Phase C: Information Systems Architectures - Data Architecture

Objective | Approach | Inputs | Steps | Outputs

This chapter describes the Data Architecture part of Phase C.


The objective here is to define the major types and sources of data necessary to support the business, in a way that is:

It is important to note that this effort is not concerned with database design. The goal is to define the data entities relevant to the enterprise, not to design logical or physical storage systems. (However, linkages to existing files and databases may be developed, and may demonstrate significant areas for improvement.)


Enterprise Continuum

As part of this phase, the architecture team will need to consider what relevant Data Architecture resources are available in the organization's Enterprise Continuum; in particular, generic data models relevant to the organization's industry "vertical" sector. For example:

Gap Analysis

A key step in validating an architecture is to consider what may have been forgotten. The architecture must support all of the essential information processing needs of the organization. The most critical source of gaps that should be considered is stakeholder concerns that have not been addressed in architectural work.

Types of data gap:

Gap analysis highlights shortfalls in data services and/or data elements that have been accidentally left out, deliberately eliminated, or are yet to be defined. Gap Analysis Matrix in Phase D illustrates an example of a gap analysis matrix. The suggested steps are as follows:

When the exercise is complete, anything under "Eliminated Services" or "New Services" is a gap, which should either be explained as correctly eliminated, or marked as to be addressed by reinstating or developing/procuring the function.


Inputs to this phase are:


  1. Develop Baseline Data Architecture Description

    Develop a Baseline Description of the existing Data Architecture, to the extent necessary to support the Target Data Architecture. The scope and level of detail to be defined will depend on the extent to which existing data elements are likely to be carried over into the Target Data Architecture, and on whether existing architectural descriptions exist, as described in Approach . To the extent possible, identify the relevant Data Architecture building blocks, drawing on the Architecture Continuum, and review/verify the following primitives from the Zachman Framework:

  2. Review and Validate Principles, Reference Models, Viewpoints, and Tools
    1. Review and validate (or generate, if necessary) the set of data principles.

      These will normally form part of an overarching set of architecture principles. Guidelines for developing and applying principles, and a sample set of data principles, are given in Part IV: Resource Base, Architecture Principles .

    2. Select relevant Data Architecture resources (reference models, patterns, etc.) from the Architecture Continuum, on the basis of the business drivers, and the stakeholders and concerns.
    3. Select relevant Data Architecture viewpoints (for example, stakeholders of the data - regulatory bodies, users, generators, subjects, auditors, etc.; various time dimensions - real-time, reporting period, event-driven, etc.; locations; business processes); i.e., those that will enable the architect to demonstrate how the stakeholder concerns are being addressed in the Data Architecture.
    4. Identify appropriate tools and techniques (including forms) to be used for data capture, modeling, and analysis, in association with the selected viewpoints. Depending on the degree of sophistication warranted, these may comprise simple documents or spreadsheets, or more sophisticated modeling tools and techniques such as data management models, data models, etc. Examples of data modeling techniques are:
      • IDEF
      • Object Role Modeling
  3. Create Architecture Model(s)
    1. For each viewpoint, create the model for the specific view required, using the selected tool or method.

      Examples of logical data models are:

      • The C4ISR Architecture Framework Logical Data Model
      • ARTS Data Model for the Retail industry
      • POSC Data Model for the Petrotechnical industry
    2. Assure that all stakeholder concerns are covered. If they are not, create new models to address concerns not covered, or augment existing models (see above). Model the following:
      1. Business data model (entities, attributes, and relationships)

        Draw entity-relationship diagrams to illustrate views of the Data Architecture to address the concerns of stakeholders.

      2. Logical data model (logical views of the actual data of interest)
      3. Data management process models, including:
        • Data dissemination view
        • Data lifecycle view
        • Data security view
        • Data model management view
      4. Relate data entities to business functions in the Business Architecture, indicating which of the CRUD operations (Create, Reference, Update, and Delete) are performed by which functions.
        • Relate each lowest-level business function in the Business Architecture to the set of data entities, indicating which of the CRUD operations (Create, Reference, Update, and Delete) are performed by the function concerned.
        • Generate entity-business function matrices tabulating all the relationships.
        • Review and validate the entity-business function matrices, checking that each entity is created by at least one function, and referenced or updated by at least one other function.
        • Time permitting, relate entities to the application systems described in the Baseline Applications Architecture Description.
    3. Ensure that all information requirements in the Business Architecture are met.
    4. Perform trade-off analysis to resolve conflicts (if any) among the different views.

      One method of doing this is CMU/SEI's Architecture Trade-off Analysis (ATA) Method (refer to

    5. Validate that the models support the principles, objectives, and constraints.
    6. Note changes to the viewpoint represented in the selected models from the Architecture Continuum, and document.
    7. Test architecture models for completeness against requirements.
  4. Select Data Architecture Building Blocks
    1. Identify required building blocks and check against existing library of building blocks, re-using as appropriate.
    2. Where necessary, define new Data Architecture Building Blocks.
  5. Conduct Formal Checkpoint Review of Architecture Model and Building Blocks with Stakeholders

    Review the entity-business function matrices generated in Step 3, and the Business Architecture generated in Phase B.

  6. Review Qualitative Criteria (e.g., performance, reliability, security, integrity)

    Review the qualitative criteria, providing as many measurable criteria as possible (e.g., costs, minimum tolerable data losses, maximum data volumes at peak times, etc.). Use to specify required service levels for data services (for example, via formal Service Level Agreements (SLAs)).

    The goal here is to guide the Applications and Technology Architecture efforts as to the qualities required in the applications, and the underlying technology, that manage and process the data.

  7. Complete Data Architecture
    1. Select standards for each of the Architecture Building Blocks, re-using as much as possible from the reference models selected from the Architecture Continuum.
    2. Fully document each Architecture Building Block.
    3. Final cross-check of overall architecture against business requirements. Document rationale for building block decisions in architecture document.
    4. Document final requirements traceability report.
    5. Document final mapping of the architecture within the Architecture Continuum. From the selected Architecture Building Blocks, identify those that might be re-used, and publish via the architecture repository.
    6. Document rationale for building block decisions in architecture document.
    7. Prepare Data Architecture Report. Generate the Data Architecture document, comprising some or all of:
      • Business data model
      • Logical data model
      • Data management process model
      • Data entity/business function matrix
      • Data interoperability requirements (e.g., XML schema, security policies)

      If appropriate, use reports and/or graphics generated by modeling tools to demonstrate key views of the architecture. Route the Data Architecture document for review by relevant stakeholders, and incorporate feedback.

  8. Conduct Checkpoint/Impact Analysis

    Check the original motivation for the architecture project and the Statement of Architecture Work against the proposed Data Architecture. Conduct an Impact Analysis, to:

    1. Identify any areas where the Business Architecture (e.g., business practices) may need to change to cater for changes in the Data Architecture (for example, changes to forms or procedures, application systems, or database systems).

      If the impact is significant, this may warrant the Business Architecture being revisited.

    2. Identify any areas where the Applications Architecture (if generated at this point) may need to change to cater for changes in the Data Architecture (or to identify constraints on the Applications Architecture about to be designed).

      If the impact is significant, this may warrant the Applications Architecture being revisited, if already developed in this cycle.

    3. Identify any constraints on the Technology Architecture about to be designed.
    4. Refine the proposed Data Architecture only if necessary.
  9. Perform Gap Analysis (see Approach) and Create Report
    1. Create gap matrix as described above.
    2. Identify building blocks to be carried over, classifying as either changed or unchanged.
    3. Identify eliminated building blocks.
    4. Identify new building blocks.
    5. Identify gaps and classify as those that should be defined and those inherited.


The outputs of this phase are:

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