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Agile Data Governance Month – Can Data Governance be Agile?
When Data Governance initiatives fail or stall out, it is not because they aren’t viewed as important. Even with executive and stakeholder buy-in, Data Governance can be seen as too big a lift and might be deprioritized in favor of initiatives with more immediate ROI.
In Part 1 of this four-part series, we learned about the guiding principles of Data Governance, and why organizations should look at Data Governance to unlock the potential of organizational data.
But how can organizations without the appetite for a large Data Governance initiative get started? What if the framework to successfully develop a Data Governance posture is right under your nose?
With Agile Data Governance, organizations can utilize the iterative, collaborative approaches that might already be in place for product development and project management for more effective and transparent Data Governance initiatives.
What is Agile Methodology?
First, a primer in Agile methodology.
When the Agile Manifesto was created in 2001, it transformed software development and project management from rigid, linear planning to a flexible and iterative approach. Agile has grown beyond software development in the years since, with Agile principles adopted by teams and organizations of all sizes across various industries. Today, large technology companies like Google, Microsoft, and Spotify, alongside smaller startups and even non-tech companies use Agile methodologies to deliver products and services more efficiently and effectively. Agile is now considered a mainstream approach, and its principles have also influenced other business practices, including project management and organizational leadership.
The Agile Manifesto is comprised of four key values that serve as the philosophical foundation of Agile methodology:
Individuals and interactions over processes and tools: Agile methodology prioritizes the human element and communication over rigid adherence to tools or processes.
Working software over comprehensive documentation: Agile methodology aims to develop software that works and meets users’ needs, rather than producing extensive documentation.
Customer collaboration over contract negotiation: Collaboration is seen as more beneficial than negotiating fixed contracts, facilitating a better outcome.
Responding to change over following a plan: Agile values adaptability and being able to respond to changes, over sticking strictly to a predefined plan.
The Blueprint for Agile Data Governance
It’s time to start thinking about Data Governance as an agile and iterative process, with an organically growing and maturing Data Governance program. Rather than a rigid set of rules and procedures, applying Agile methodology to data governance initiatives can help make the process more adaptable and effective.
Start with a Vision, and Expand
You can create an initial vision for your data governance program but understand this vision will evolve. An Agile approach focuses on iteratively and incrementally refining this vision. Start with high-priority areas and evolve your strategy as you gain more understanding and encounter new challenges.
Create Cross-Functional Data Governance Teams
Data governance is a cross-departmental effort and requires input from various roles: data stewards, data owners, data users, IT, and business stakeholders. Like Agile development teams, these individuals should work together regularly to ensure a holistic approach to data governance.
Work in Iterations
Break down the data governance program into smaller, manageable objectives. Each iteration should have a clear goal and result in tangible progress toward the overall data governance objectives.
Adopt User Stories
While Agile methodology in software development prioritizes customer success and engagement, Agile Data Governance considers the needs of data users. User stories can be a helpful tool for understanding the needs of different data stakeholders. For example, a user story might be “As a data analyst, I want to have access to clean, consistent sales data so that I can generate accurate reports,” or “As the Director of a business line, I need to be able to cross-reference data in my ERP and CRM.”
Frequent Delivery and Review
At the end of each iteration, deliver some tangible improvement to the organization’s data governance. This could be a new data quality rule, a cleaned dataset, or an improved data governance policy. Then, review progress and adapt plans based on feedback and results. Regularly reassess and adjust your data governance practices based on the results. Reflect on what’s working, what isn’t working, and how you can improve.
Leverage Automation
Similar to Agile’s emphasis on technical excellence and useful design, use automation in your data governance program to improve efficiency and consistency.
Prioritize Transparency and Collaboration
Encourage active participation and open communication among all stakeholders. This includes clear documentation, shared definitions, crowd-sourcing of business definitions and usage associated with a data asset, open discussions about data quality and security, and collaborative decision-making. While Agile Data Governance will always require buy-in from organizational leadership, it also ensures a bottom-up implementation approach that will ensure stickiness beyond the initial implementation.
