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What to Consider When Implementing Data Governance?


Mar 1, 2022 - 5 minute read

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Rafał Imielski Content Marketing Specialist

He has two years’ experience in copywriting, translation and proofreading. His goal is to help people communicate in a concise and understandable way. Rafał is an archaeology graduate who’s fascinated by both prehistoric and modern technologies. 

See all Rafał's posts

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Becoming data-driven is a must for modern enterprises in today’s world. Organisations from all industries know it and strive to leverage data in their efforts to accomplish business goals. This reality has presented new challenges to business, such as ensuring controlled access to data, as well as its quality and consistency across the entire organisation. Implementing data governance processes can solve these issues.

The Impact Data Governance Has On Your Business

Many people associate data governance with dealing with the complex legal issues surrounding the use of data. It’s partially correct — strong data governance helps organisations remain compliant with the law and industry standards. As data continues to play an increasingly important role in business, the way it can be used and processed becomes more regulated. Legislations such as GDPR are very strict in defining what you can and cannot do with data, and failing to comply may result in receiving serious fines. Data governance procedures can help ensure that all the required laws and standards are being followed in every area of your organisation.

However, there are far more benefits of data governance than just compliance with external regulations. In fact, a consistent approach to data can improve your trust and understanding of your data, create controlled democratisation of data access and support citizen development, as well as the efficiency of the way your organisation uses data. This in turn can contribute to the effectiveness of your business in areas such as time-to-market, scalability, security, response to change, cost-efficiency and more.

Implementing Successful Data Governance

If you want your organisation to truly benefit from its data, you’ll have to implement the data processes with a well-thought-out approach that’s aligned with the specific needs of your business.

Which data can be openly accessed by the employees, and which should require asking for permission? How flexible does your approach need to be in order to satisfy the needs of all departments without completely disrupting their work? Which people in your organisation are most fit to take the responsibilities of data stewards? These are just some examples of the question you will have to answer at the beginning of your journey. To do so, you’ll need both strong data expertise and a detailed understanding of your company’s business goals and internal processes.

Common Pitfalls of Data Governance Efforts

According to Gartner, organisations often make these five mistakes while trying to implement data governance in a suboptimal, inconsiderate way.

  1. Perceived disconnection between data governance and business outcomes. While high management and business leaders are generally in favour of implementing data governance, they rarely want to be engaged directly, as they perceive data as something associated solely with the IT domain. Building the awareness of the business benefits brought by efficient processes and high-quality data should pull the management in and make them more interested in taking an active role in ensuring data governance.
  2. Lack of consistency in organisational data processes and policies. Without a uniform approach coming from the very top of the organisation or an established data governing body, multiple approaches to data governance will organically emerge across the company. The necessary processes will be adopted selectively, or not at all, and you’ll see significant differences in the approach to data governance in different departments or projects.
  3. Inconsistent nomenclature and definitions across the organisation. While the technical teams often talk about data in language that’s taken directly from Data Science, business users do not. Their interest and use cases are narrower and much more specific. As a result, they often have a different nomenclature for data-related items and processes, which makes it difficult to aggregate data and create an alignment between IT and business.
  4. Data treated differently across projects. The approach to data itself often varies between projects and departments within the organisation. They have different needs, so they implement different processes. As a result, creating a data governance policy that’s feasible for all areas of the company can be a challenge.
  5. Difficulty in sustaining the data governance efforts. Data quality, consistency and accuracy are relatively abstract goals which can be difficult to define and track. Companies often start the process with enthusiasm, but as they fail to find measurable objectives, they quickly start losing steam. Creating a long-term roadmap with tangible goals is often necessary to ensure the continuity of the data governance implementation.

Making sure that you don’t fall into these traps is crucial for your data governance efforts to yield significant results.

Crucial Success Factors

You will need a sound plan for your data governance to be truly effective. You can create it by identifying the crucial areas and aligning your approach to the specifics of your organisation. Gartner points at 9 key success factors.

  1. Start with the most important business outcomes. Make sure that business stakeholders understand how data governance contributes to achieving their goals. Begin by focusing on a few crucial activities and you can widen the scope later.
  2. Create a system with multiple degrees of governance, depending on the asset category. You don’t have to treat all data with the same diligence. Concentrate your efforts on the business-critical data assets that are widely used and can produce the most business value.
  3. Build a data governance body and establish a framework for how it should interact with all teams. This top-down approach can introduce some consistency in governance processes and data treatment across the company.
  4. Pick the right data stewardship model. Consider your business characteristics and workload, and decide if you want to have data stewards designated to subject areas, functions, business processes, systems, or projects. You will also need to pick the right people to assume these responsibilities.
  5. Involve business stakeholders in the implementation of data governance processes. This is the only way to ensure the long-term business focus of your efforts.
  6. Talk to your data users, identify similar groups and target them when creating consistent data processes, standards and definitions.
  7. Take every opportunity you can get to facilitate data standardisation. Any big and impactful change in organisational structure or personnel can be the right moment to implement stronger data governance processes.
  8. Focus on data quality and how it affects business outcomes. Highlight the risks associated with insufficient data quality and analyse them from various user perspectives. Focus on detecting the DQ issues early (shift-left). Presenting the impact of potential improvements can facilitate organisational buy-in and ensure the right focus.
  9. Track your progress. Create a maturity roadmap with measurable goals and milestones to monitor how well you’re doing with your data governance efforts.


Successful implementation of data governance is a complex and multifaceted process. Planning and executing on all the key areas can be a difficult challenge — especially for an organisation that has little in-house data expertise and is at an early stage of becoming data-driven. As I mentioned earlier, the combination of deep knowledge of your business and proficiency with data is necessary. Consider engaging an experienced Data & Analytics partner to help you map out your processes and tailor a perfect plan for your organisation’s needs

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Rafał Imielski Content Marketing Specialist

He has two years’ experience in copywriting, translation and proofreading. His goal is to help people communicate in a concise and understandable way. Rafał is an archaeology graduate who’s fascinated by both prehistoric and modern technologies. 

See all Rafał's posts

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