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Data Governance: Best Practices & Challenges

  • Business Analysis
  • DevOps

21 December 2022

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It’s important to rely on data that hasn’t been tampered with. Control of this dataflow, known as data governance, is a common practice used by many organizations across the world as they deal with security breaches and business continuity.

Optimization of these processes is key, which is why it’s done by a myriad of professionals that need to meld together in order to ensure that the correct policies are implemented. Tasks like breaking down data silos, and harmonizing data packets into one, are necessary steps if one wants to turn Data Governance operations into an asset.

In essence, we can list three key steps that successfully implement Data Governance measures into the system:

  1. Identify the existing data assets and any informal governance processes.
  2. Increase the data literacy levels of end-users.
  3. Delimitate the success ratings for the program.

In short, these three steps are part of setting up an initial framework. Once this is done – the structure that will hold other projects together – we can set up the tasks of our governance team, along with other more complex terms like data cataloging and policies.

There are various ways to handle these practices. One of them, Proactive Data Governance, refers to a more aggressive approach that tries to scale these operations for Machine Learning Experiments. All in all, there are various forms of handling Data Governance. We believe that the following tips might be of use to those interested in reaping the benefits of Data Governance practices.

Best Practices for Data Governance

There are many forms of achieving the same results. Nevertheless, these are some of the most recommended steps:

  1. Set up clear, achievable goals. With clear parameters, it’s much simpler to establish a framework.
  2. Identify the business ownership, even if starting small. Without it, your framework will not succeed.
  3. Define the operating model, and define standardized data definitions.
  4. Identify critical data elements and focus on them.
  5. Define the control measurements behind your project, and deploy them when appropriate.
  6. Always communicate your goals and objectives to your other team members.

The one thing most people forget is that Data Governance is much more than a simple, tech-focused operation. It is, in fact, very focused on the people. It needs to be connected to your employees’ workflows, so as to ensure perfect and harmonious solutions to each of your problems. In the end, there should be four tenets to keep in mind when developing the game plan:

  1. A focus on business value and organizational outcomes.
  2. Transparency in all of the decision-making, so as to make sure they’re integrated into the entire workflow.
  3. Risk management and data security as the core components of a proactive data governance policy.
  4. A collaborative culture that encourages broad participation.

There are various points of improvement for data governance projects. Day by day, these initiatives are transforming the landscape of business, leading to specialized organizations with a slight competitive edge due to their handling of data policies. Of course, even if one considers the above in their pursuit of a perfect governance policy, some challenges will eventually arrive.

The Challenges

Agreeing to common definitions, and creating a good dispute resolution process – Most challenges for data governance appear at the onset of a project. Some of them include:

  • Demonstrating the Business Value: Investors need documentation and hard numbers. Without the necessary documents to back you up, getting approval for any governance project can be tricky.
  • Supporting Self-Service Analytics: Having data in the hands of more users within our organizations. This is one of the main shifts happening over the past decade. Business executives, data scientists, and business analysts need to be responsible for any misuse of data.
  • Big Data Governance: While previously we dealt only with structured data packets, nowadays we deal with the hybrid clash between structured, unstructured, and semistructured data packets. Plus, the majority of this data is stored in raw form in data lakes. It’s easy to see how data governance has a lot more on its plate lately.

The future of data governance is complex, to say the least. Consumers become more aware of their privacy rights, while proactive developers rush to create more stable and more secure programs. Leaders must not only learn the importance of deriving value from these data packets, but also the numerous factors that make data trustworthy, such as using metadata for context as well as proactive monitoring activities.

We’ve always ensured that executives reach the correct decision when applying delicate technology. If you have any further doubts regarding methods, technological trends, and strategies, contact us directly, and let us help you make your vision a reality.

  • data governance
  • data lakes

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