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Businesses Discover the Shocking Cost of Bad Data

Big data has become incredibly important for many companies all over the world. Unfortunately, the growing emphasis on big data has led to some poor decision-making. Many entities are prioritizing data scalability at the expense of data quality. As a result, bad data is costing them a lot of problems. 

In the USA alone, bad data - any poorly structured or managed data - costs the country over $3 trillion every year. Whether it’s created from data engineers accidentally adding an extra zero, a discrepancy in how things are formatted, or even problems with the data system itself, a lot can go wrong with data.

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Using Machine Learning to Automate Data Cleansing

According to Gartner’s report, 40% of businesses fail to achieve their business targets because of poor data quality issues. The importance of utilizing high-quality data for data analysis is realized by many data scientists, and so it is reported that they spend about 80% of their time on data cleaning and preparation. This means that they spend more time on pre-analysis processes, rather than focusing on extracting meaningful insights.

Although it is necessary to achieve the golden record before moving on to the data analysis process, there must be a better way of fixing the data quality issues that reside in your dataset, rather than correcting each error manually.

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The Cost of Bad Data

When developing new functionality or expanding existing features, members of the feature team can find themselves battling a hidden enemy — bad test data. In this article, I am going to talk about the challenges of not having adequate test data for feature validation and offer some options to avoid falling into this hidden sinkhole that can absorb a team's success rate.

How Bad Data Is Bad?

One of the first topics of discussion when starting a new project is to understand the source of the non-production data sources, which will be used to validate a feature team's work. Nothing scares me more to hear something like, "We have a test database that was created from production a few years ago."