Continuous Data Quality: What It Means and Why You Need It
Achieving data quality is not enough to maximize the value of big data operations. In order to make the very most of big data, your data quality should be continuous. Here’s what continuous data quality means, and how to achieve it.
What Is Data Quality?
Data quality refers to the ability of a given set of data to fulfill an intended purpose.
Without data quality, the information that you attempt to use to drive business value will fall short of enabling the insights you seek.
Problems like missing information within data sets, inconsistent data formatting and redundant entries, to name just a few common data quality problems, can undercut the quality of data.
Typical Data Quality Strategies
In a basic big data workflow, data quality might be something that you manage at a single point in time. You might run data quality checks as data is collected. Or maybe data quality review is built into your data integration process.
In the worst of cases, you might check data quality retrospectively, after data interpretations have already been reached. That can help you to identify problems that might cause flawed conclusions, but only after the fact.
In all of these cases, the main problem is not when data quality checks happen, but rather the fact that they happen at only a single point in the workflow. Data quality is therefore not continuous; it’s treated as a one-and-done process.
Achieving Continuous Data Quality
You can take data quality to the next level by making quality checks fully continuous.
Continuous data quality means building data quality reviews into every stage of your big data workflow. Start by verifying data as you collect it. Run ongoing analyses of your data sets to check for data quality issues. Check for data quality again whenever data aggregation or transformation occurs. Continue looking for and fixing data quality issues even for archived data.
A continuous data quality strategy is important for two main reasons.
First, by performing constant checks for data quality problems, you significantly increase your chances of finding critical issues before they disrupt your big data operation. If you run data quality reviews at only one point in your workflow, you may overlook issues that are not apparent in one context, but would be easy to identify at other points in the workflow.
Second, continuous data quality helps you to find and fix data quality issues that arise as your data is moved and transformed. Data quality problems do not originate from only one point in a big data workflow. Instead, the various processes that you perform on your data as you aggregate, transform and visualize it can all introduce new data quality issues. By enforcing data quality early and often, you stand a much better chance of catching these problems.
Continuous Data Quality and Continuous Software Delivery
You can think of continuous data quality as a similar strategy to continuous software delivery. In the world of software development and DevOps, the idea of continuously delivering software updates to users – which means developers roll out application updates on a rolling, ongoing basis, rather than at fixed and disparate points in time – is essential to modern application development strategies. Continuous delivery leads to faster innovation and higher user satisfaction.
Continuous software quality is similar in that it’s the best way to avoid critical data quality problems in a world where possible sources of data quality issues abound. By making data quality part and parcel of every stage of your big data operation, you maximize your chances of maintaining sound data sets and, by extension, deriving actionable insights.
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