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The Benefits of Data Quality – A Review of Use Cases & Trends

Authors Photo Jamie Heckler | May 2, 2020

Looking for an overview of use cases and current trends for data quality? You’ve come to the right place! In this post, we review the benefits of data quality and how it can help your business.

Data quality saves you money

A big reason to pay attention to data quality is that it can save you money. First and foremost, it can help you maximize the return  on your big data investments. And there are additional cost-related benefits (areas that we will discuss below) to help you save even more.

It builds trust

Business leaders rely on big data analytics to make informed decisions. Ensuring quality data can help organizations trust the data.

And further, customers can trust businesses who are confident in their data. If your data is inaccurate, inconsistent or otherwise of low quality, you risk misunderstanding your customers and doing things that undermine their trust in you.

It appears there is an abundance of data, but a scarcity of trust, and the need for data literacy. It’s important to understand what your data MEANS to your organization. Defining data’s value wedge may be key to developing confidence in your enterprise data.

Data quality’s link to data governance

Data quality is  essential for data governance because ensuring data quality is the only way to be certain that your data governance policies are consistently followed and enforced.

During her Enterprise Data World presentation, Laura Sebastian-Coleman, the Data Quality Center of Excellence Lead for Cigna, noted specifically that data quality depends on fitness for purpose, representational effectiveness and data knowledge. And, without this knowledge, which depends on the data context, our data lakes or even our data warehouses are doomed to become “data graveyards.”

Data governance and data quality are intrinsically linked, and as the strategic importance of data grows in an organization, the intersection of these practices grows in importance, too.


4 Ways to Measure Data Quality

Assessing data quality on an ongoing basis is necessary to know how well the organization is doing at maximizing data quality. There are a variety of data and metrics that organizations can use to measure data quality. We review of few of them in this ebook.

Data quality and your customers

Engaging your customers is vital to driving your business. Data quality can help you improve your customer records by verifying and enriching the information you already have. And beyond contact info, you can manage customer interaction by storing additional customer preferences such as time of day they visit your site and which content topics and type they are most interested in.

The more customer information you have, the better you can understand your customers and achieve “Customer 360,” or full-view of your customer. But you need to be aware that more data means more complexity – creating a data integration paradox.

Its role in cyber security

You may be aware of all the ways you can leverage big data to detect fraud, but maybe you’re wondering how data quality can fight security breaches?

Think about it. If the machine data that your intrusion-detection tools collect about your software environments is filled with incomplete or inaccurate information, then you cannot expect your security tools to effectively detect dangerous threats.

Keep in mind, too, that when it comes to fraud detection, real-time results are key. By extension, your data quality tools covering fraud analysis data will also need to be work in real time.

Additional data quality trends

Of course, we’re always thinking about what’s next for data quality. One additional area of interest that’s gaining momentum is machine learning. While machine learning may seem like a “silver bullet,” because of the technologies it enables for us today, it’s important to understand that without high-quality data on which to operate, it is less magical.

Download our eBook to learn how you can measure data quality and track the effectiveness of your data quality improvement efforts.