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 as well, you may be undercutting the effectiveness of your entire big data operation.
Let’s take a look at how data quality can determine whether your big data operations succeed or fail.
What is Data Quality?
Data quality refers to the ability of a given set of data to fulfill an intended purpose. If you have quality data, it means your data can help you achieve your goals.
Whether or not a data set contains quality data is determined ultimately by what you want to use the data to achieve. However, data quality generally depends on having data that is free of errors, inconsistencies, redundancies, poor formatting and other problems that might prevent the data 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 data 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 data quality.
Why? Consider the following ways in which data quality 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 data analytics results are accurate, data 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 data set 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 data sets 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 data you have, you can’t make up for the shortcomings described above if you lack data quality.
The new TDWI Checklist Report: Strategies for Improving Big Data Quality for BI and Analytics, takes a look at applying data quality methods and technologies to big data challenges that fit an organization’s objectives.