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Data quality and data governance: You can't have one without the other

Think of classic combinations: peanut butter and jelly, summer and beach days, mornings and coffee. Though the components that comprise these pairings do just fine on their own, they are somewhat incomplete without the other. That’s how you should think of data governance and data quality.

Data quality is the underlying foundation that supports business initiatives today. Given the importance of exceptional customer experience as a competitive differentiator, data is truly one of the most important assets for companies today.

There’s not just a one-size-fits-all approach to obtaining and maintaining good data quality. It involves data governance—and taking a look at this picture below—data quality also involves data preparation, data integration, master data management, and the eventual use and analysis of said data.

For the purposes of this blog post, however, we’ll focus on data governance and data quality. Because of their wide-reaching and foundational nature, I believe data governance and data quality to be two of the most impactful practices out there today.

Data governance defined
Data governance means having a system in place that encompasses people, processes, and technology that ensure data meets precise business standards. It means ensuring that data meets internal and external rules and regulations, and allows businesses to take control over their data assets.

For example, data governance is particularly relevant for those in the finance and insurance industries, where not adhering to government regulations can mean hefty fines, and having faulty data can lead to severe repercussions.

Data quality defined
Data quality plays directly into the principles in data governance, though it broadens the view to encompass everything that goes into making data good and fit for purpose.

There are many aspects that go into data quality, but it generally includes the following:
• Accuracy
• Relevancy
• Completeness
• Uniqueness
• Lineage
• Comprehensibility
• Usability
• And so on

What goes into data quality and data governance?
Data funnels into your business through many channels (e.g. websites, call centers, social media platforms) and in many different formats. Think of just how many variations there can be in names (like Katelyn/Kaitlin /Caitlin/Caitlyn etc.) and phone numbers (555.123.4567/555-123-4567/(555) 123-4567). Without the right practices surrounding your data, the margin for error is very high.

What goes into data quality and data governance is ensuring not only that the information you collect is correct, but also that there are processes and people in place to maintain it going forward.

There are technologies and software solutions that perform one-off data cleansing. They often get the job done for the time being, but what happens when you introduce new variables into the company equation? What happens when you acquire a new company with new people, new processes, and new databases? What happens when your organization decides to undergo data and systems modernization efforts? Will one-off solutions still get the job done and done well? Definitely not. That’s why both data governance and data quality are ongoing processes that require consistent attention and upkeep.

Building and instilling good data quality and data governance practices takes work, but good data is always worth investing in.

We can help you establish strong data governance and data quality practices.

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