suited for more in-depth study.

Scrum is appropriate to this Competency Area, as it is product-focused. It calls for the roles of:

and avoids further elaboration of roles.

The Scrum product owner is responsible for holding the product vision and seeing that the team executes the highest value work. As such, the potential features of the product are maintained in a “backlog” that can be re-prioritized as necessary (rather than a large, fixed-scope project). The product owner also defines acceptance criteria for the backlog items. The Scrum master, on the other hand, acts as a team coach, “guiding the team to ever-higher levels of cohesiveness, self-organization, and performance” [260]. To quote Roman Pichler:

The product owner and Scrum master roles complement each other: The product owner is primarily responsible for the “what" - creating the right product. The Scrum master is primarily responsible for the “how" - using Scrum the right way [219 p. 9].

Scrum uses specific practices and artifacts such as sprints, standups, reviews, the above-mentioned concept of backlog, burndown charts, and so forth. We will discuss some of these further in Section 6.2.2, “Work Management” and Section 6.3.1, “Coordination and Process” along with Kanban, another popular approach for executing work.

In Scrum, there are three roles:

There are seven activities:

There are a number of artifacts:

Scrum is well grounded in various theories (process control, human factors), although Scrum team members do not need to understand theory to succeed with it. Like Lean UX, Scrum emphasizes high-bandwidth collaboration, dedicated multi-skilled teams, a product focus, and so forth.

The concept of having an empowered product owner readily available to the team is attractive, especially for Digital Practitioners who may have worked on teams where the direction was unclear. Roman Pichler identifies a number of common mistakes, however, that diminish the value of this approach [219 pp. 17-20]:

Scrum and Shu-ha-ri

In the Japanese martial art of aikido, there is the concept of shu-ha-ri, a form of learning progression.

Scrum at its most prescriptive can be seen as a shu-level practice; it gives detailed guidance that has been shown to work.

See [104] and [65 pp. 17-18]

Marty Cagan suggests that the product team has three primary concerns, requiring three critical roles [53]:

Jeff Patton represents these concepts as a Venn diagram (see Figure 48, “The Three Views of the Product Team”, similar to [217]).

Finally, a word on the product manager. Scrum is prescriptive around the product owner role, but does not identify a role for product manager. This can lead to two people performing product management: a marketing-aligned “manager” responsible for high-level requirements, with the Scrum “product owner” attempting to translate them for the team. Marty Cagan warns against this approach, recommending instead that the product manager and owner be the same person, separate from marketing [53 pp. 7-8].

In a subsequent section, we will consider the challenge of product discovery — at a product level, what practices do we follow to generate the creative insights that will result in customer value?

Evidence of Notability

Product team structure and practices are widely debated and discussed in the industry, particularly in the Agile community. Notable conferences include Agile Alliance and Global Scrum Gathering. Many books are published on Scrum and related product team organization topics; e.g., [243, 114, 273],

Limitations

Product team structure and practices are only relevant when there is a concept of product. Some digital work may be framed as projects, where structures are temporary and objectives are more constrained.

Related Topics

Creating effective plans in complex situations is challenging. Planning a new product is one of the most challenging endeavors, one in which failure is common. The historically failed approach (call it the "planning fallacy”) is to develop overly detailed (sometimes called “rigorous”) plans and then assume that achieving them is simply a matter of “correct execution” (see Figure 49, “Planning Fallacy”).

Contrast the planning fallacy with Lean Startup approaches, which emphasize ongoing confirmation of product direction through experimentation. In complex efforts, ongoing validation of assumptions and direction is essential, which is why overly plan-driven approaches are falling out of favor. However, some understanding of timeframes and mapping goals against the calendar is still important. Exactly how much effort to devote to such forecasting remains a controversial topic with DPM professionals, one we will return to throughout this document.

Minimally, a high-level product roadmap is usually called for: without at least this, it may be difficult to secure the needed investment to start product work. Roman Pichler recommends the product roadmap contains:

More detailed understanding is left to the product backlog, which is subject to ongoing “grooming”; that is, re-evaluation in light of feedback.

The product discovery and roadmapping activity ultimately generates a more detailed view or list of the work to be done. As we previously mentioned, in Scrum and other Agile methods this is often termed a backlog. Both Mike Cohn and Roman Pichler use the DEEP acronym to describe backlog qualities [68 p. 243, 219 p. 48]:

The backlog should receive ongoing “grooming” to support these qualities, which means several things:

When “detailed appropriately”, items in the backlog are not all the same scale. Scrum and Agile thinkers generally agree on the core concept of "story”, but stories vary in size (see Figure 50, “Backlog Granularity and Priority”, similar to [219]), with the largest stories often termed “epics”. The backlog is ordered in terms of priority (what will be done next) but, critically, it is also understood that the lower-priority items, in general, can be larger-grained. In other words, if we visualize the backlog as a stack, with the highest priority on the top, the size of the stories increases as we go down. (Don Reinertsen terms this progressive specification; see [229 pp. 176-177] for a detailed discussion.)

Estimating user stories is a standard practice in Scrum and Agile methods more generally. Agile approaches are wary of false precision and accept the fact that estimation is an uncertain practice at best. However, without some overall estimate or roadmap for when a product might be ready for use, it is unlikely that the investment will be made to create it. It is difficult to establish the economic value of developing a product feature at a particular time if you have no idea of the cost and/or effort involved to bring it to market.

At a more detailed level, it is common practice for product teams to estimate detailed stories using “points”. Mike Cohn emphasizes: “Estimate size, derive duration” ([67], p.xxvii). Points are a relative form of estimation, valid within the boundary of one team. Story point estimating strives to avoid false precision, often restricting the team’s estimate of the effort to a modified Fibonacci sequence, or even T-shirt or dog sizes [67 p. 37] as shown in Table 6, “Agile Estimating Scales” (similar to [67 p. 37]).

Mike Cohn emphasizes that estimates are best done by the teams performing the work [67 p. 51]. We will discuss the mechanics of maintaining backlogs in Section 6.2.2, “Work Management”.

Backlogs require prioritization. In order to prioritize, we must have some kind of common understanding of what we are prioritizing for. Mike Cohn, in Agile Estimating and Planning, proposes that there are four major factors in understanding product value:

In Section 6.2.2, “Work Management” we will discuss additional tools for managing and prioritizing work, and we will return to the topic of estimation in Section 6.3.2, “Investment and Portfolio”.

Evidence of Notability

Product roadmaps are one of the first steps towards investment management and strategic planning. There are robust debates around estimation and planning in the Agile community.

Limitations

Roadmaps are uncertain at best. They are prone to false precision and the planning fallacy.

Related Topics

To understand Kanban we should start with Lean. Lean is a term invented by American researchers who investigated Japanese industrial practices and their success in the 20th century. After the end of World War II, no-one expected the Japanese economy to recover the way it did. The recovery is credited to practices developed and promoted by Taiichi Ohno and Shigeo Shingo at Toyota [212]. These practices included:

Credit for Lean is also sometimes given to US thinkers such as W. Edwards Deming, Peter Juran, and the theorists behind the Training Within Industry methodology, each of whom played influential roles in shaping the industrial practices of post-war Japan.

Kanban is a term originating from Lean and the Toyota Production System. Originally, it signified a “pull” technique in which materials would only be transferred to a given workstation on a definite signal that the workstation required the materials. This was in contrast to “push” approaches where work was allowed to accumulate on the shop floor, on the (now discredited) idea that it was more “efficient” to operate workstations at maximum capacity.

Factories operating on a “push” model found themselves with massive amounts of inventory (work-in-process) in their facilities. This tied up operating capital and resulted in long delays in shipment. Japanese companies did not have the luxury of large amounts of operating capital, so they started experimenting with "single-piece flow”. This led to a number of related innovations, such as the ability to re-configure manufacturing machinery much more quickly than US factories were capable of.

David J. Anderson was a product manager at Microsoft who was seeking a more effective approach to managing software development. In consultation with Don Reinertsen (introduced below) he applied the original concept of Kanban to his software development activities [20].

Scrum (covered in the previous chapter) is based on a rhythm with its scheduled sprints; for example, every two weeks (this is called cadence). In contrast, Kanban is a continuous process with no specified rhythm. Work is “pulled” from the backlog into active attention as resources are freed from previous work. This is perhaps the most important aspect of Kanban — the idea that work is not accepted until there is capacity to perform it.

You may have a white board covered with sticky notes, but if they are stacked on top of each other with no concern for worker availability, you are not doing Kanban. You are accepting too much work-in-process, and you are likely to encounter a “high-queue state” in which work becomes slower and slower to get done. (More on queues below.)

Eliyahu Moshe Goldratt was an Israeli physicist and management consultant, best known for his pioneering work in management theory, including The Goal, which is a best-selling business novel frequently assigned in MBA programs. It and Goldratt’s other novels have had a tremendous effect on industrial theory and, now, digital management. One of the best known stories in The Goal centers around a Boy Scout march. Alex, the protagonist struggling to save his manufacturing plant, takes a troop of Scouts on a ten-mile hike. The troop has hikers of various speeds, yet the goal is to arrive simultaneously. As Alex tries to keep the Scouts together, he discovers that the slowest, most overweight scout (Herbie) also has packed an unusually heavy backpack. The contents of Herbie’s pack are redistributed, speeding up both Herbie and the troop.

This story summarizes the Goldratt approach: finding the “constraint” to production (his work as a whole is called the Theory of Constraints). In Goldratt’s view, a system is only as productive as its constraint. At Alex’s factory, it is found that the “constraint” to the overall productivity issues is the newest computer-controlled machine tool — one that could (in theory) perform the work of several older models but was now jeopardizing the entire plant’s survival. The story in this novelization draws important parallels with actual Lean case studies on the often-negative impact of such capital-intensive approaches to production.

The above quote reflects one of the most critical foundations of team collaboration: a common ground, a base of “knowledge, beliefs, and assumptions” enabling collaboration and coordination. Common ground is an essential quality of successful teamwork, and we will revisit it throughout the book. There are many ways in which common ground is important, and we will discuss some of the deeper aspects in terms of information in Section 6.4.2, “Information Management”. Whether you choose Scrum, Kanban, or choose not to label your work management at all, the important thing is that you are creating a shared mental model of the work: its envisioned form and content, and your progress towards it.

Below, we will discuss:

Visualization is a good place to introduce the idea of common ground.

Why are shared visual representations important? Depending on how you measure, between 40% to as much as 80% of the human cortex is devoted to visual processing. Visual processing dominates mental activity, consuming more neurons than the other four senses combined [252]. Visual representations are powerful communication mechanisms, well suited to our cognitive abilities.

This idea of common ground, a shared visual reference point, informing the mental model of the team, is an essential foundation for coordinating activity. This is why card walls or Kanban boards located in the same room are so prevalent. They communicate and sustain the shared mental model of a human team. A shared card wall, with its two dimensions and tasks on cards or sticky notes, is more informative than a simple to-do list (e.g., in a spreadsheet). The cards occupy two-dimensional space and are moved over time to signify activity, both powerful cues to the human visual processing system.

Similarly, monitoring tools for systems operation make use of various visual clues. Large monitors may be displayed prominently on walls so that everyone can understand operational status. Human visual orientation is also why Enterprise Architecture persists. People will always draw to communicate. (See also visualization and Enterprise Architecture.)

Card walls and publicly displayed monitors are both examples of information radiators. The information radiator concept derives from the Japanese concept of Andon, important in Lean thinking.

The Andon cord (not to be confused with Andon in the general sense) is another well-known concept in Lean manufacturing. It originated with Toyota, where line workers were empowered to stop the production line if any defective materials or assemblies were encountered. Instead of attempting to work with the defective input, the entire line would shut down, and all concerned would establish what had happened and how to prevent it. The concept of Andon cord concisely summarizes the Lean philosophy of employee responsibility for quality at all levels [212]. Where Andon is a general term for information radiator, the Andon cord implies a dramatic response to the problems of flow — all progress is stopped, everywhere along the line, and the entire resources of the production line are marshaled to collaboratively solve the issue so that it does not happen again. As Toyota thought leader Taiichi Ohno states:

Andon and information radiators provide an important stimulus for product teams, informing priorities and prompting responses. They do not prescribe what is to be done; they simply indicate an operational status that may require attention.

As work flows through the system performing it, understanding its status is key to managing it. One of the most important mechanisms for doing this is to define what is meant by “done simply”. The Agile Alliance states:

“The team agrees on, and displays prominently somewhere in the team room, a list of criteria which must be met before a product increment, often a user story, is considered “done” [9]. Failure to meet these criteria at the end of a sprint normally implies that the work should not be counted toward that sprint’s velocity.” There are various patterns for defining “done”; for example, Thoughtworks recommends that the business analyst and developer both must agree that some task is complete (it is not up to just one person). Other companies may require peer code reviews [206]. The important point is that the team must agree on the criteria.

This idea of defining “done” can be extended by the team to other concepts such as “blocked”. The important thing is that this is all part of the team’s shared mental model, and is best defined by the team and its customers. (However, governance and consistency concerns may arise if teams are too diverse in such definitions.)

At some point, your team will be faced with the problems of time and/or space shifting. People will be on different schedules, or in different locations, or both. There are two things we know about such working relationships. First, they lead to sub-optimal team communications and performance. Second, they are inevitable.

The need for time and space shifting is one of the major drivers for more formalized IT systems. It is difficult to effectively use a physical Kanban board if people aren’t in the office. The outcome of the daily standup needs to be captured for the benefit of those who could not be there.

However, acceptance of time and space shifting may lead to more of it, even when it is not absolutely required. Constant pressure and questioning are recommended, given the superior bandwidth of face-to-face communication in the context of team collaboration.

But not all work requires the same degree of collaboration. While we are still not ready for full-scale process management, at this point in our evolution, we likely will encounter increasing needs to track customer or user service interactions, which can become quite numerous even for small, single-team organizations. Such work is often more individualized and routine, not requiring the full bandwidth of team collaboration. We will discuss this further with the topic of the help or service desk, later in this Competency Area.

Even at this stage of our evolution, with just one co-located collaborative team, it is important to consider work-in-process and how to limit it. One topic we will emphasize throughout the rest of this document is queuing.

A queue, intuitively, is a collection of tasks to be done, being serviced by some worker or resource in some sequence; for example:

Queuing theory is an important branch of mathematics used extensively in computing, operations research, networking, and other fields. It is a topic getting much attention of late in the Agile and related movements, especially as it relates to digital product team productivity.

The amount of time that any given work item spends in the queue is proportional to how busy the servicing resource is. The simple formula, known as Little’s Law, is:

In other words, if you divide the percentage of busy time for the resource by its idle time, you see the average wait time. So, if a resource is busy 40% of the days, but idle 60% of the days, the average time you wait for the resource is:

Conversely, if a resource is busy 95% of the time, the average time you will wait is:

If you use a graphing calculator, you see the results in Figure 54, “Time in Queue Increases Exponentially with Load”.

Notice how the wait time approaches infinity as the queue utilization approaches 100%. And yet, full utilization of resources is often sought by managers in the name of “efficiency”. These basic principles are discussed by Gene Kim et al. in The Phoenix Project [165], Chapter 23, and more rigorously by Don Reinertsen in The Principles of Product Development Flow [230], Chapter 3. A further complication is when work must pass through multiple queues; wait times for work easily expand to weeks or months. Such scenarios are not hypothetical, they are often seen in the real world and are a fundamental cause of IT organizations getting a bad name for being slow and unresponsive. Fortunately, Digital Practitioners are gaining insight into these dynamics and matters are improving across the industry.

Understanding queuing behavior is critical to productivity. Reinertsen suggests that poorly managed queues contribute to:

These issues were understood by the pioneers of Lean manufacturing, an important movement throughout the 20th century. One of its central principles is to limit work-in-process. Work-in-process is obvious on a shop floor because physical raw materials (inventory) are quite visible.

Don Reinertsen developed the insight that product design and development had an invisible inventory of “work-in-process” that he called design-in-process. Just as managing physical work-in-process on the factory floor is key to a factory’s success, so correctly understanding and managing design-in-process is essential to all kinds of R&D organizations — including digital product development; e.g., building software(!). In fact, because digital systems are largely invisible even when finished, understanding their work-in-process is even more challenging.

It is easy and tempting for a product development team to accumulate excessive amounts of work-in-process. And, to some degree, having a rich backlog of ideas is an asset. But, just as some inventory (e.g., groceries) is perishable, so are design ideas. They have a limited time in which they might be relevant to a customer or a market. Therefore, accumulating too many of them at any point in time can be wasteful.

What does this have to do with queuing? Design-in-progress is one form of queue seen in the digital organization. Other forms include unplanned work (incidents and defects), implementation work, and many other concepts we will discuss in this chapter.

Regardless of whether it is a “requirement”, a “user story”, an “epic”, “defect", “issue”, or “service request”, you should remember it is all just work. It needs to be logged, prioritized, assigned, and tracked to completion. Queues are the fundamental concept for doing this, and it is critical that digital management specialists understand this.

Multi-tasking (in this context) is when a human attempts to work on diverse activities simultaneously; for example, developing code for a new application while also handling support calls. There is broad agreement that multi-tasking destroys productivity, and even mental health [57]. Therefore, minimize multi-tasking. Multi-tasking in part emerges as a natural response when one activity becomes blocked (e.g., due to needing another team’s contribution). Approaches that enable teams to work without depending on outside resources are less likely to promote multi-tasking. Queuing and work-in-process therefore become even more critical topics for management concern as activities scale up.

So, do you choose Scrum, Kanban, both, or neither? We can see in comparing Scrum and Kanban that their areas of focus are somewhat different:

Ultimately, instead of talking too much about “Scrum” or “Kanban”, the student is encouraged to look more deeply into their fundamental differences. We will return to this topic in the section on Lean Product Development.

There are deeper philosophical and cultural qualities to Kanban beyond workflow and queuing. Anderson and his colleagues continue to evolve Kanban into a more ambitious framework. Mike Burrows [48] identifies the following key principles:

Evidence of Notability

Work and task management is a fundamental problem in human organizations. It is the foundation of workflow and BPM. Lean generally is one of the most significant currents of thought in modern management [212, 307, 308, 239, 238]. Kanban is widely discussed at Agile and DevOps conferences. Using a lightweight, generalized task tracking tool, often physical, is seen in digital organizations worldwide.

Limitations

Kanban’s generalized workflow does not scale to complex processes with many steps and decision points. This will be covered further in the section on workflow management. Not all activities reduce well to a list of tasks. Some are more intangible and outcome-focused. As Bjarne Stroustrup, the inventor of C+, stated: "The idea of software development as an assembly line manned by semi-skilled interchangeable workers is fundamentally flawed and wasteful" +[271]. It is critical to distinguish Lean as applied to digital systems development (as a form of applied R&D) versus Lean in its manufacturing aspects. Reinertsen’s contributions ([229, 230]) are unique and notable in this regard and are discussed in the next section.

Related Topics

One of the Lean Product Development practices is "Go & See". Instead of relying on secondary information such as market studies or marketing reports (as also discussed in Product Discovery), product team members are encouraged to experience first hand customers' needs, problems, and emotions. For example, one of the Toyota Chief Engineers rented a car in Canada and drove for several months in the winter time to understand the unique needs of the Canadian driver. Design thinking possibly combined with anthropological approaches help understand value from a customer’s perspective.

Toyota’s Chief Engineers are not program managers who focus on controlling and reporting development activities. They are leaders who create and communicate a compelling and feasible vision. They define a clear and logical architecture for the product and value stream. Their T-shaped profile gives them enough understanding of the various disciplines at play so they can help solve cross-disciplinary problems. Chief Engineers are accountable for the economic success of their products. Last but not the least, their leadership skills help them inspire excellent engineers. The Chief Engineer does not have formal authority on the teams that develop the product. Team members report to functional department heads. New Agile at scale organizational models such as the Spotify model are similar with teams members reporting to chapters or guilds and not squad leaders. The Product Owner plays a role comparable to the Chief Engineer’s one at a smaller scale.

Poor decisions made early in the development process have negative consequences that increase exponentially over time because reversing them later in the lifecycle becomes more and more difficult. Amazon CEO Jeff Bezos classifies decisions into type 1 and type 2 categories [35]. Type 1 decisions are not reversible, and you have to be very careful making them. Type 2 decisions are like walking through a door — if you don’t like the decision, you can always go back. Because type 1 decisions are difficult to reverse, alternatives should be thoroughly explored before the final decision is made. This tendency to front load the development process could slow the development process.

Set-Based Concurrent Engineering or SBCE makes front loading compatible with short product development lead times. Instead of focusing on the rapid completion of individual component designs in isolation, SBCE looks at how individual designs will interact within a system before the design is complete. The focus is on system integration before individual design completion. The concurrent nature of the design process contributes to shortening product development lead time while front loading combined with the integration focus helps minimize bad design decisions which would at the end slow the development process and increase "non-quality". A good metaphor for SBCE is doodle.com which offers a much better way of scheduling a meeting compared to the old iterative "point-based" way of finding a time that works for all.

We introduced the concept of Andon previously. Lean Product Development has a similar practice, Obeya. The Obeya process begins with the entire team posting in a physical room visual artifacts representing the product’s components. Product component owners are responsible for posting status information such as timing, issues, key design questions, etc. Because the information is shared in a transparent manner, useful conversations are elicited. When problems are identified they are analyzed using problem solving approaches such as PDCA and A3. Collocation greatly intensifies communication and helps solve problems earlier. One of the advantages of the Obeya process is that it does not force the enterprise to change its departmental organization or to co-locate hundreds of engineers. When an Obeya room cannot be set up at the same place, virtual ones can be created using specialized collaborative software. The Obeya process proved to be a critical element of the Toyota product development system helping radically reduce lead time.

Traditional task-based project management is based on tasks completed and not on technical results. Because project managers do not understand the reality that hides behind the Gantt chart, problems can remain hidden for a long time. In contrast, the Chief Engineer defines integrating events at fixed dates. Required results are communicated to responsible engineers who are free to plan and organize as needed to meet these dates and deliver expected results. Top-down detailed planning and control is replaced by top-down objectives, the detailed planning and execution being delegated to autonomous teams. The responsibility style helps develop a learning development organization. Bureaucracy is eliminated and the creation of useful knowledge encouraged.

Unevenness (Mura) and overburden (Muri) are root causes of waste (Muda) in both production and development value streams. In the context of LPPD work should be released in the organization on a regular cadence in order to level the workload. Integrating events gives freedom to developers to plan their work to meet those events. In this way development work is pulled (as covered in the Kanban discussion) rather than scheduled. Similarly information is pulled by developers based on what they need to know rather than being pushed according to some centrally planned schedule. Don Reinertsen, the author of The Principles of Product Development Flow, proposes a method to maximize the economic benefit of a portfolio of projects. The key idea is that the sequencing of projects should consider both the cost of delay of each project and the amount of time that the project will block scarce development resources. This approach is known as a Weighted Shortest Job First (WSJF) queueing discipline. It has influenced the Agile community; SAFe specifies WSJF to prioritize backlogs.

In IT, simply developing software for a new problem (or even new software for an old problem) is an R&D problem, not a production line problem. It is iterative, uncertain, and risky, just like other forms of product development. That does not mean it is completely unmanageable, or that its creation is a mysterious, artistic process. It is just a more variable process with a higher chance of failure, and with a need to incorporate feedback quickly to reduce the risk of open-loop control failure. These ideas are well known to the Agile community and its authors. However, there is one thought leader who stands out in this field: an ex-Naval officer and nuclear engineer named Donald Reinertsen who was introduced in our previous discussions on beneficial variability in product discovery and queuing.

Reinertsen’s work dates back to 1991, and (originally as a co-author with Preston G. Smith) presaged important principles of the Agile movement [263], from the general perspective of product development. Reinertsen’s influence is well documented and notable. He was partnering with David Anderson when Anderson created the “software Kanban” approach. He wrote the introduction to Leffingwell’s Agile Software Requirements, the initial statement of SAFe. His influence is pervasive in the Agile community. His work is deep and based on fundamental mathematical principles such as queueing theory. His work can be understood as a series of interdependent principles:

These can be summarized as in Figure 55, “Lean Product Development Hierarchy of Concerns”.

If a company wishes to innovate faster than competitors, it requires fast feedback on its experiments (whether traditionally understood, laboratory-based experiments, or market-facing validation as in Lean Startup. In order to achieve fast feedback, work-in-process must be reduced in the system, otherwise high-queue states will slow feedback down.

But how do we reduce work-in-process? We have to prioritize. Do we rely on the HiPPO, or do we try something more rational? This brings us to the critical concept of cost of delay.

Don Reinertsen is well known for advocating the concept of “cost of delay” in understanding product economics. The term is intuitive; it represents the loss experienced by delaying the delivery of some value. For example, if a delayed product misses a key trade show, and therefore its opportunity for a competitive release, the cost of delay might be the entire addressable market. Understanding cost of delay is part of a broader economic emphasis that Reinertsen brings to the general question of product development. He suggests that product developers, in general, do not understand the fundamental economics of their decisions regarding resources and work-in-process.

In order to understand the cost of delay, it is first necessary to think in terms of a market-facing product (such as a smartphone application). Any market-facing product can be represented in terms of its lifecycle revenues and profits (see Table 7, “Product Lifecycle Economics by Year”, Figure 56, “Product Lifecycle Economics, Charted”).

The numbers above represent a product lifecycle, from R&D through production to retirement. The first year is all cost, as the product is being developed, and net profits are negative. In year 2, a small net profit is shown, but cumulative profit is still negative, as it remains in year 3. Only into year 3 does the product break even, ultimately achieving lifecycle net earnings of 175. But what if the product’s introduction into the market is delayed? The consequences can be severe.

Simply delaying delivery by a year, all things being equal in our example, will reduce lifecycle profits by 30% (see Table 8, “Product Lifecycle, Simple Delay”, Figure 57, “Product Lifecycle, Simple Delay, Charted”).

But all things are not equal. What if, in delaying the product for a year, we allow a competitor to gain a superior market position? That could depress our sales and increase our per-unit costs — both bad (see Table 9, “Product Lifecycle, Aggravated Delay”, Figure 58, “Product Lifecycle, Aggravated Delay, Charted”).

The advanced cost of delayed analysis argues that different product lifecycles have different characteristics. Josh Arnold of Black Swan Farming has visualized these as a set of profiles [22]. See Figure 59, “Simple Cost of Delay” (similar to [22]) for the simple delay profile.

In this delay curve, while profits and revenues are lost due to late entry, it is assumed that the product will still enjoy its expected market share. We can think of this as the “iPhone versus Android” profile, as Android was later but still achieved market parity. The aggravated cost of delay profile, however, looks like Figure 60, “Aggravated Cost of Delay” (similar to [22]).

In this version, the failure to enter the market in a timely way results in long-term loss of market share. We can think of this as the “Amazon Kindle™ versus Barnes & Noble Nook” profile, as the Nook has not achieved parity, and does not appear likely to. There are other delay curves imaginable, such as delay curves for tightly time-limited products (e.g., such as found in the fashion industry) or cost of delay that is only incurred after a specific date (such as in complying with a regulation).

Reinertsen observes that product managers may think that they intuitively understand cost of delay, but when he asks them to estimate the aggregate cost of (for example) delaying their product’s delivery by a given period of time, the estimates provided by product team participants in a position to delay delivery may vary by up to 50:1. This is powerful evidence that a more quantitative approach is essential, as opposed to relying on “gut feel” or the HiPPO.

Finally, Josh Arnold notes that cost of delay is much easier to assess on small batches of work. Large projects tend to attract many ideas for features, some of which have stronger economic justifications than others. When all these features are lumped together, it makes understanding the cost of delay a challenging process, because it then becomes an average across the various features. But since features, ideally, can be worked on individually, understanding the cost of delay at that level helps with the prioritization of the work.

The combination of product roadmapping, a high-quality DEEP backlog, and cost of delay is a solid foundation for digital product development. It is essential to have an economic basis for making the prioritization decision. Clarifying the economic basis is a critical function of the product roadmap. Through estimation of story points, we can understand the team’s velocity. Estimating velocity is key to planning, which we will discuss further in Section 6.3.2, “Investment and Portfolio”. Through understanding the economics of product availability to the market or internal users, the cost of delay can drive backlog prioritization.

Evidence of Notability

Lean influences on software development and the management of digital systems are the subject of conference talks, books, and articles, and much other evidence demonstrating an engaged community of interest. Notable works include [165, 20, 221, 27, 230].

Limitations

Lean has broad applicability but the nature of the digital work must be understood carefully. Classic Lean applies well to less-variable operational work in digital systems. Developing new digital systems requires Lean Product Development principles, and some aspects of classic Lean (e.g., always reducing variability) are less applicable or may even be harmful. See, for example, [230] for further discussion (Chapter 4, "The Economics of Product Development Variability").

Related Topics

We have discussed some of the factors leading to the need for process management, but we have not yet come to grips with what it is. To start, think of a repeatable series of activities, such as when a new employee joins (see Figure 62, “Simple Process Flow”).

Process management can represent conditional logic (see Figure 63, “Conditionality”).

Process models can become extremely intricate, and can describe both human and automated activity. Sometimes, the process simply becomes too complicated for humans to follow. Notice how different the process models are from the card wall or Kanban board. In Kanban, everything is a work item, and the overall flow is some simple version of “to do, doing, done”. This can become complex when the flow gets more elaborate (e.g., various forms of testing, deployment checks, etc.). In a process model, the activity is explicitly specified on the assumption it will be repeated. The boxes representing steps are essentially equivalent to the columns on a Kanban board, but since sticky notes are not being used, process models can become very complex — like a Kanban board with dozens or hundreds of columns! Process management as a practice is discussed extensively in Context III. However, before we move on, two simple variations on process management are:

The Checklist Manifesto is the name of a notable book by author/surgeon Atul Gawande [109]. The title can be misleading; the book in no way suggests that all work can be reduced to repeatable checklists. Instead, it is an in-depth examination of the relationship between standardization and complexity. Like Case Management, it addresses the problem of complex activities requiring professional judgment.

Unlike Case Management (discussed below), it explores more time-limited and often urgent activities such as flight operations, large-scale construction, and surgery. These activities, as a whole, cannot be reduced to one master process; there is too much variation and complexity. However, within the overall bounds of flight operations, or construction, or surgery, there are critical sequences of events that must be executed, often in a specific order. Gawande discusses the airline industry as a key exemplar of this. Instead of one “master checklist” there are specific, clear, brief checklists for a wide variety of scenarios, such as a cargo hold door becoming unlatched.

There are similarities and differences between core BPM approaches and checklists. Often, BPM is employed to describe processes that are automated and whose progress is tracked in a database. Checklists, on the other hand, may be more manual, intended for use in a closely collaborative environment (such as an aircraft cockpit or operating room), and may represent a briefer period of time.

Full process management specifies tasks and their flow in precise detail. We have not yet got to that point with our Kanban board, but when we start adding checklists, we are beginning to differentiate the various processes at a detailed level. We will revisit Gawande’s work in Context III with the coordination technique of the submittal schedule.

Case Management is a concept used in medicine, law, and social services. Case Management can be thought of as a high-level process supporting the skilled knowledge worker applying their professional expertise. Cases are another way of thinking about the relationship between the Kanban board and process management (see Figure 64, “Process Management versus Case Management”).

IT consultant and author Rob England contrasts “Case Management” with “Standard Process” in his book Plus! The Standard+Case Approach: See Service Response in a New Light [94]. Some processes are repeatable and can be precisely standardized, but it is critical for anyone working in complex environments to understand the limits of a standardized process. Sometimes, a large “case” concept is sufficient to track the work. The downside may be that there is less visibility into the progress of the case — the person in charge of it needs to provide a status that can’t be represented as a simple report. We will see process management again in Section 6.2.3, “Operations Management” in our discussion of operational process emergence.

Evidence of Notability Workflow management in the basic emergent sense is a key precursor to full BPM. See, for example, [255].

Limitations Not all work can or should be reduced to a procedural paradigm. Higher-touch, more variable services and R&D work require different approaches, such as Case Management.

Related Topics

As the Senge quote implies, brute force does not scale well within the context of a system. One of the reasons for systems stability is feedback. Within the bounds of the system, actions lead to outcomes, which in turn affect future actions. This is a positive thing, as it is required to keep a complex operation on course.

Feedback is a problematic term. We hear terms like positive feedback and negative feedback and associate such usage with performance coaching and management discipline. That is not the sense of feedback in this document. The definition of feedback as used in this document is based on engineering and control theory.

Figure 65, “Reinforcing Feedback Loop” illustrates the classic illustration of a reinforcing feedback loop.

For example (as in Figure 66, “Reinforcing (Positive?) Feedback, with Rabbits”), “rabbit reproduction” can be considered as a process with a reinforcing feedback loop.

The more rabbits, the faster they reproduce, and the more rabbits. This is sometimes called a “positive” feedback loop, although the local gardener may not agree. This is why feedback experts (e.g., [268]) prefer to call this “reinforcing” feedback because there is not necessarily anything “positive” about it.

We can also consider feedback as the relationship between two processes (see Figure 67, “Feedback Between Two Processes”).

In the example, what if Process B is fox reproduction; that is, the birth rate of foxes (who eat rabbits) (see Figure 68, “Balancing (Negative?) Feedback, with Rabbits and Foxes”)?

More rabbits equal more foxes (notice the “+” symbol on the line) because there are more rabbits to eat! But what does this do to the rabbits? It means fewer rabbits (the “--” on the line). Which, ultimately, means fewer foxes, and at some point, the populations balance. This is classic negative feedback. However, the local gardeners and foxes don’t see it as negative. That is why feedback experts prefer to call this “balancing” feedback. Balancing feedback can be an important part of a system’s overall stability.

In an engineering sense, positive feedback is often dangerous and a topic of concern. A recent example of bad positive feedback in engineering is the London Millennium Bridge. On opening, the Millennium Bridge started to sway alarmingly, due to resonance and feedback which caused pedestrians to walk in cadence, increasing the resonance issues. The bridge had to be shut down immediately and retro-fitted with $9 million worth of tuned dampers [75].

As with bridges, at a technical level, reinforcing feedback can be a very bad thing in IT systems. In general, any process that is self-amplified without any balancing feedback will eventually consume all available resources, just like rabbits will eat all the food available to them. So, if you create a process (e.g., write and run a computer program) that recursively spawns itself, it will sooner or later crash the computer as it devours memory and CPU. See runaway processes.

Balancing feedback, on the other hand, is critical to making sure you are “staying on track”. Engineers use concepts of control theory; for example, damping, to keep bridges from falling down.

Section 6.1.1, “Digital Fundamentals” covered the user’s value experience, and also how services evolve over time in a lifecycle. In terms of the dual-axis value chain, there are two primary digital value experiences:

Additionally, the product team derives career value. This becomes more of a factor later in the game. We will discuss this further in Section 6.3.1, “Coordination and Process” — on organization — and Context IV, on architecture lifecycles and technical debt.

The product team receives feedback from both value experiences. The day-to-day interactions with the service (e.g., help desk and operations) are understood, and (typically on a more intermittent basis) the portfolio investor also feeds back the information to the product team (the boss’s boss comes for a visit).

Balancing feedback in a business and IT context takes a wide variety of forms:

In short, we see these two basic kinds of feedback:

The following should be considered:

One of the most important concepts related to feedback, one we will keep returning to, is that product value is based on feedback. We have discussed Lean Startup, which represents a feedback loop intended to discover product value. Don Reinertsen has written extensively on the importance of fast feedback to the product discovery process.

At a business level, there is a special kind of reinforcing feedback that defines the successful business (see Figure 69, “The Reinforcing Feedback Businesses Want”).

This is reinforcing feedback and positive for most people involved: investors, customers, employees. At some point, if the cycle continues, it will run into balancing feedback:

But those are problems that indicate a level of scale the business wants to have.

Finally, we should talk briefly about open-loop versus closed-loop systems.

In navigation terminology, the open-loop attempt to stick to a course without external information (e.g., navigating in the fog, without radar or communications) is known as "dead reckoning", in part because it can easily get you dead!

A good example of an open-loop system is the children’s game “pin the tail on the donkey” (see Figure 70, “Pin the Tail on the Donkey”^[4]). In “pin the tail on the donkey”, a person has to execute a process (pinning a paper or cloth “tail” onto a poster of a donkey — no live donkeys are involved!) while blindfolded, based on their memory of their location (and perhaps after being deliberately disoriented by spinning in circles). Since they are blindfolded, they have to move across the room and pin the tail without the ongoing corrective feedback of their eyes. (Perhaps they are getting feedback from their friends, but perhaps their friends are not reliable.)

Without the blindfold, it would be a closed-loop system. The person would rise from their chair and, through the ongoing feedback of their eyes to their central nervous system, would move towards the donkey and pin the tail in the correct location. In the context of a children’s game, the challenges of open-loop may seem obvious, but an important aspect of IT management over the past decades has been the struggle to overcome open-loop practices. Reliance on open-loop practices is arguably an indication of a dysfunctional culture. An IT team that is designing and delivering without sufficient corrective feedback from its stakeholders is an ineffective, open-loop system. Mark Kennaley [164] applies these principles to software development in much greater depth, and is recommended.

Engineers of complex systems use feedback techniques extensively. Complex systems do not work without them.

After the Korean War, the US Air Force wished to clarify why its pilots had performed in a superior manner to the opposing pilots who were flying aircraft viewed as more capable. A colonel named John Boyd was tasked with researching the problem. His conclusions are based on the concept of feedback cycles, and how fast humans can execute them. Boyd determined that humans go through a defined process in building their mental model of complex and dynamic situations. This has been formalized in the concept of the OODA loop (see Figure 71, “OODA Loop”^[5]).

OODA stands for:

Because the US fighters were lighter, more maneuverable, and had better visibility, their pilots were able to execute the OODA loop more quickly than their opponents, leading to victory. Boyd and others have extended this concept into various other domains including business strategy. The concept of the OODA feedback loop is frequently mentioned in presentations on Agile methods. Tightening the OODA loop accelerates the discovery of product value and is highly desirable.

We covered continuous delivery and introduced DevOps in Competency Area 3. Systems theory provides us with powerful tools to understand these topics more deeply.

One of the assumptions we encounter throughout digital management is the idea that change and stability are opposing forces. In systems terms, we might use a diagram like Figure 72, “Change versus Stability” (see [33] for original exploration]). As a Causal Loop Diagram (CLD), it is saying that change and stability are opposed — the more we have of one, the less we have of the other. This is true, as far as it goes — most systems issues occur as a consequence of change; systems that are not changed in general do not crash as much.

The trouble with viewing change and stability as diametrically opposed is that change is inevitable. If simple delaying tactics are put in, these can have a negative impact on stability, as in Figure 73, “Change Vicious Cycle”. What is this diagram telling us? If the owner of the system tries to prevent change, a larger and larger backlog will accumulate. This usually results in larger and larger-scale attempts to clear the backlog (e.g., large releases or major version updates). These are riskier activities which increase the likelihood of change failure. When changes fail, the backlog is not cleared and continues to increase, leading to further temptation for even larger changes.

How do we solve this? Decades of thought and experimentation have resulted in continuous delivery and DevOps, which can be shown in terms of system thinking in Figure 74, “The DevOps Consensus”.

To summarize a complex set of relationships:

Notice the reinforcing feedback loop (the “R” in the looped arrow) between change frequency and change capability. Like all diagrams, this one is incomplete. Just making changes more frequently will not necessarily improve the change capability; a commitment to improving practices such as monitoring, automation, and so on is required, as the organization seeking to release more quickly will discover.

Evidence of Notability

Discussions of systems thinking, feedback, and OODA occur repeatedly throughout IT and digital management literature; e.g., ITIL’s Service Strategy volume [282] and The DevOps Handbook [166].

Limitations

Systems thinking is an advanced and somewhat theoretical topic, and discussions of it should carefully consider the audience.

Related Topics

Description

Operations management is a broad topic in management theory, with whole programs dedicated to it in both business and engineering schools. Companies frequently hire Chief Operations Officers to run the organization. We started to cover operations management in Section 6.2.2, “Work Management”, as we examined the topic of “work management” — in traditional operations management, the question of work and who is doing it is critical. For the Digital Practitioner, “operations” tends to have a more technical meaning than the classic business definition, being focused on the immediate questions of systems integrity, availability and performance, and feedback from the user community (i.e., the service or help desk). We see such a definition from Limoncelli et al.:

… operations is the work done to keep a system running in a way that meets or exceeds operating parameters specified by a Service-Level Agreement (SLA). Operations includes all aspects of a service’s lifecycle: from initial launch to the final decommissioning and everything in between [178 p. 147].

Operations often can mean “everything but development” in a digital context. In the classic model, developers built systems and “threw them over the wall” to operations. Each side had specialized processes and technology supporting their particular concerns. However, recall our discussion of design thinking — the entire experience is part of the product. This applies to both those consuming it as well as running it. Companies undergoing Digital Transformation are experimenting with many different models; as we will see in Context III, up to and including the complete merging of Development and Operations-oriented skills under common product management.

Since this document has a somewhat broader point of view covering all of digital management, it uses the following definition of operations:

Operations is the direct facilitation and support of the digital value experience. It tends to be less variable, more repeatable, yet more interrupt-driven than product development work. It is more about restoring a system to a known state, and less about creating new functionality.

What do we mean by this? In terms of our dual-axis value chain, operations supports the day-to-day delivery of the digital “moment of truth” (see Figure 75, “Operations Supports the Digital Moment of Truth”).

The following are examples of “operations” in an IT context. Some are relevant to a “two pizza product team” scenario; some might be more applicable to larger environments:

As above, the primary thing that operations does not do is develop new systems functionality. Operations is process-driven and systematic and tends to be interrupt-driven, whereas R&D fails the “systematic” part of the definition (review the definitions in process, product, and project management). However, new functionality usually has operational impacts. In manufacturing and other traditional industries, product development was a minority of work, while operations was where the bulk of work happened. Yet when an operational task involving information becomes well defined and repetitive, it can be automated with a computer.

This continuous cycle of innovation and commoditization has driven closer and closer ties between “development” and “operations”. This cycle has also driven confusion around exactly what is meant by “operations”. In many organizations there is an “Infrastructure and Operations” (I&O) function. Pay close attention to the naming. A matrix may help because we have two dimensions to consider here (see Table 11, “Application, Infrastructure, Development, Operations”).

Notice that we distinguish carefully between the application and infrastructure layers. This document using the following pragmatic definitions:

Infrastructure services and/or products, as discussed in Digital Infrastructure, need to be designed and developed before they are operated, just like applications. This may all seem obvious, but there is an industry tendency to lump three of the four cells in the table into the I&O function when, in fact, each represents a distinct set of concerns.

Either a digital system is available and providing a service, or it isn’t. The concept of "service level” was mentioned above by Limoncelli. A level of service is typically defined in terms of criteria such as:

A Service-Level Agreement, or SLA, is a form of contract between the service consumer and service provider, stating the above criteria in terms of a business agreement. When a service’s performance does not meet the agreement, this is sometimes called a “breach” and the service provider may have to pay a penalty (e.g., the customer gets a 5% discount on that month’s services). If the service provider exceeds the SLA, perhaps a credit will be issued.

SLAs drive much operational behavior. They help prioritize incidents and problems, and the risk of proposed changes are understood in terms of the SLAs.

In all of IT (whether “infrastructure” or “applications”) there is a particular concern with managing state. IT systems are remarkably fragile. One incorrect bit of information — a “0” instead of a “1” — can completely alter a system’s behavior, to the detriment of business operations depending on it.

Therefore, any development of IT — starting with the initial definition of the computing platform — depends on the robust management state.

The following are examples of state:

State also has more transient connotations:

Finally, we saw in the previous section some server/application/business mappings. These are also a form of state.

It is therefore not possible to make blanket statements like “we need to manage state”. Computing devices go through myriads of state changes with every cycle of their internal clock. (Analog and quantum computing are out of scope for this document.)

The primary question in managing state is “what matters”? What aspects of the system need to persist, in a reliable and reproducible manner? Policy-aware tools are used extensively to ensure that the system maintains its configuration, and that new functionality is constructed (to the greatest degree possible) using consistent configurations throughout the digital pipeline.

“Production” is a term that new IT recruits rapidly learn has forbidding connotations. To be “in production” means that the broader enterprise value stream is directly dependent on that asset. How do things get to be “in production”? What do we mean by that?

Consider the fundamental principle that there is an IT system delivering some “moment of truth” to someone. This system can be of any scale, but as above we are able to conceive of it having a “state”. When we want to change the behavior of this system, we are cautious. We reproduce the system at varying levels of fidelity (building “lower” environments with Infrastructure as Code techniques) and experiment with potential state changes. This is called development. When we start to gain confidence in our experiments, we increase the fidelity and also start to communicate more widely that we are contemplating a change to the state of the system. We may increase the fidelity along a set of traditional names (see Figure 76, “Example Environment Pipeline”):

The final state, where value is realized, is “production”. Moving functionality in smaller and smaller batches, with increasing degrees of automation, is called continuous delivery.

The fundamental idea that new system functionality sequentially moves (“promotes”) through a series of states to gain confidence before finally changing the state of the production system is historically well established. You will see many variations, especially at scale, on the environments listed above. However, the production state is notoriously difficult to reproduce fully, especially in highly distributed environments. While Infrastructure as Code has simplified the problem, lower environments simply can’t match production completely in all its complexity, especially interfaced interactions with other systems or when large, expensive pools of capacity are involved. Therefore there is always risk in changing the state of the production system. Mitigating strategies include:

Another important development in environmental approaches is A/B testing or canary deployments. In this approach, the “production” environment is segregated into two or more discrete states, with different features or behaviors exposed to users in order to assess their reactions. Netflix uses this as a key tool for product discovery, testing the user reaction to different user interface techniques, for example. Canary deployments are when a change is deployed to a small fraction of the user base, as a pilot.

The concept of “environment” can reinforce functional silos and waterfall thinking, and potentially the waste of fixed assets. Performance environments (that can emulate production at scale) are particularly in question.

Instead, in a digital infrastructure environment (private or public), the kind of test you want to perform is defined and that capacity is provisioned on-demand.

It used to be that the concept of “testing in production” was frowned upon. Now, with these mitigating strategies, and the recognition that complex systems cannot ever be fully reproduced, there is more tolerance for the idea. But with older systems that may lack automated testing, incremental deployment, or easy rollback, it is strongly recommended to retain existing promotion strategies, as these are battle-tested and known to reduce risk. Often, their cycle time can be decreased.

On the other hand, development systems must never be treated casually.

It is, therefore, important to treat “development” platforms with the same care as production systems. This requires nuanced approaches: with Infrastructure as Code, particular virtual machines or containers may represent experiments, expected to fail often and be quickly rebuilt. No need for burdensome change processes when virtual machine base images and containers are being set up and torn down hundreds of times each day! However, the platforms supporting the instantiation and teardown of those virtual machines are production platforms, supporting the business of new systems development.

Evidence of Notability

Operations management is a broad topic in management and industrial theory, with dedicated courses of study and postgraduate degrees. The intersection of operations management and digital systems has been a topic of concern since the first computers were developed and put into use for military, scientific, and business applications.

Limitations

Operations is repeatable, interrupt-driven, and concerned with maintaining a given state of performance. It is usually rigorously distinguished from R&D.

Related Topics

Limoncelli et al. define monitoring as follows:

Monitoring is the primary way we gain visibility into the systems we run. It is the process of observing information about the state of things for use in both short-term and long-term decision-making. [178].

But how do we “observe” computing infrastructure? Monitoring tools are the software that watches the software (and systems more broadly).

A variety of techniques are used to monitor computing infrastructure. Typically these involve communication over a network with the device being managed. Often, the network traffic is on the same network carrying the primary traffic of the computers. Sometimes, however, there is a distinct “out-of-band” network for management traffic. A simple monitoring tool will interact on a regular basis with a computing node, perhaps by “pinging” it periodically, and will raise an alert if the node does not respond within an expected timeframe (see Figure 77, “Simple Monitoring”).

More broadly, these tools provide a variety of mechanisms for monitoring and controlling operational IT systems; they may monitor:

Some monitoring covers low-level system indicators not usually of direct interest to the end user. Other simulates end-user experience; SLAs are often defined in terms of the response time as experienced by the end user (see Figure 79, “User Experience Monitoring”). See [178], Chapters 16-17.

All of this data may then be forwarded to a central console and be integrated, with the objective of supporting the organization’s SLAs in priority order. Enterprise monitoring tools are notorious for requiring agents (small, continuously running programs) on servers; while some things can be detected without such agents, having software running on a given computer still provides the richest data. Since licensing is often agent-based, this gets expensive.