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What is Data Quality? And why should you be thinking about it? | NodeGraph


What is Data Quality? And why should you be thinking about it? | NodeGraph

What is Data Quality? And why should you be thinking about it?

As the Big Data industry continues to grow, organizations are becoming increasingly hungry for more and more data. But is the saying "the more, the merrier" always applicable? We'd argue no. Not unless you are maintaining a high level of data quality. What is data quality, you ask? That's what we are here to tell you.

What is Data Quality?

Data quality refers to the perceived appropriateness of data in a specific situation. In a broader sense, it encapsulates elements such as:

  • Completeness
  • Uniqueness
  • Timeliness
  • Validity
  • Accuracy
  • Consistency
  • Relevance
  • Reliability
  • Accessibility

So let's break that down a little. In order to establish a high level of data quality, you need to ask yourself whether the data you are using is appropriate within the context. Basically, if you are applying the wrong type of data to a problem, regardless of whether it is outdated or simply irrelevant, you are not establishing quality. And, if this is the case, more data definitely doesn't mean better insights.

Cleansing, scrubbing, and profiling

There are several different ways to improve the quality of your data. Most commonly, you can embark on a data cleanse. Data cleansing, or data scrubbing,  is the act of removing incorrect, incomplete and duplicated data. And no, this is not a simple process, but it allows you to substantially improve your derived business insights.

On a slightly more qualitative level, you also need to remove data that isn't accurate and/or relevant to the case in question. This is called data profiling.

Ultimately, you need to ensure that the data follows all of the aforementioned elements.

And, finally, why should you be thinking about it?

It goes without saying that the process of establishing a high level of data quality is a lot simpler if you consider it from the get-go, rather than in an after-the-fact manner. On top of this, it's also important to note that it's not enough to merely establish a high level of data quality- you also have to maintain it. So, quite simply, that's why you should be thinking about it. Because it is essential to producing well-informed and useful business insights. And because if you don't take into account, you won't be able to reap the benefits of a successful business intelligence initiative.

If you are new to NodeGraph, first of all - welcome. We are a data quality platform for QlikView and Qlik Sense. To find out how we can help you take control of your data, get started today.

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