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data governance

Data Governance: 10 Best Practices for Success

Data governance can differ depending on the organization, the data maturity of the employees, and the amount of data in circulation in the company. Each organization is different, and the data governance strategy must be adapted according to existing data practices.

However, there are some universal steps, and best practices to follow concerning your organization’s data governance.

Data governance basics

Implementing a data governance strategy means better data quality. Data quality is synonymous with cleaner, more understandable data, allowing for better analysis, business decisions, and, therefore, better commercial results. The importance of prioritizing data governance cannot be overstated. There are many different aspects to consider when designing a data governance project.

These elements range from the definition of your goal to the implementation of the strategy and its measurement. Data governance allows you to better manage your current challenges and gives you access to new business opportunities that would not possible with bad data quality.

Here are all of the best practices your organization can follow to unlock the power of your data.

Data governance best practices

#1: Start small

As is often the case, it is not recommended to do everything at once regarding data governance. It is better to target a concrete data use case and complete it to increase the teams’ data maturity and progress step by step. When introducing a data governance strategy to your teams, it is best to start in small, actionable steps.

#2: Set clear and attainable objectives

Set clear, measurable, and specific goals. You can’t improve your strategy for a future goal if you don’t learn from the previous one. Without well-defined objectives, your plan is unlikely to have enough context to truly engage your teams.

Without clear objectives, you won’t see clear improvement. If you don’t know which features need to be defined and how they should be prioritized, then your entire strategy could be at risk. The best way to prevent this from happening is to start with a good plan, one that’s clear about what success looks like for data governance and what steps you’ll take to achieve it.

#3: Define responsibilities and identify roles

Who is responsible for data in the company? Who is responsible for a given data perimeter? Without a clear role, the project will not move forward… or not well.

Identify the different roles and the organization to be put in place for data governance. Data governance is a team effort: collaboration is essential and knowing who is responsible for what is key. There are a few key roles to remember regarding data governance: data admins, data stewards, and data users.

5: Gauge data maturity

When establishing your governance program, it is important to gauge your company’s data maturity. Doing so will help you identify any weaknesses in your data quality, knowledge, or current processes. By gauging your company’s maturity concerning data, you can ensure that your company is ready for a comprehensive governance program. Of course, the path from here is up to you and your team. Re-evaluate your strengths, weaknesses, and needs, and create a plan for how you will proceed with your data management efforts.

In reality, gauging your company’s data maturity isn’t something that can be done in a day or even a week. Instead, try to think about this process as a longer-term goal for your business and focus on making small but strategic improvements along the way.

6: Facilitate the adoption of data governance

While choosing the right tools and technology is important, adoption is just as critical. It doesn’t matter if you have the best technology in the world if your teams are hesitant to use it. According to Deloitte, you should budget just as much time for culture and adoption. Another key step to promoting change is to demonstrate the value of data governance early on and provide detailed context for all teams, not just IT.

7: Define data governance standards

Develop standardized data definitions and policies. When it comes to data, consistency is key.

8: Identify critical data elements

This identification is essential to give data context. With a high volume of data available, it is key to identify and prioritize the most critical data.

9: Optimize communication

Efficient communication and collaboration are essential. Without fluid communication, it will be impossible to fully align, track milestones, and detect problems early on. It is equally important to use a shared language when it comes to data terms and uses, which is where a business glossary comes in handy.

By using a shared language, you can facilitate the efficient sharing of information about your data. Through improved communication and collaboration, you can prevent unnecessary risks to the business’s performance and the company’s reputation.

10: Make progress visible

Ensure that progress and milestones are communicated frequently. Everyone must remain engaged and updated for their data governance project to be successful. Monitoring the project’s progress is vital to ensure problems are addressed early on and that all actors stay involved.

Continuous improvement of your governance model will also help ensure your program can work long-term. Throughout the project, don’t forget to review your processes and adjust and update as necessary.

Conclusion

With an appropriate data strategy, companies can streamline business processes and reap the benefits of analytics. However, it is easier said than done. By taking small, actionable steps and fully defining your company’s objectives and needs, you are more likely to succeed. Another critical element is keeping open communication and ensuring the project is visible to all key players.

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