Six Steps to Data Governance Implementation

Data governance is an essential part of adopting modern data solutions such as data lakes, as it can help ensure that data is managed properly and securely while optimizing spend. Even after you decide on a governance strategy relevant to your business’s needs, implementing this strategy on the ground can be daunting. In this article, we provide a guided 6-step approach to data governance implementation – getting your governance strategy off the ground and in motion.

1. Define Data Stewards

Data Stewards are responsible for making data-related decisions in an organization, such as defining the structure of the data lake, setting access rules, monitoring usage and even kickstarting the common data model as per the business domains.

Therefore, the first step in setting the data governance strategy in motion is to define who the data stewards are and assign them specific roles and responsibilities to manage different aspects of the data governance process. This can be done by identifying owners for each of the data governance pillars and assigning them the responsibility to further define the necessary roles and responsibilities to manage that pillar.

2. Determine Data Governance Objectives

The second step is to drive clarity on what the objectives are for the data governance strategy. This includes the goals of the governance body, immediate organizational objectives, such as cost optimization or improved data collaboration, as well as specific objectives for each pillar.

Setting clear objectives and KPIs early on helps makes it easier to measure its progress, make tweaks along the way as required and ensure success of the data investments.

3. Build a Data Governance Community of Practice

The Data Governance community of practice should include members from across the organization and represent everyone who is involved in managing or working with data, regardless of their role or position in the organizational chart. A data governance community of practice is most effective when it includes members from multiple cross-functional teams, such as data engineering, data science and analytics, operations, IT security and even legal and other departments.

A community of practice is not built overnight. It is evolves over time and therefore, the data governance strategy should start by building the necessary foundation ensuring there is intake process for new members that want to join and actively participate

4. Adopt a Common Data Model

The first step to setting up a data governance framework is to adopt a common data model that will be used across the organization. This data model should provide the basis for data management and processes such as data quality, security, access control, etc.

5. Set up Automation & Monitoring

The automation & monitoring processes should be incorporated into the data governance framework to ensure that all data is managed in accordance with the framework.

The goal at this stage should be to put the foundational automation and monitoring in place that can be easily enhanced over time as the data governance framework matures.

6. Establish a Feedback Loop

Finally, a good data governance framework must include a feedback loop that allows the data stewards and the governance committee to constantly evaluate and improve the governance framework itself. This helps ensure that governance processes are regularly reviewed and updated to the changing business needs.

A feedback loop can be in the form of periodic meetings to assess the effectiveness of the governance framework, identify areas of improvement. Governance community of practice can also hold open office hours to hear what’s working and what needs improvement directly from the users.


Data governance is a critical component of an effective data management strategy. The steps outlined in this article provide a roadmap for organizations to establish a robust data governance framework.

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