Good Data vs Bad: How to Decide What to Keep and What to Discard

Unfortunately, there’s no “silver bullet” for separating good data from bad. Instead, organizations should think of data quality as a habit, with “good” data clearly defined and concrete processes in place to harvest what’s valuable and discard what isn’t.

With that in mind, here are three steps to taking unfiltered data and deciding what to keep — and what to throw out — to achieve optimal data accuracy.