Four Key Factors to Consider Data Testing

It is a data-driven world. The amount of data produced each day is 2.5 quintillion bytes. And by 2020, 1.7 megabytes of new information will be created every second, per person. Data, therefore, is the new oil, and businesses have already started leveraging this data for their benefits.

With a growing movement to digital technologies like IoT and AI/ML, data is all set to become even more valuable. However, what is of paramount importance is the way this data is leveraged. The reality of data is that it is never clean. Data is either incomplete, or inconsistent, or invalid, or inaccurate. For the sake of understanding, let’s take a simple example – in a mortgage firm, while collating data about debtors, there could be fields that might be missing from the source like no zip code (incomplete data) or there could be an error in entering the zip code and its only 4 digits, making it inaccurate. These errors can lead to inaccurate insights that can result in revenue loss, missed opportunity, or even reputational damage.

Testing, therefore, forms an imperative part of any data strategy. It is critical that data is tested at the source to clean, correct, and validate it.

A good-quality data brings in several benefits to a business, which include:

a. Boosting productivity: Data scientists are data experts who have been hired to analyze and interpret data. Instead, 80% of the time of a data scientist is spent on cleaning and preparing the data, which also is the least enjoyable part of their work. In short, businesses are wasting their premium data scientists as ‘data janitors’. Testing data can prevent this and data scientists can focus on their core work – getting relevant and actionable insights.

b. Making better decisions with the right insights: A better data quality results in the right insights, which in turn leads to high confidence decisions by removing the ‘guess-work’ out of critical decisions. This decreases the business risks to a great extent.

c. Enabling customized targeting to customers: With the right data, the business has the right insights to customize its offerings to the customers. Imagine this – a Netflix user who watches a lot of horror shows and movies would continue using Netflix if he gets the right recommendations.

d. Improving revenues: Businesses lose millions of dollars every year due to poor data quality. In the above example of Netflix, if the user doesn’t get the right recommendations, he would move to your competition, thus resulting in the loss of business.

Conclusion:

Needless to say, Data is important.

However, one of the biggest issues is masking unstructured data and archiving data. Moreover, organizations also lack the necessary skills and expertise to test their data, which results in inaccurate insights. Data can only be as useful if an organization can maximize its full potential.

The good news is that Tavant can offer a leading-edge automated solution for testing data to improve its quality and make it appropriate for consumption. For more information, reach out to us at [email protected].

Source –

• https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/#3fd7751d60ba

• https://www.nodegraph.se/big-data-facts/

• https://hackernoon.com/a-few-facts-to-take-into-account-about-big-data-market-growth-eaf7c993f0fd

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