Delivering a robust and scalable data governance framework that supports enterprise data analytics requires a disciplined, holistic approach. Often, enterprise-wide data governance programs are designed in a siloed manner, with little to no consideration for the broader organizational context. This approach can yield short-lived solutions that do not meet the needs of their intended users or are not conducive to successful execution of the overarching business strategy.

We are in a new wave of data governance that is gaining momentum: Agile data governance. This is oriented to improve effectiveness through data literacy of business users who are much closer to the data. It is the antithesis of traditional data governance, which creates a costly and time-consuming process, with rigid policies that do not often work well beyond certain regulatory requirements and the rewards are quite less significant when compared to the efforts involved.

Agile data governance is supported by efficient and user-friendly tools that deliver knowledge about the data to the data users, which differ from the tools supporting traditional governance in that they have machine learning and/or artificial intelligence features built into them that enables automating key aspects that a traditional data governance strives to achieve, such as trustworthy self-serve analytics, data protection and anonymization, data cleansing and prepping.  

As agile data governance becomes more popular, it will likely be adopted throughout organizations. Allowing for different roles to be involved, new processes, and new technology will benefit those using agile data governance with increased capabilities.

Guiding principles for Agile Data Governance:

  1. Data governance must be prioritized in the context of business objectives
  2. Agile data governance must balance both offensive and defensive use cases of data
  3. Implementation of a Minimum Viable Capability that is scalable and aligned with the organization’s data strategy
  4. Agile data governance should follow a non-invasive approach leveraging existing structure as much as possible
  5. Data governance and agile development teams must collaborate and understand each other’s philosophy. Agile teams should understand that data consumption much adhere to data governance policy, whereas Data Governance team must understand the concept of rapid incremental development
  6. Senior leaders should understand and actively promote the importance of data governance by not just advocating but also providing adequate resources