Why and How to Create an Agile Data Governance Policy

Your data is always changing, and your data governance policy needs to change, too. If it can’t, it’s not agile – and if it’s not agile, it won’t work in today’s IT environment. Here’s why and how to create a lean and agile data governance strategy.

Why and How to Create an Agile Data Governance Policy

To understand what agile data governance means, let’s first define the two major terms that are at play in that phrase:

  • Agile is a trending word in the IT world today. It refers to processes and tools that are scalable, flexible and easily adapted to new purposes whenever needs change. Agile is more commonly used in the DevOps world of software delivery than in the data management ecosystem, but the concept applies to data just as much as it does code.
  • Data governance is the set of rules that an organization adopts to define how its data is managed, analyzed and stored. Data governance helps ensure regulatory compliance, prevent data loss and allow businesses to derive the greatest value from the data they generate.

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The Problem with Traditional Data Governance

The data governance idea has been around for years. Most organizations have data governance strategies in place.

The problem with many existing data governance frameworks, however, is that they were not crafted with the agile concept in mind. The data governance concept arose and was embraced before agile became the operative word of most IT organizations.

As a result, the data governance policies that many organizations rely on today are not able to keep pace with the fast-changing nature of data sources, storage solutions and analytics tools.

That’s a problem because inflexible data governance undercuts an organization’s ability to be agile. The organization can’t adopt new data tools or processes without having to overhaul its data governance policy – which is a huge undertaking.

Why and How to Create an Agile Data Governance Policy

Achieving an Agile Data Governance Strategy

The solution to this conundrum is to develop a new generation of data governance policies that jive with the agile needs of modern software organizations.

An agile data governance policy is one that is flexible enough to accommodate a range of different data processing, storage and analytics workflows – including ones that your organization can’t even envision today. You don’t know what your data needs will look like in a month, a year or a decade, or which data tools will become available to you. Your data governance policy should be flexible enough to work with whichever tools and processes you decide to implement, rather than forcing you to cling to outdated ones.

To achieve an agile data governance policy, adhere to the following guidelines:

  • Have a clear focus, but don’t be overly specific. You want to identify clear goals for your data governance processes while at the same time avoid rules that are too rigid. So, for example, your data governance might include a rule that says all data will be analyzed using modern analytics tools, but not name a specific analytics tool. If it names a specific tool, you risk creating problems for yourself if newer, better tools become available and your data governance policy was not written to work with them.
  • Make your development and data teams work together. One major source of delays and inflexibility in data management is a disconnect between software developers and the data team. The goals and toolsets of both groups are constantly changing. To stay in sync with each other, the teams need to communicate effectively. Your data governance rules should encourage this by requiring developers to coordinate their work with your data team, rather than having the teams simply hand work off to one another.
  • Be scalable. It’s hard to predict how the scale of your data needs will change in the future. The trend is toward having more rather than less data to work with, but just how much more is difficult to predict. For this reason, your data governance rules should provide seamless flexibility. They should allow your data management team, storage resources, and analytics environments to expand as needed, whenever needed.
  • Compliance compatibility. Compliance needs and expectations constantly change. Ten years ago, the healthcare industry was terrified of placing data in the cloud because of worries about HIPAA compliance problems. Today that’s no longer the case, as newer cloud hosting and storage options have made the cloud much more HIPAA-friendly. You want the flexibility in your data governance policy to take advantage of changes like these.
  • Have flexible architectures. Just as application architectures change due to new concepts and tools like microservices and containers, the way you organize data storage and analytics resources changes, too. Make sure your data governance policy can adapt to new architectures. Don’t write specific architectural requirements into your governance rules because what is a forward-thinking architecture today may become outdated tomorrow.

By following these principles, you can create a data governance policy that is truly agile and equipped to accommodate your ever-changing needs.

Staying agile can help you navigate the continuing evolution of data management. Discover The New Rules for Your Data Landscape with Syncsort’s latest eBook!

Christopher Tozzi

Authored by Christopher Tozzi

Christopher Tozzi has written about emerging technologies for a decade. His latest book, For Fun and Profit: A History of the Free and Open Source Software Revolution, is forthcoming with MIT Press in July 2017.

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