Data Governance in Agile Projects

ART01_dec15 - imageI’ve come to consider the Agile project delivery methodology to be a great tool for data governance. The iterative approach in Agile has fast feedback loops that focus on what really matters to reach business objectives, providing much needed data priorities for governance.   Agile can be leveraged to identify critical data very early on in projects and it allows for proactive data governance efforts, as opposed to reactions to data issues in traditional (i.e. waterfall) development or testing project phases.

First, let’s agree on what Data Governance could mean in the context of a project, then we’ll consider the specificities within the Agile methodology.

Data Governance and the Project Context

By definition, data governance requires Enterprise vision and leadership. Establishment of enterprise accountabilities, terminology (the Business Glossary), policies, and decision processes around data cannot and should not be determined by projects. The data coherence between organizational silos requires Enterprise leadership.

However, business projects implementing processes that consume or generate data represent a splendid opportunity to bring focus and priorities to data governance efforts.  Because all projects should deliver to a business case, a real and direct link can be established between data governance needs and business objectives of the project.

Projects can connect Enterprise governance efforts to real business priorities by delivering the following elements:

  • Assessment by the business of the criticality of data to the processes that will be affected or implemented by the project
  • Identification of data problems in data sources and business prioritization based on the criticality of data
  • Identification and management of data risks, weighted by data criticality
    • Lack of accountabilities (ownership, stewardship)
    • Missing data terminology; missing definitions
    • Insufficient level of quality
    • Compliance to data policies

In effect, projects are actually identifying Data Governance requirements within their risk management plans, with risks prioritized by criticality of the data and the impact that lack of governance may have on the business processes.

The ability of an organization to act on these data governance requirements is critical.  We can actually define KPIs and measure this ability…but this is the subject of a future blog.

Agile Specificities

There is great value in identifying data risks, and thus Data Governance requirements, as early as possible in the project life cycle. It makes the difference between reacting to data issues during deployment and being proactive in managing risks.

Agile projects aim at defining business outcome in their first iterations. This allows for earlier assessment of the criticality of data, when compared with a waterfall project.   It also means data risks can be mitigated and data governance requirements can be addressed earlier.

The fact that some domains and business terms needed by the project might be identified only in later releases does not matter.  It simply means that new risks around data might be identified later in the project- it implies an agile approach to the evolution of data governance.

For most organizations, embedding data governance in projects is a significant change. Bi-modal data governance (Enterprise + Agile through projects) is as much a change for organisations as it is to change from waterfall project delivery to agile. These organizational changes take time. Agility is required.

Embedding data governance in agile projects is a very effective way of focusing data governance efforts on data that really impacts business outcomes.  Linking data to business value- now that’s the language that brings business people to the Data Governance table.  In fact, the Scaled Agile Framework (SAFe) actually integrates the Enterprise leadership component, needed for Enterprise data governance, at the Portfolio level.

Key Takeaways

  • Data governance in projects is the agile side of data governance. By identifying and managing data risks, projects create a link between Business Value and Data Governance.
  • With its iterative approach, Agile methodology enables early identification of critical data and data risks. The sooner the risks are identified, the greater the chances the governance organization will have time to act before the end of the project.  This can be done by leveraging quick feedback loops as well as the inherent accountabilities in an agile approach to project delivery.



Copyright © 2015 – Prodago

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Mario Cantin

Mario Cantin

Mario Cantin is the Founder & Chief Data Strategist for Prodago. Prodago is a Lean Data Governance company. Mario can be reached at

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