The Data Strategy: Enterprise Architecture Partnership

It’s wonderful to see that governments and organizations are recognizing data as an asset. And it’s important to see why a data strategy and enterprise architecture partnership are so effective. Today, many organizations are going digital, and the government in particular is moving toward a digital transformation. This has resulted in a conclusion that improvements need to be made, and that leads us to data strategy.

The Role and Purpose of Data Strategy

Strategy is defined as “a course of action an enterprise has decided to take to achieve one or more of its goals”. Therefore data strategy focuses on the control and management of data to ensure its quality and veracity for conducting business.

Typical Pillars of Data Strategy
  • Discoverable: dealing with ensuring that the data that is needed can be located
  • Accessible: ensuring that the data can be accessed by those needing it
  • Secure: ensuring that the data is properly protected as well
  • Comprehensible: ensuring the data is understandable consistent clear and unambiguous
  • Shareable: Ensuring that the data can not only be shared with relevant stakeholders–and encouraged
  • Reliable: Ensuring that data can be trusted for its purpose
Role and Purpose of Enterprise Architecture

Enterprise architecture focuses on all elements and their relationships that constitute an enterprise in the following ways:

  • Maintains architectural elements including business processes and capabilities, roles and organizational units, applications and systems, information and data, and technological components.
  • Aligns elements to business imperatives such as vision, mission, strategies, policies/rules, goals, and so on.
  • Provides cross-organizational impact analysis that identifies the consequences of change to one enterprise area to other areas.
  • Provides a holistic “single pane of glass” view of how the organization is structure

The Challenge to a Successful Transition

When making a successful transition, changing the attitude must look beyond the data itself. Some problems include focusing only on the data and its quality, which leaves out aspects of a complex ecosystem that can adversely impact the realization of a data strategy. Also, lack of insight into the inner-workings and complexities of the applications, processes and people that rely upon and operate the data raises the risk of good intentions resulting in less-than-stellar results.

The Solution

The solution to get out of this siloed thinking and accounting for the entire ecosystem requires partnering that encourages open and timely information sharing. And when it comes to data strategy realization, collaboration with enterprise architecture benefits not only treatment of the data assets but the enterprise operations as a whole.

The Collaboration Model

Primary principle: Must restructure an enterprise to realize a strategy

  • A data strategy is a defined course of action to move an organization to a desired result (data driven enterprise)
  • Realizing a data strategy requires assembling all the “piece parts” (architectural elements) of an enterprise in a manner that enables an organization to achieve this desired result (data driven enterprise)

DS-EA collaboration encompasses the identifying, mapping, analyzing, tracking and measuring of all the enterprise architecture elements needed to realize the strategy that will yield the desired result.

So, How Does EA Contribute to a Data Strategy Realization?

Regarding the Six Pillars mentioned above:

  • Discoverable: Full location reporting through business data usage maps
  • Accessible: Targeted access control utilizing roles usage and performance responsibility assignments
  • Secure: Informed security assessments leveraging platform protection mechanisms descriptions
  • Comprehensible: Jump-start data comparisons/improvements utilizing location reporting coupled with data element alignment to policies and standards
  • Shareable: Identification of data sharing and consolidation opportunities by combining data location, usage and responsibilities analysis
  • Reliable: Reliability-rating analysis findings/insight informed by data element trustworthy scores

What Are Potential Pitfalls of Non-Collaboration?

Not leveraging EA’s holistic view risks missing something or introducing unintended consequences.

  • Cost: Without accounting for upstream and downstream impacts, data improvement initiatives may under-estimate budget requirements
  • Scope: Without a thorough understanding of the full “reach” of a proposed data improvement measure, the overall work effort may be underestimated
  • Complexity: Without recognizing the inherent complexity of the organizations interrelated ecosystem, the degree of difficulty of enacting the data improvement may be underestimated
  • Consistency: Without considering all users and uses of a data element, standardization improvements could adversely affect areas reliant upon non-standard representations
  • Modernization: Without aligning data improvement and modernization efforts, timings of changes could get out of synch or result in wasted or duplicative efforts

An Example: Applying This Concept to the DoD Data Strategy

DoD’s Data Strategy Summarized

The strategy’s purpose is “Unleashing Data to Advance National Defense Strategy”. The strategy lays out a compelling argument for becoming a data-centric organization.

  • Guiding Principles: to control and bound its vision
  • Essential Capabilities: reflect the skills and competencies needed to obtain its vision
  • Goals and Objectives to measure its progress toward its vision

As a problem statement, the DoD states it “has lacked enterprise data management to ensure that trusted critical data is widely available.”

Operationalizing the Strategy — EA’s Value Add

EA’s contributions to the DoD’s data strategy realization:

  • Whole Ecosystem Impact: EA’s full line of sight view from business goals through to data element usage enables an understanding of the full scope and criticality of individual data elements
  • Holistic Perspective on Change: EA’s representation of all architectural elements involved in delivering business capabilities allows for thorough impact analysis of a proposed data change
  • Entire Ecosystem Evaluation and Scoring: EA’s full delivery system view allows for aggregating measures across an entire flow or transaction to help analyze if inefficiencies are due to data related issues
  • Alignment to Business Strategies and Goals: EA’s association of architecture elements (including data) to business imperatives provides a complete view of all elements involved in meeting those imperatives
  • Compliance to Laws, Regulations, and Policies: EA’s linkage to all architecture elements (including data) to the LRPs to which they must comply, gives a holistic picture of all compliance requirements
Mapping EA Contributions to Guiding Principles

EA can be used to monitor and track adherence to the Guiding Principles. All Guiding Principles are incorporated into the EA repository, and can be mapped to all affected elements–providing a full view of each element’s required adherence.

Mapping EA Contributions to Essential Capabilities

EA embodies aspects of each identified Capability. The DoD Essential Capability of Architecture, Standards, and Governance align with EA’s capabilities, making collaboration straightforward.

  • Architecture speaks to enablement through technology – EA provides insight into both current and future state architecture ensuring the architecture is reflective of the data vision.
  • Standards are incorporated throughout the EA where not only data standards are maintained, but also architectural patterns built on standard technologies
  • Governance is applied to the EA, and works alongside and is complementary to Data Governance thereby ensuring consistency in controls.
Mapping EA Contributions to Goals and Objectives

EA is integral to ensuring the achievement of the goals. EA’s capability of defining measurable characteristics of data elements, along with associating these data elements to other architectural items, ensures progress towards goal achievement can be monitored and tracked.

The DOD’s stated goals of Visible, Accessible, Understandable, Linked, Trustworthy, Interoperable, and Secure can be tracked using assigned metrics, as well as visualized and analyzed using EA’s representational views and reports.

Bringing It All Together

Collaboration facilitates whole-system solutioning
  • Lower the risk of unintended consequences by leveraging the holistic analysis capability enabled by the “single pane of glass” view
  • Avoid redundant and re-work efforts by taking advantage of fully collaborative change management planning
  • Improve the ability to achieve goals efficiently and effectively by accounting for all architectural elements contributions

 

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