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How much is poor Data Quality costing you?

We have already tackled the definition of Data Quality in our post “What is Data Quality? And why should you be thinking about it?“. So, this time around, we decided to have a closer look at Data Quality figures and trends across the globe. Scouring the web, we have collected what we consider to be some of the most interesting findings when it comes to quality – unfortunately largely connected to the lack thereof.  Keep reading to find out more about the issue at hand – and how to solve it!

Let’s take a closer look at the numbers

Did you know that the average organization is losing $13.5 million per year as a result of poor Data Quality? (Gartner, 2015) On top of this, 25% of companies in the U.S. do not trust that their data is accurate (Experian, 2013) and, from a more role-specific perspective, 84% of CEOs worry about the quality of the data within their organizations. (KPMG, 2016)

As if this wasn’t enough to highlight the problem, a study by Deloitte (2014) showed that companies believe that barriers to using data effectively are most commonly related to a lack of quality (67% of the time). To break this down further, this means that if organizations managed to hone in on an increased level of quality, 67% of their issues when it comes to using data effectively would be solved.

However, at the moment, there is a shockingly low adoption rate when it comes to integrating strategies to promote and ensure high-quality data. As is depicted further through the pie-chart above, only 18% of organizations around the world have an optimized strategy in place with a dire 17% of companies currently operating without one in place. (Experian, 2017)

Benefits of achieving and maintaining Data Quality

So, beyond expressing how costly it can be when companies do not operate with high-quality data, let’s focus on some of the explicit benefits of doing so. As is presented by Experian (2017) in their “2017 global data management benchmark report“, some of the top reasons for maintaining high Data Quality include:

  • Increased efficiency
  • Cost savings
  • Enhanced customer or citizen satisfaction
  • Increased protection of the organization’s reputation and brand
  • Enabling informed decisions
  • Reducing risk or fraud
  • Simplified compliance with industry or government legislation

Benefits of operating with high-quality data further include:

  • Employee efficiency
  • Revenue growth
  • Improved sales conversions
  • Linking data from different databases (especially important regarding legislature such as the GDPR)
  • Delivering data projects on time and on budget
  • Marketing campaign efficiencies

Contact us today to improve your organization’s Data Quality

Who would we be if we presented the problem without a solution? Enter NodeGraph – a Data Quality platform for QlikView and Qlik Sense that utilizes features such as Automated Testing and Data Lineage Visualization to help you understand and trust you Qlik environment.

To find out more about how NodeGraph can help improve the Data Quality in your organization, click here.

Learn more about Data Quality from Ellen!

Interested in learning more about data quality concepts? Then we recommend checking out the first episode of our explainer series “NodeGraph explains”:

By |2018-11-12T10:02:58+00:00September 24th, 2018|Guides|0 Comments