Posted on: October 29, 2020 | 2 min read

The Scary Truth About Your Data Quality

The scary truth about your data quality

Your Halloween costume isn’t the only thing that is spooky this season. Learning that your data is far from being consumable may be a horrifying truth to your upcoming analytics initiatives.

It's the 80/20 issue. Most professionals have heard of it; many probably identify and struggle with it. "80% of data analyst time is spent simply, finding, cleansing, and organizing data.” How great would it be if an organization could give back those 32 hours a week and allow their analysts to dig deeper into data to provide meaningful insights to their departments?

Time and time again, we hear that organizations in the United States are dealing with failing governance initiatives. What are the blockers that are causing these initiatives to fail and their data workers to put endless hours into data upkeep? One blocker that we repeatedly see is that these organizations are struggling with data quality. In a video interview, our experts discuss how data quality can be used to gain an edge.

Data Quality Misconceptions

One of the misconceptions around data quality is that “data governance and data quality only affect people in IT.” This couldn’t be farther from the truth. Data Quality affects anybody who interacts with that data, and with the sheer amount of data that organizations consume on a day to day basis, this understanding is imperative. To enable data quality, an organization needs to understand that standards and procedures need to be set cross-departmentally.

Addressing Poor Data Quality

Data issues can come about in several ways. Data quality can arise as soon as a human interacts with an application. Some examples of how poor data quality could arise are:

  • Regulatory issues
  • Mergers or acquisitions,
  • Downsizing headcount
  • Staff relocations
  • Product launches
  • Product changes
  • System implementations

Most of these changes get handed to IT, which naturally can result in a lack of quality. With the competing priorities that organizations traditionally have, data governance and data quality often are put on the back burner.

Combatting Poor Data Quality

The fastest way to resolve poor data quality issues is by getting buy-in.This comes from providing your organization with an understanding of why data quality is important; this can be referred to as the WIIFM (what’s in it for me) effect. With stern maintenance of your data, the accuracy of your insights improves, which leads to better decision making and increased ROI. 

Get Started

Struggling with poor data quality in your organization? Check out our blog on How to Overcome 9 Common Challenges of Data Governance, or learn more by visiting our solution page


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