When it is time to develop data governance policies the first thing to consider is how the team views data governance. In this post, we will look at various data governance frameworks and principles to keep in mind when employing a data governance framework.

Top-Down

The top-down framework involves a small group of data providers. These data providers serve as gatekeepers for data that is used in the institution. Whatever data is used is controlled centrally in this framework.

One obvious benefit of this approach is that with a small group of people in charge, decision-making should be fast and relatively efficient. In addition, if something does go wrong it should be easy to trace the source of the problem. However, a top-down approach only works in situations that have small amounts of data or end users. When the amount of data becomes too large the small team will struggle to support users which indicates that this approach is hard to scale. Lastly, people may resent having to abide by rules that are handed down from above.

Bottom-Up

The bottom-up approach to data governance is the mirror opposite of the top-down approach. Where top-down involves a handful of decision-makers bottom-up focus is on a democratic style of data leadership. Bottom-up is scaleable due to everyone being involved in the process while top-down does not scale well. Generally, controls and restrictions on data are put in place after the raw data is shared rather than before when the bottom-up approach is used.

Like all approaches to data governance, there are concerns with the bottom-up approach. For example, it becomes harder to control the data when people are allowed to use raw data that has not been prepared for use. In addition, because of the democratic nature of the bottom-up approach, there is also an increased risk of security concerns because of the increased freedom people have.

Collaborative

The collaborative approach is a mix of top-down and bottom-up ideas on data governance. This approach is flexible and balanced while placing an emphasis on collaboration. The collaboration can be among stakeholders or between the gatekeepers and the users of data.

One main concern with this approach is that it can become messy and difficult to execute if principles and goals are not clearly defined. There it is important to spend a large amount of time in planning when choosing this approach.

Principles

Regardless of which framework you pick when beginning data governance. There are also several terms you need to be familiar with to help you be successful. For example, integrity involves maintaining open lines of communication and the sharing of problems so that an atmosphere of trust is maintained or developed.

It is also important to determine ownership for the purpose of governance and decision-making. Determining ownership also helps to find gaps in accountability and responsibility for data.

Leaders in data governance must also be aware of change and risk management. Change management is tools and process for communicating new strategies and policies related to data governance. Change management helps with ensuring a smooth transition from one state of equilibrium to another. Risk management is tools related to auditing and developing interventions for non-compliance.

A final concept to be aware of is strategic alignment. The goals and purpose of data governance must align with the goals of the organization that data governance is supporting. For example, a school will have a strict stance on protecting student privacy. Therefore, data governance needs to reflect this and support strict privacy policies

Conclusion

Frameworks provide a foundation on which your team can shape their policies for data governance. Each framework has its strengths and weaknesses but the point is to be aware of the basic ways that you can at least begin the process of forming policies and strategies for governing data at an organization.