How Data Scientists Can Follow Quality Assurance Best Practices

The world runs on data. Data scientists organize and make sense of a barrage of information, synthesizing and translating it so people can understand it. They drive the innovation and decision-making process for many organizations. But the quality of the data they use can greatly influence the accuracy of their findings, which directly impacts business outcomes and operations. That’s why data scientists must follow strong quality assurance practices.

What Is Quality Assurance?

In data science, quality assurance ensures a product or service meets the required standards. It refers to verifying data is accurate, complete, and consistent. The data must be free of inconsistencies, errors, and duplicates, and the scientists must properly organize and document it well.