6 Best Practices for Data Governance

When looking for data governance best practices, you can learn a lot from others who have worked through the various processes and templates. While each organization is different and you will need to adapt your data governance practices to your process, there is no need to completely reinvent the wheel. When applying an agile development mindset to data governance, start small with a minimum viable deployment, and then iterate and grow from there. This can yield greater long-term benefits and bring the rest of the organization on the journey with you. First, it is important to understand what data governance is and what it can bring to your organization.

What is a data governance framework?

A data governance framework is a collaborative model for managing enterprise data. The framework or system can set soft guidelines or firm boundaries around data creation and manipulation. Often companies assemble a data governance team to ensure proper use of data, data quality, and policy compliance. Executing a data governance framework impacts all parts of your data management process, including architecture analytics and data models. Proper execution makes it easier to make smarter decisions, faster. Once you have a solid understanding of data governance and the impact it can have on your organization, look for opportunities to use templates, models, and best practices that are available on the market. Data governance best practices can be found in software tools, frameworks, libraries, or consultants, and you can look at Tableau Blueprint to understand how Tableau can help you move towards successful implementation. While every organization is different, there are some basic best practices to help guide you when you’re ready to move forward.

Data governance best practices

1. Think with the big picture in mind, but start small

Data governance is a combination of people, process, and technology. To begin building the big picture, start with the people, then build your processes, and finally incorporate your technology. Without the right people, it’s difficult to build the successful processes needed for the technical implementation of data governance. If you identify or hire the right people for your solution, then they will help build your processes and source the technology to get the job done well.

2. Build a business case

Getting buy-in and sponsorship from leaders who will be part of the process is key when building a data governance practice, but buy-in alone won’t fully support the effort and ensure success. Build a strong business case by identifying the benefits and opportunities that data quality will bring to the organization and show the improvements that can be gained, like an increase in revenue, better customer experience, and efficiency. Help everyone involved see and understand both the energy required and the eventual benefits to be successful. Most leaders can be convinced that poor data quality and poor data management is a problem, but data governance plans can fall short if leadership isn’t committed to driving change.

3. Metrics and more metrics

As with any goal, if you cannot measure it, you cannot reach it. When making any change, you should measure the baseline before to justify the results after. Collect those measurements early, and then consistently track each step along the way. You want your metrics to show overall changes over time and serve as checkpoints to ensure the processes are practical and effective.