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Does Your Organization Need to Invest in Data Quality Help?

If you’re considering finding out more about data quality solutions, there’s likely a reason you’re doing so. But identifying why a solution is necessary for your business and how it’d make a positive impact are two issues very much worth looking into prior to jumping into a solution head-first. If you’re considering a data quality solution, here are a few questions to ask yourself and your team before taking the issue to the C-Level.

What Critical Business Issues Would Better Data Quality Solve?

In our experience, our solution has the biggest impact for companies that identified the top five issues within their organization that data quality improvements would make an immediate, positive change. Identifying these issues, wherever they reside in your data records, from the outset of this exercise will focus your assessment of how best a DQ solution will help your business. Enlist the help of a few cohorts across the company to ask how data issues are impeding their work, and how better data would help them do their jobs more efficiently and effectively.

What’s the ROI on a Data Quality Solution?

According to the research firm Gartner, poor data quality costs the average enterprise business $9.1 million a year. Obviously, putting together an analysis of the estimated losses incurred by poor data quality specific to your business would be ideal. However, estimating what your organization can expect to gain in the form of saved time, energy, resources, and better analytical insight is usually a bit easier to predict. Knowing the expected ROI in adopting a data quality solution will help the higher-ups see this as a worthy investment for the company.

What is the Current State of Your Data Quality?

Having an idea of where your organization stands before you get a new initiative started helps keep expectations and timelines in check. If the C-Level is under the impression that your data quality and data processes just need a few tweaks before signing off on an initiative, only to find out that the DQ situation is far worse than anticipated, support and patience for the data quality solution’s integration can quickly wane. Having a clear understanding of the state of your data quality before getting started with a data quality solution tempers expectations and paints a realistic picture of the way ahead.

If you need further incentive to schedule a consultation on your data, check this post out. Be sure to follow us on Twitter, LinkedIn, and Facebook for more company news and industry insights.

5 Steps to Building an Agile Data Quality Process

Wes Sovis

About Wes Sovis

Wes is a technology professional who specializes in marketing and business development.

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