Four Questions to Ask When Buying a Data Quality Tool

Todd Hinton | February 18, 2016

When you’re evaluating data quality tools, it’s important to ask key questions that will help you ultimately monetize your data faster and more efficiently.

It’s an old story, but it still rings true. When it comes to maximizing the business value you get from your data, what comes out can only be as good as what you put in.

To save you time and energy, here are four questions to ask potential data quality vendors:

1. Is the Technology Future-proofed?

Data is only increasing in its variety, velocity, and volume. It wasn’t that long ago that most captured data was from a form or a written interaction. Today, there are texts, social channel posts, photos and videos, and plenty of unstructured data all coming into your data management system. You need to be vigilant that your data quality technology has the capability to quickly scale and handle new types and unanticipated volumes of data, making sure to keep your future roadmap clearly in sight.

2. How Effective is its Matching Accuracy?

The accuracy of your match results and the scarcity of false positives is imperative to data quality. The standard of accuracy and the definition of duplicates can vary depending on the job, so you don’t want a matching tool that operates in a black box that you can’t adjust. You need the control to map the tool the way you want. If your vendor can’t give you that control, then you are sacrificing your matching accuracy and control to adhere to your business rules.

RedPoint offers an array of pre-built matching tools based on best practice matching rules that we’ve developed over the years. These matching tools, whether they match households, individuals, residents, or businesses, allow you to have superior control over the results as well as the flexibility to adjust scores and weights.

3. How Powerful is the Data Standardization?

It is imperative to cleanse, standardize and normalize your data prior to performing any sort of analysis or data migration project. Without this, you cannot efficiently or effectively monetize your data.

When evaluating data quality tools, you must ensure that the software product can perform postal address standardization, individual name normalization, and company name normalization in an effort to allow the matching process to find the greatest number of matches with the fewest false positives. RedPoint’s address standardization tool supports the address conventions of more than 200 countries.

Share your data goals with potential vendors to see if they offer the kind of data standardization features you need to accomplish your specific goals.

4. Who Are Your Competitors?

It’s important to evaluate other data quality tools to have a better understanding of how your preferred vendor stacks up against its competition and also to know if you’re even looking at the right kind of data quality product.

For the second time in a row, RedPoint received the highest score in both the Data Integration and Operational/Transactional Data Quality Use Case categories in https://www.gartner.com/doc/3179817/critical-capabilities-data-quality-tools. RedPoint also received the second highest scores in Data Migration, Big Data & Analytics, and Master Data Management, and the third highest score in Information Governance Initiatives. The full report may be downloaded here.

Now What?

If your data quality product provider can’t answer these four questions well, it’s time to question whether it has the right solution for you.

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Todd Hinton
Todd Hinton

As VP of Engineering, Data Management for RedPoint Global, Todd Hinton leverages more than 20 years of technology management and software development experience to his oversight of RedPoint Global’s data management product offerings, including master data management and the RedPoint Customer Data Platform.