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Data Quality Study Guide – A Review of Use Cases & Trends

Our summer school series continues with today’s fully loaded study session. Have you been taking note of all the use cases and current trends for data quality? Maybe now is a good time for a review!

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. But according to figures presented at the recent Gartner Data and Analytics Summit, C-Level executives believe that 33% of their data is inaccurate. 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.”

At this year’s Data Governance and Information Quality Conference (DGIQ), our own Keith Kohl lead the session about how 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.

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.

Download eBook: Getting Closer to Your Customer in a Big Data World

For a more detailed overview of the different sources of this data, which data points are critical in obtaining, and tips for customer 360 success, download our eBook Getting Closer to Your Customers in a Big Data World.

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. In March, Syncsort’s CEO Josh Rogers was interviewed on theCUBE, where he discussed his vision for its future.

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.

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