Data is the lifeblood of most organizations these days. The insights that come from a company’s data can help drive major — and minor — decisions, providing incremental boosts in company performance on a regular basis and even drastic boosts on occasion. But if you’re not seeing these kinds of results from your data, your problem might not be the analytics. It’s more likely that you’re missing key steps in preparing the data and ensuring its quality.
Proper data preparation ensures the ability to access both internal and external sources of data and transform these data sets into a form that’s ready for analysis. This might involve various forms of data transformation, including processes for improving data quality. Data scientists spend up to 80% of their time preparing data for analysis and ensuring its quality, leaving only 20% to do the actual modeling and analysis that deliver the relevant, actionable insights companies are after.
These numbers are not new, and they shouldn’t be a surprise to anyone reading this. But for those wondering what exactly goes into the process, why it takes up so much of scientists’ time, and why data prep and data quality are so important, we spell it out here.
