Businesses rely on huge data systems to drive both long-term decisions and real-time operations. To manage them successfully, data must be handled consistently and intentionally across the enterprise. 

Data governance provides this vital framework.

What is data governance?

Data governance is a set of policies and practices that guide the use of data across the enterprise including its availability, quality, and security.

Put more simply, data governance is exactly what one might expect: the process of governing your enterprise’s data. It’s the highest level of a business-driven data strategy that informs your data architecture, pipelines, storage, processing, and more. Successful data governance creates a system that controls the consistency and quality of data and ensures that it’s used correctly [1].

Data governance helps you:

  • Meet regulatory and business requirements. Building legal and industry requirements into your framework ensures your business will stay compliant.
  • Maintain consistent, high-quality data. All data records adhere to the same standards and don’t have missing components. They match across different platforms.
  • Avoid data errors. Clearly defined procedures make it easier to flag and fix errors. 
  • Eliminate data silos. Data is linked across your different platforms, reducing redundancy and improving accuracy.
  • Lower management costs. A streamlined, clearly defined approach keeps ongoing operation and maintenance simple. 

Why is data governance important?

Data is an increasingly important element in running a successful business, from the data analytics that power decision making to the real-time pipelines that keep operations running. Much of this data is private, so controlling access is critical. Therefore, the standards, goals, and practices surrounding data are a vital component of your larger business strategy. 

Ungoverned data on an enterprise scale can descend into chaos. Inconsistencies and errors may become common and difficult to resolve. Analytical results will be less reliable, and real-time monitoring of operations can get hung up. Sensitive data can leak out — for example, in public companies, performance data can get to the market before it’s reported. 

In addition, organizations face more data privacy regulations than ever before — for example, the California Consumer Privacy Act and the EU’s General Data Protection Regulation. Without data governance to meet these standards, staying legally compliant will be impossible.

Creating a data governance framework

The form that your data governance framework takes depends on your business vision, stakeholders, and pre-existing data practices and infrastructure. 

Regardless, it should begin with these vital components:

  • The team. Create a leadership committee with stakeholders from different groups. Data governance is a collaborative effort between business leaders and those who are more directly involved with your data — engineers, analysts, devops and/or IT professionals. 

Additionally, designate a group of data stewards: those who are responsible for the day-to-day implementation of rules and standards.

  • A mission statement. Focus on the business outcomes you want your data to drive. What is your large-scale vision for your data? Why is it essential from a business standpoint? 
  • Goals. Create a set of actionable, measurable goals that tie into the mission. They should be realistic given your budget, available technology, and personnel. Include both long and short-term goals.
  • Standards, definitions, and rules. The nitty-gritty details that will guide the daily use and management of data. Usually, these take the form of a detailed written document.
  • Adaptable process for growth. A healthy business will routinely update their data products.  To allow for this while safeguarding your business, you’ll need to develop a flexible process which allows you to review and approve new types of data products while avoiding mistakes.

You can find a more detailed outline of data governance frameworks in this article from Profisee.

Once your framework is outlined on paper, the next challenge is implementing it across your enterprise. You’ll need to consider data architecture, pipelines, storage, and analytics. 

First and foremost, you’ll almost certainly be aiming for efficiency and consistency. To that end, you should focus on unifying your data infrastructure. 

It’s common for enterprises in the early stages of data governance to have data sprawled across many systems managed by different teams.  Unification could be a stretch goal at this phase and instead you may wish to simply take an inventory of data products.  Sometimes even doing just that may be challenging from both an organizational and technical standpoint — but one that must be addressed early.

We designed Estuary Flow to unify your data around a single source of truth, so you can design a data pipeline that realizes your data governance vision without getting bogged down in the technicalities. This way, you can shift your focus to meeting data-driven business goals quicker.

Regardless of the solutions you choose, keep in mind that data governance is not a one-and-done project, but an ongoing process. It’s important to allow room for flexibility. Your stakeholders should continue to collaborate and refine your approach over time as you learn what works best for your use-case.

Sources

[1] https://searchdatamanagement.techtarget.com/definition/data-governance

[2] https://profisee.com/data-governance-what-why-how-who/

[3] https://www.putitforward.com/successful-data-governance-in-snowflake

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