5 BIGGEST DATA GOVERNANCE MISTAKES AND HOW TO FIX THEM

Knowing the most common data governance mistakes is the first step to avoiding them. Still, before we address them, it’s paramount to know why efficient data governance is steadily becoming one of the greatest assets for an organization in the dynamic business landscape we currently live in. To start with, the simple possibility of being able to own and control all the management of data is something that everyone should strive for, but it is when we understand the sheer amount of benefits a well-established data governance framework could bring that it raises the stakes to a whole new level. So, you can learn more about these benefits in another article we wrote that leads you through the steps to jump-start effective data governance.

IS IT ENOUGH TO IMPLEMENT A DATA GOVERNANCE STRATEGY?

It might give you a little nudge over some organizations that don’t – but simply put, no.

To be able to turn data governance into a greater asset, it is crucial to ensure that your data governance strategy is well planned, adequate for your organization, involves all relevant stakeholders, and is thoroughly understood and embraced by everyone. As regulation and requirements regarding the usage or management of data are evolving swiftly, organizations should take a step back and turn this into an opportunity for developing and designing a well-thought-out strategy instead of addressing all the regulator’s demands without any critical thinking.

As with all systemic changes, the implementation of such a framework will be prone to errors and mistakes. Some you will only discover once you roll up your sleeves and start, but in this article, we will call your attention to some of the top data governance mistakes and ideally enable your organization to avoid them.

5 BIGGEST DATA GOVERNANCE MISTAKES

• Creating a “Data Governance Project Team”
  

  Before thinking about your strategy, the first thing to understand is that data governance should not be treated as a standalone project but rather as a transversal program that will eventually reach all stakeholders. A data governance program should include all processes, available technologies, structures inside the organization, policies, regulations, and rules that delineate how the organization treats and manages its data. Developing a framework is crucial and should always be well documented and competently communicated throughout all different areas and branches. Furthermore, this framework should become an essential part of your organization to ensure its implementation is sustained in the long term.
  It’s then extremely important that the entire organization is engaged in and committed to these changes since, as there are numerous stakeholders involved, the number of possible bottlenecks is very high. Involving all people in defining roles and responsibilities and ensuring they understand the program’s relevance will undoubtedly strengthen their pledge to it.
  

• Having inconsistent data

  When different structures inside the organization tend to use a different language to process and treat their data, this leads to overall inconsistent data. This clutter and inaccuracy will obviously interfere with a data governance program, so there is a great need for uniformization and clear KPIs so as not to have misleading reports or departments working against one another, which will hinder even the initial implementation of the plan. 
  

• Unclear objectives
  

  The lack of clear objectives or a misguided focus on the governance objectives unfailingly leads to wasted resources and a delay in the positive impact data governance can have. Usually, these objectives tend to be very general, and there are failures in the ability to measure if or when they are achieved. In order to enhance its effectiveness, data governance should be initiated with a clear objective in mind aligned with the organization’s strategy, preferably linked to business value and risk mitigation. Furthermore, these objectives should be defined in coordination with the entirety of the stakeholders and efficiently shared across the organization to ensure everyone agrees. Moreover, these objectives must be actively monitored and measured with a clear definition of benchmarks and KPIs at different levels to repeatedly demonstrate the value added and, consequently, build confidence and keep people engaged.
  

• Insufficient tools or skills
  

  A recurring mistake with implementing a data governance plan is the desire and pressure to initiate the process before assessing and obtaining all the fundamental tools, resources, and skills for the implementation. Most of the time, the lack of a centralized procedure leads to different organizational structures using or creating their own tools and consequently dealing with data in different ways, which causes erratic outputs. Uniform and adequate tools and platforms should be provided prior to implementing a program. This way, it can be ensured that all teams are following and maintaining organizational standards.
  

• Too much focus on tools
  

  As stated above, IT tools are very helpful in developing and implementing a data governance program. However, even when the best and most adequate tools are used, they are not a solution by themselves. The first priority for implementing such a program is that it should rely on the 3Ps: people, processes, and platforms. The structures, processes, and responsibilities should be well established before providing them with any resources. As an example, data ownership within an organization must be thoroughly identified and detailed (i.e., what data belongs to who and who is responsible for what data) prior to any commitment to a tool or software. The focus should then be placed on the processes since they primarily involve several organization-wide decisions that no tool can make.

WHY IS IT IMPORTANT TO AVOID THESE MISTAKES?