Governing Data Architecture to Achieve Success

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Click here to learn more about author Tejasvi Addagada.

The challenges of efficiently managing data are significant in today’s in-organic data landscapes. In these landscapes, we can see many legacy data stores and processes that are yet to be discovered along with the data that they produce, distribute and apply. There are many political and adoption barriers that an organization needs to overcome to simplify, appraise these landscapes, better govern data to bring value and reduce risk to the organization. And an enabler of better Governance service operations is to understand the current nuances of data, as it exists in the organization today.

Governing data in today’s world is also about having to naturally “Manage it as a Business Meaning” as rightly put by EDM Council. Most folks feel that Governance activities are a tad over and above regular Data Management. For example, the first intuition that a business analyst gets when a data-mapping artifact needs to be produced is that it would impact the time to market of a critical business change. This is where continuing awareness in organization, while enabling sponsors, data owners, analysts, data stewards and other stakeholders, brings a cultural adoption of Governance. The other way is to standardize this necessary information as organization process assets that bring immediate and cumulative benefits to the organization.

As Data Governance divisions, the need is now stronger to assist organizations make themselves aware of the value that these activities bring forth when embedded tactfully into Business Analysis, Data Analysis & Management, and Architecture disciplines. It is also about rediscovering the importance of producing artifacts like Metadata directories, Data Flow diagrams that have a direct impact on end goal of managing data with excellence. These disciplines of architecture are required to operate with excellence and achieve Data Governance objectives and goals.

Likewise, Governance can be rightly embedded into all phases of Architecture encompassing Business architecture, Information architecture, Process architecture, Systems architecture, and Technology architecture. Unraveling these disciplines informs how data should be Identified, Defined, Modeled, Related, Created, Distributed, Maintained, Applied and Decayed.

What are the direct & indirect benefits we are achieving from unraveling these disciplines?

  1. Increase efficiency of scoping activities performed in Strategy or Enterprise analysis
  2. Reduce effort (person hours) spent on analyzing business requirements and eliciting data requirements
  3. Reduce stakeholder communication overhead with common vocabulary and common understanding
  4. Provides increased awareness and understanding around data in the enterprise for impact analysis
  5. Provides specific standards that impart confidence in the data
  6. Proves ease of sharing inter-operable data across services, systems, applications with less effort
  7. Reduced general and administrative costs, such as cost of IT operations and personnel costs and technical support associated with Inter-operability, data sharing, data analysis.
  8. Reduced Up-front costs, such as the integration of data and training needs.
  9. Reduced future costs, such as maintenance of legacy systems, transformation, and decommissioning of information systems.
  10. Reduce costs associated with maintaining replicated information.

The choice of which specific architecture views need to be developed is a key decision that needs to be made by an Architect in coordination with Data Governance division along with other applicable functions. “While performing needs analysis in requirements & design phase, make sure to generate maximum options to satisfy the business need. Make sure to have the Data Governance council, representative and data risk committee/group participates while generating options.”

The architect has a responsibility to ensure the completeness and usability of the architecture while addressing various stakeholder concerns in the data driven realm. So, should the scope be narrowed down to the Data Architecture? Do you think there is a need to look at data from the view of Business, Application and Technology owners as well?

In the below snapshot, we will be looking at various architecture views that can assist the organization in better governing data. This would be by, better appraising the data landscape and flow of data for interoperability, business functioning, in views that fits the needs of varied stakeholders. Further, not every view needs to be in place to drive a holistic perspective and the selection of views depends on the needs.


Further, I tried to map the Zachman framework with the Togaf related views along with the change life cycle stages where the views can be leveraged or managed to add value either in a regular change like a project, a transformation, or a migration. The views marked in green will bring forth a holistic perspective of data to better understand what, where, how, why, and when data is being realized. All the disciplines of Architecture as described below based on their relevance and context with data are important for an organization.

Business Architecture views – Depicts the flow of information between people (users) and Business Processes

Data Architecture Views – Defines the Meaning of data along with its relationships.

Application Architecture Views – Defines how various software components are developed, integrated while depicting data that is realized by each component and flows between components.

Technology Architecture Views – Defines how technology infrastructure (platforms, tools and applications) works in tandem to enable access and use of data.



In my next blog we will look in detail on integrating Information Architecture including Data and Technology Architecture as a dimension in Data Governance along with Data Quality, Metadata and Risk Management

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