Businesses are missing out on data science’s potential because of a mix of data- and business-related errors in their projects. Eight typical data-related mistakes that can undermine these efforts are covered in a recent report.
One major error is not giving enough weight to comprehending the business issue before attempting to solve it. Sometimes, data teams install complex models or tools without first making sure they truly solve a need. This may result in models that fall short of expectations and a waste of time and resources.
An additional offender? data quality. In data science, the proverb “garbage in, garbage out” is accurate. Data that is incorrect, missing, or inconsistent might provide biased or untrustworthy models, which eventually obstruct important insights. The significance of doing data quality checks at the outset of a project is emphasized in the study.
The study emphasizes how underutilized data visualization is, despite the human eye being an extremely powerful tool. When immersed in intricate modelling methods, data scientists may neglect to step back and visually examine the data. Strictly depending on numerical summaries makes it easy to overlook patterns and linkages.
Want to learn more about the eight data-related mistakes and how to avoid them? Check out the full report here.
