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.

4. Communicate early and often

No matter where you and your organization are in the data governance program and processes, it is essential to communicate. Consistent and effective communication is critical to show the impact of the program, celebrate wins, and honestly acknowledge setbacks. Create and update a defined list of stakeholders within your organization and make sure communications are easy to access and easy to digest. This will make sure the right people know what they need to know while avoiding surprises and socializing progress.

5. Account for the fact data governance is a marathon, not a sprint

There is no finish line to good data governance; you typically won’t assemble a team to launch a project and then just cross your fingers. When implementing a data governance program, make sure to present it as a long-term investment, not a one-off project.

A project has a start and end date, and big flashy project names and launches may spark interest. However, data governance is a continuous, iterative process consisting of many sub-projects and milestones. Start with small pilots and bring the learnings from these projects into the company to inform lager and more comprehensive initiatives.

Data governance programs can run for years, but individual projects typically should not last more than three months. Build smaller projects into the long-term data governance strategy to weave more fundamental change into your organization.

6. Identify related roles and responsibilities

Data governance requires teamwork with deliverables from all your departments. Clearly defined roles are essential to every data governance program, and it is important to assign levels of ownership across your organization. Determining who has authority and responsibility will help socialize your data governance program and establish an intelligent structure to tackle data programs as one powerful team.

Data governance roles vary slightly between organizations, but the common roles might include:

  • Data governance council (steering committee/strategic level): A data governance council is a governing body responsible for the strategic guidance of the data governance program, prioritization for the projects and initiatives, and approval of organization-wide data policies and standards.
  • Data governance board (tactical level): A data governance board is a group of people that develops an organization's policies and practices to treat data as a strategic asset.
  • Data managers: A data manager creates database systems that meet an organization's needs for the data they plan to gather or have already gathered.
  • Data owners: A data owner is an individual who is accountable for a data asset.
  • Data stewards: A data steward is responsible for utilizing your data governance processes to ensure the quality of data elements, including content and metadata.
  • Data users: Data users are team members with direct responsibility for entering and using data as part of their daily tasks. They may directly access and investigate integrated datasets at the unit record level for statistical and research purposes.

Ultimately, data governance is about people, processes, and technology. A successful program results in a clear understanding of where data comes from and who owns what. It also results in known processes to follow when data changes are required. Take the time to understand the value of the people you choose to manage the process and operate within the technology. Together, these people will provide data you can depend on, use for strategic decision-making, and drive your organization forward. Learn more about governed self-service analytics at scale.