Why Data Quality Makes or Breaks Your Big Data Operations

What drives big data success? Your first thought might be analytics accuracy or the amount of data you have available to process. But if you’re not thinking about data quality, you may be undercutting the effectiveness of your entire big data operation.

What is data quality?

Data quality refers to the ability of a given set of data to fulfill an intended purpose. Whether or not a dataset contains quality information is determined ultimately by what you want to achieve. However, it generally depends on having information that is free of errors, inconsistencies, redundancies, poor formatting and other problems that might prevent  it from being used readily.

Factors in big data success

If you have invested in big data, it’s probably because you want to use large amounts of information to glean insights that would not be available to you by other means.

Your ability to obtain those insights will depend, in part, on the types of analytics tools you have available at your disposal.

The amount of data you collect matters, too. There is no official definition of how much data amounts to big data, but in general, the more quality data you have available at your disposal, the more accurate and detailed your analytics results will be.

Big data success hinges as well on the speed of your big data operations. In many cases, real-time data analytics are crucial for obtaining actionable results.

These are some of the factors you should consider when designing a big data strategy.

Data quality and big data

But they’re not the only considerations you need to keep in mind.
In many respects, the single biggest factor in shaping big data success is the quality of the data.
Why? Consider the following ways in which it can make or break the accuracy, speed and depth of your big data operations:

• Real-time data analytics are no good if they are based on flawed data. No amount of speed can make up for inaccuracies or inconsistencies.
• Even if your analytics results are accurate, quality issues can undercut analytics speed in other ways. For example, formatting problems can make data more time-consuming to process.
• Redundant or missing information within datasets can lead to false results. For example, redundant information means that certain data points appear to be more prominent within a dataset than they actually should be, which results in misinterpretation of data.
• Inconsistent data – meaning data whose format varies, or that is complete in some cases but not in others – makes datasets difficult to interpret in a deep way. You might be able to gather basic insights based on inconsistent data, but deep, meaningful information requires complete datasets.

No matter how great your analytics tools are, how fast you can obtain results or how much information you have, you can’t make up for the shortcomings described above if you lack quality information. Precisely’s 2019 Enterprise Data Quality survey explores the challenges and opportunities for organizations looking to bring quality data across the enterprise as volumes grow and new technologies emerge.

Read How “Good Enough” Quality is Eroding Trust in Your Big Data Insights to learn more.