Managing Digital: Concepts and Practices

Consider the following scenario:

A woman (Dinner out tonight? ^[1]) is wondering if she can afford to dine out that evening. She uses her mobile device to access her banking information and determines that in fact she does have enough money to do so. She also uses her mobile device to make a reservation and contact some friends to join her. Finally, she uses social navigation software to avoid heavy traffic, arriving at the restaurant in time for an enjoyable evening with her friends.

IT pervaded this experience. The origins, layers, and complex connections of the distributed systems involved are awe-inspiring to consider.

The screen on her cell phone represents information accessed and presented by a complex "architecture" of distributed systems, including her cell phone’s hardware and operating system, which run software written and deployed by the bank, which communicates with computers ("servers") in the bank’s data center. The communication with her bank’s central systems is supported by 4G LTE data which in turn relies on the high-volume IP backbone networks operated by the telecommunications carriers, based on research into packet switching now approaching 50 years old. The application operating on the cell phone interacts with core banking systems via sophisticated and highly secure middleware, crossing multiple network control points. This middleware talks in turn to the customer demand deposit system that still runs on the mainframe.

The mainframe is now running the latest version of IBM’s z/OS operating system (a direct descendant of OS/360, one of the most significant operating systems in the history of computing). The customer demand deposit banking application running on the mainframe is still based on code written in the lowest level assembler. Some of the comments in this code date back to the 1970s. It has been tuned and optimized over the decades into a system of remarkable speed and efficiency. Although re-platforming it is periodically discussed, the cost/benefit ratio for such a project has to date not been favorable.

The reservation system looks similar on the mobile device, but the network routes it to a large cloud data center hosting the reservation system. The back-end application here is very different from the banking system; the programming languages are newer, the database is structured very differently, and the operating system is Linux.

Finally, the navigation software looks much like the reservation system, as it too is based on the cloud. However, the system is much more active as it is continually processing inputs from millions of drivers in thousands of cities and updating traffic maps for those drivers in real time so that they can choose the most optimal route to their destinations (e.g., dinner). The capabilities of this system are comparable to an air traffic control system, and yet it is available as a free download for our IT user.

The resulting value (as in Digital made this gathering easier ^[2]) is clear:

Clearly, IT added value to her life and helped maximize her experience of social enjoyment.

As we have seen, there are many ways in which digital systems deliver value. Some systems serve as the modern equivalent of file cabinets: massive and secure storage for financial transactions, insurance records, medical records, and the like. Other systems enable the transmission of information around the globe, whether as emails, web pages, voice calls, video on-demand, or data to be displayed in a smartphone application (app). Some of these systems support engaged online communities and social interactions with conversations, media sharing, and even massive online gaming ecosystems. Yet other systems enable penetrating analysis and insight by examining the volumes of data contained in the first two kinds of systems for patterns and trends. Sophisticated statistical techniques and cutting-edge approaches like neural network-based machine learning increase the insights our digital systems are capable of, at a seemingly exponential rate.

Digital technology generates value in both direct and indirect ways. People have long consumed (and paid for) communication services, such as telephone services. Broadcast entertainment was a different proposition, however. The consumer (the person with the radio or television) was not the customer (the person paying for the programming to go out over the airwaves). New business models sprung up to support the new media through the sale of advertising air time. In other words, the value proposition was indirect, or at least took multiple parties to achieve: the listener, the broadcaster, and the advertiser. Finally, some of the best known uses of digital technology were and are very indirect -— for example, banks and insurance agencies using the earliest computers to automate the work of thousands of typists and file clerks.

From these early business models have evolved and blossomed myriads of creative applications of digital technology for the benefit of human beings in their ongoing pursuit of happiness and security. We see the applications mentioned at the outset: online banking, messaging, restaurant reservations, and traffic systems. Beyond that we see the use of digital technology in nearly every aspect of life.

Digital and IT pervades all of the major industry verticals (e.g., manufacturing, agriculture, finance, retail, healthcare, transportation, services) and common industry functions (e.g., supply chain, human resources, corporate finance, and even IT itself). Digital systems and technologies also are critical components of larger-scale industrial, military, and aerospace systems. For better or worse, general-purpose computers are increasingly found controlling safety-critical infrastructure and serving as an intermediating layer between human actions and machine response. Robotic systems are based on software, and the Internet of Things ultimately will span billions of sensors and controllers in interconnected webs monitoring and adjusting all forms of complex operations across the planet.

We’ve started this book in the previous section by providing an example of digital or IT value, without much discussion of how it is delivered. This is deliberate. But what is IT, anyways?

None of these are how this book defines “IT.” Although this is not a technical book on computer science or software engineering, the intent is that it reflects and is compatible with foundational principles.

“Information Technology” is ultimately based on the work of Claude Shannon, Alan Turing, Alonzo Church, John von Neumann, and the other pioneers who defined the central problems of information theory, digital logic, computability, and computer architecture.

Additionally, as an organizational function, IT also draws on organizational theory, systems theory, human factors and psychology, and more recent concepts such as design thinking, among many other areas. Discussions of “IT” become contentious because some think of the traditional organization, while others think of the general problem area. IT has a long history as a corporate function, a single hierarchy under a powerful Chief Information Officer. This model has had its dysfunctions, including a reputation for being slow and expensive. Often, when one encounters the term “IT,” the author using the term is referring to this organizational tradition.

We are less interested in the future of IT as a distinct organizational structure. There are many different models, from fully centralized to fully embedded. Organizational structure will be discussed in Part III.

For this book, we define “Information Technology” in terms of its historic origins. We look to IT’s common origins in automating the laborious and error-prone processes of computation, through the application of digital logic technologies based on information transmission.

Regardless of organizational form or delivery methods, IT is defined by these origins. It does not matter if the application developers and systems engineers ultimately report up through the CIO, the CMO, the CFO, or the COO. There are common themes throughout IT and digital as a professional domain: the fragility and complexity of these systems, the need for layered abstractions in their management, and more.

IT increasingly permeates business operations and social interactions. The breadth and depth of IT support for virtually all domains of society continues to expand. Lately, this is known as Digital Transformation [281].

The role of IT seems critical to society and the economy, but there are various points of view. Nicholas Carr, in his controversial Harvard Business Review article “IT Doesn’t Matter,” recognized that IT was becoming commoditized in an important sense [54]. As cloud providers started to offer utility-style computing, the choice of particular vendors of computers was no longer strategic. Looking to history, Carr argued that just as businesses no longer have “Vice Presidents for Electricity,” so businesses no longer need Chief Information Officers or dedicated IT departments.

Carr has insight -— there is no question IT is becoming pervasive -— but he ultimately reflects a narrow view of what “IT” is. If “IT” were merely computation at the lowest level -— just shuffling bits of information around, doing a little math -— then perhaps it could be embedded throughout a business like electricity.

But IT has emergent aspects that are not comparable to electrical power. As it pervades all dimensions of business operations, it brings its concerns with it: complexity, fragility, and the skills required to cope with them.

One watt of electrical power is like any other watt of electrical power, and is therefore a commodity. We can use it to run toasters, hair dryers, or industrial paint mixers, and there is little concern (beyond supply and demand management) that the consumption of power by the paint mixer will affect the toaster. It’s also true that one cycle of computing, in a certain sense, is like any other cycle. But IT systems interact with each other in surprising and unpredictable ways, orders of magnitude more complex than electrical power grids. (This is not to imply the modern electrical grid is a simple system!)

IT also radically transforms industries: from retail to transportation to manufacturing to genetics. Applied software-centric IT is unleashing remarkable economic disruption.

A lawyer may depend on a cell phone, but (in keeping with Carr) beyond its provision as a commodity service, it’s not a differentiator in delivering the legal strategies a firm needs. A graphic designer may use computerized graphic tools, but these have become relatively standardized and commoditized in the past 20 years, and probably are not a source of competitive advantage in the quest for new marketing clients.

On the other hand, consider a text analytic algorithm that replaces thousands of paralegals, resulting in order-of-magnitude more accurate legal research in a fraction of cost and time. This is strategic and disruptive to the legal community. A superior supply chain algorithm, and the ability to improve it on an ongoing basis, may indeed elevate a logistics firm’s performance above competitors. In cases like these -— and they seem to be increasing -— IT matters very much. The annual State of DevOps research finds that “Firms with high-performing IT organizations were twice as likely to exceed their profitability, market share, and productivity goals.” [95]

In the digitally transforming economy, traditional “back office” IT organizations find themselves called on to envision, develop, and support market-facing applications of IT. And what starts with one market-facing use case can quickly expand into entire portfolios. It is such cases that are of particular concern in this book. Ultimately, it is possible that IT is the most strategic capability an organization can invest in. As Diomidis Spinellis, editor in chief of IEEE Software notes [256],

“…other industries are also producing what’s in effect software (executable knowledge) but not treating it as such … Although many industries have developed their own highly effective processes over the years, software engineering maintains an essential advantage. It has developed methods and tools that let even small teams manage extremely high complexity … This advantage is important because the complexity in non-software activities is also increasing inexorably … the time has come to transform our world … by giving back to science and technology the knowledge software engineering has produced.”

This ability to manage complexity, to turn tacit into explicit and formalize the previously unstructured, is an essential aspect of Digital Transformation.

So, how do we define an IT problem, as opposed to other kinds of business problems? An IT problem is any problem where you are primarily constrained by your capability for and understanding of IT.

Regardless of whether IT is housed under a traditional CIO, an operations capability, a Chief Marketing Officer (CMO), or a “line of business”, when it is critical to operations certain concerns inevitably follow:

Newcomers who propose changes to these practices in hopes of making IT more “Agile” are often surprised to find that these concerns were not mere bureaucracy, but instead had well-grounded origins in past failures. Ignoring these lessons is perilous. And yet, the traditional, process-heavy IT organization does seem dysfunctional from a business point of view: a central theme of this book.

So, is there any real difference between "IT" and "Digital"? Perception is important, and IT as a term is becoming outdated. It is associated with an earlier generation of centralized computers running back-office systems. "Digital," on the other hand, is associated with the everyday experience of computers and software, pervasive throughout our lives. "Digital" of course is based on IT, if we are to be precise in our use of words. But the social and marketing connotations are clearly distinct.

This book is not completely precise in distinguishing the two terms, and this is to some extent deliberate. But in general, if you think of "IT" as more structured and internal, and "digital" as more market-facing, you will be on the right track.

Let’s examine our diner’s value experience (The basis of digital value ^[3]) in more detail, without getting unnecessarily technical, and clarify some definitions along the way. The first idea we need to cover is the "moment of truth.” In terms of IT, this English-language cliche (elaborated into an important service concept by SAS group president Jan Carlzon [53]) represents the user’s experience of value.

In the example, our friend seeking a relaxing night out had several moments of truth:

Each of these individual value experiences was co-created by our friend’s desire for value, and the response of a set of IT resources.

In order to view her balance, our user is probably using 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 our friend’s device:

Consider: what does all this mean to our 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. Our friend is seeking some value that IT 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 (The IT 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.

This fundamental tension between what a system is supposed to do, versus how it does it, pervades IT management and will likely define your career. “Don’t trouble me with the details, just give me the results” is the overall theme, and we encounter this reaction to complexity in many aspects of life.

Terminology is important. We need to have a more precise way of describing the IT, beyond just saying there is “lots” of it. A variety of terms are used in this text:

We also see discussion of components, resources, subsystems, assets, and many more terms.

In general, in this book, we will use the following definitions:

Finally, the concept of the “IT stack” is important. Notice how the different technology layers appear “stacked” in The IT stack supports the moment of truth. Layered approaches to understanding IT are common, such as the OSI model and the Zachman framework (see Further Reading for useful references).

In the previous section we discussed the question of “who pays/who benefits” for the service, proposing that the service consumer, the service customer, and the service sponsor might be three distinct roles (sometimes collapsing into two or one individuals).

We see this again in how we can categorize the "customers” of IT services and systems. Roughly, such 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, an HR 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 other words, it’s all relative. Especially when products are not market-facing, we start to run into the problem of distinguishing discovery versus design, as we discuss below.

As you start to think about digital value, you must think about the context for your startup or product idea. What is 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 [225].

Rogers' research proposed the idea of “Adopter Categorization on the Basis of Innovativeness,” with a well-known graphic (Technology adoption categories^[4]).

Rogers went on to characterize the various stages:

Rogers' figure was popularized in the following variation by Geoffrey Moore [189] in his bestseller Crossing the Chasm (Purported “chasm” between adopter categories ^[5]).

You’ll see Moore’s variation often, but you should be aware that Rogers himself (the person who has researched the data) says that (p. 282):

Past research shows no support for this claim of a “chasm” between certain adopter categories. On the contrary, innovativeness, if measured properly, is a continuous variable and there are no sharp breaks or discontinuities between adjacent adopter categories (although there are important differences between them).

The idea of technology diffusion frames the problem for us, but we need more. Steve Blank, in his influential book The Four Steps to Epiphany [31 p. 31], argues there are four categories for startups:

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 [269]:

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.

Let’s start with two well known approaches that can help you bridge from an understanding of your product context, to an effective vision for building and sustaining a product:

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