As companies increasingly rely on data to drive their organizations and bring insights to more and more pockets of their business, it’s critical they implement a data governance process in tandem.
Data governance when done effectively documents clearly defines the rules and regulations surrounding data access and usage. In addition, a data governance process can help to improve data quality, which in turn increases the value of data initiatives at large. Last, data governance is an essential element in adhering to privacy, compliance, and security protocols by ensuring individuals only have access to the data they should.
Despite the vital role data governance plays in business today, many organizations still struggle with implementing a program at scale. There are several steps that you can take to implement a data governance process in your organization. In this post, we will outline the key steps involved in this process.
Step 1 : Define the goals and objectives of your data governance initiative
Before embarking on a data governance initiative, it is important to have a clear understanding of the goals and objectives you hope to achieve. Do you want to improve data quality? Increase transparency and accountability? Improve decision-making? These goals must serve as the foundation from the broader project, and provide the structure for step 2.
Step 2: Identify the stakeholders who will be involved in the initiative
Once your goals have been outlined, you need to bring in the cross functional team who will be essential to implementation. Data governance is not something that can be done in isolation – it requires buy-in and involvement from a range of stakeholders. Who needs to be involved in your initiative? For some organizations, they may be your data team. For others, they may be your IT department or individuals in your security department.
Step 3: Develop a framework for how data will be managed and governed
Once you have defined the goals and objectives of your data governance initiative, and identified the stakeholders who will be involved, you need to develop a framework for how data will be managed and governed. This should include processes and controls for things like data quality, security, access, and retention.
Step 4: Implement processes and controls to support the data governance framework
After developing a framework for how data will be governed, it is time to put it into action. This includes implementing processes and controls to enforce the framework, such as data quality assurance processes or access control measures. If you’re looking to truly capitalize on the value of your data and extend it to multiple parts of your organization, it’s important to ensure the tools in your data stack give you the ability to manage these down to the finest level, at every row of your data, for every user.
Step 5: Monitor and evaluate the effectiveness of the data governance initiative
Data governance is not something you set and forget. It is an ongoing process that must evolve in parallel with the evolving use of data in your business. That’s why it’s absolutely critical to monitor and evaluate its effectiveness on an ongoing basis. This will help you to make sure that your initiative is achieving its goals and objectives, and identify any areas where improvements could be made.
Step 6: Communicate the results of the data governance initiative to stakeholders
Finally, once you have implemented and evaluated your data governance initiative, it is important to communicate the results to all stakeholders. This will help to ensure that everyone understands the benefits of the initiative, and buy-in for future projects.
Empower everyone in your organization
If you’re looking for a way to get started with data governance in your organization, we recommend following these six steps. And if you want to see how you can deliver self-service analytics at scale without sacrificing data governance, sign up for a free trial today. With ThoughtSpot, you can empower anyone in your organization to easily find and act on insights hidden in your data in a safe, secure, and compliant manner.
