Data governance refers to the policies, ownership models, and tools that ensure data is accurate, secure, and usable throughout its lifecycle.  

Done well, it delivers: 

• Clear definitions for shared metrics 
• Faster, more confident decision-making 
• Better protection of sensitive data 
• Simpler compliance processes 

When definitions vary across departments, you can’t trust your data. And the longer it goes unaddressed, the bigger the impact – it snowballs. 

Example from our practice: 

One of our clients had the same KPIs being used by different departments, but each department had its own definition and calculation method. This resulted in the same KPI name showing different values across reports, which caused confusion at the management level. The problem arose because the departments didn’t align on the definitions, even though both values were technically correct for their specific contexts. 

By implementing data governance, we helped the client define and document top-level KPIs for the company and each department. With a clear, unified understanding of what each KPI means and how it should be calculated, the client was able to ensure consistent reporting and eliminate confusion. 

Takeaway: Start with business-critical KPIs. Define their formulas, source systems, and owners. Publish and maintain them centrally. 

Laws like GDPR require companies to know where their data is, how it’s used, and who has access. 

Takeaway: Map your data lifecycle and identify high-risk data early. Automate documentation where possible.

Data security is a growing concern. With proper governance, sensitive data is protected through role-based access controls, preventing unauthorized access and reducing the risk of data leaks. 

Takeaway: Implement role-based permissions to ensure that only authorised personnel have access to sensitive data, reducing the chance of breaches. 

Data governance reduces the time spent cleaning and managing data. Employees can trust the data they use, as it is consistent and accurate. This not only improves individual productivity but also streamlines collaboration between departments.  

Example from our practice: 

One of our clients faced challenges with managing multiple reports tailored to different departments. Each department needed similar data but with slight variations depending on user roles. This led to the creation and maintenance of multiple reports, each requiring updates and corrections, causing significant overhead. By implementing row-level security (RLS) and column-level security (CLS) within the data platform and the dashboarding tool, we were able to consolidate these reports into a single dynamic report. Now, each department can access the same report, but the data changes depending on the user’s role and permissions. This eliminated the need for redundant report creation, significantly reducing maintenance time while improving operational efficiency. 

Takeaway: Take take advantage of RLS and CLS to manage access control at a granular level. This allows you to maintain fewer reports while ensuring users only see the data relevant to them, reducing the risk of errors and improving efficiency across your business. 

Inconsistent data stalls decision-making. With strong governance, executives don’t have to question whether numbers are reliable. 

Reliable data is also critical for predictive models and AI. Without consistent inputs, your models will generate noise instead of insight. 

Takeaway: Build trust in your data first – before building advanced analytics or AI models. 

As your business grows, so does the amount of data you manage. Data governance ensures that data remains structured and scalable, supporting business expansion and digital transformation initiatives such as cloud migration. 

Takeaway: Implementing data governance helps businesses scale by providing a solid foundation to manage increasing data complexity and future growth. 

Optimising the use of data can be a competitive edge. With data governance, businesses can reduce costs and even monetise their data. For instance, when your data is clean and reliable, you can: 

• Build better customer profiles 
• Sell anonymised insights (in compliant ways) 
• Create more personalised products 

Takeaway: Use governance to drive innovation, improve customer experiences, and create new revenue streams by using insights gained through proper governance. 

Here is step-by-step process that will lead you to successful implementation of data governance framework: 

1. Set your goals – Is it compliance? Operational efficiency? Trust in reporting? 
2. Assign ownership – Designate data stewards and KPI owners. 
3. Define policies – For naming, access, retention, and documentation. 
4. Choose tools wisely – Use platforms with governance baked in (e.g. Azure, AWS, Databricks). 
5. Train the org – Embed governance into onboarding and project delivery. 
6. Monitor and adapt – Treat governance as a living system – not a one-off project. 

The benefits of data governance are tangible: cleaner data, faster reporting, reduced risk, and scalable infrastructure. 

Whether you’re preparing for an IPO, preparing to new regulation, or tired of slow, error-prone reports – strong governance can help.