Flattening the Data Quality Mistrust Curve with DataOps

A lack of trust will dramatically impact your efforts to become data driven unless you proactively limit the spread of mistrust from data quality incidents.

It only takes a small problem to shake someone’s trust in data, but it takes a lot of deliberate effort to make them realize it was just one problem, not a larger issue. Even mature data organizations run this risk, as it is impossible to fully eliminate all data quality issues. This can lead to the rapid spread of mistrust throughout your organization, unless we adapt some lessons from the world’s efforts to flatten the curve of the COVID pandemic.

Data Mistrust is already Endemic

Despite the fact that nearly 98% of organizations are making major investments in their ability to become data driven, data quality still costs the average organization $15M per year in bad decisions according to Gartner, and impacts 90% of companies according to Experian . While I covered a few data quality horror stories in a prior article, it isn’t common for a data quality problem to bring down a whole company. Additionally, there are now many modern tools (SodaData, ToroData, Trifacta) and practices (primarilly DataOps) that are making the…