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Don’t Let Lack of Big Data Quality Drive Bad Business Decisions

In the past decade, big data has made headlines for its ability to provide answers to crucial questions. However, what these stories tend not to focus on is that big data is only useful when you have high levels of data quality. If data quality is poor, you won’t be able to make good decisions. 

The issue with big data is that there’s so much of it. Where do you start? In this post, we’ll explore the importance of data quality and how you can measure it in a way that’s relevant for your business.

Why Is Data Quality Important, Especially When It Comes to Big Data?

To understand why data quality is important, it’s first critical to explain what it is. “Data quality” is a set of measures of information’s condition. It includes a number of dimensions such as accuracy, completeness, consistency, timeliness, validity, and uniqueness. 

Data quality is crucial, especially for big data, because without these measures, you don’t know how good your data is. As a result, you can make poor decisions. For example, if you’re using information from three years ago for marketing campaigns, it won’t be as accurate as data that was gathered two days ago. Your marketing messages won’t reach the right target, which wastes time and money. 

Three colleagues sitting at a table with a laptop.

How Can You Measure Big Data Quality So It’s Relevant for Your Company?

The thing about data quality is that it’s not exactly one-size-fits-all. Some data quality dimensions simply won’t apply to your firm. Completeness is a good example; it looks different at every firm. You may, for instance, not need a customer’s middle name for an entry to be considered complete. 

Also, the advent of big data means that companies have different data quality needs. With information coming in from so many different sources, you’ll see a variety of formats. Maybe there will be fields missing. There might also be invalid information, such as codes your company doesn’t use, or values that aren’t in the right ranges. 

To make the most out of your big data, you need to implement data profiling. “Data profiling” is a set of analytical techniques that evaluates data content to provide a complete view of each element in a data source. In the next section, we’ll discuss a tool that simplifies data profiling for big data. 

The-State-of-Enterprise-Data-Quality

Syncsort’s Trillium DQ: Get the Most Out of Big Data 

Syncsort’s Trillium DQ is an industry-leading data profiling solution designed to handle big data. With Trillium DQ, data analysts and data stewards can profile data sources to learn about data anomalies and validate information so that it meets business and regulatory requirements. One of Trillium DQ’s benefits is that it incorporates previously inaccessible data sources, so you gain a complete view of your corporate information. 

Understanding which elements of data quality and data integrity matter most helps you get more out of your data. For more information on the state of data quality, take a look at our survey.

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