Ralph Waldo Emerson once wrote, “Nothing is at last sacred but the integrity of your own mind.” While it’s highly improbable that he could ever have expected it, his sentiment can easily be applied to enterprise analytics.

After all, modern organizations are heavily reliant on data—and will only become more so as digital transformation accelerates. But if this information is incomplete, inconsistent, or has other integrity issues, companies will face numerous challenges and lose their competitive advantage. There’s also the cost to consider. According to Gartner, bad data costs organizations an average of 9.7 to 14.2 million dollars a year.

So, what should you be mindful of when devising a data integrity plan? Below, we’ve outlined four fundamental points of criteria to consider:

  • Invest in Integration. Investing in data integration that simultaneously cleanses and organizes data is an important step. As with any long-term investment, the cost associated with spending the time and manpower now on data integration pales when compared to the money and resources you save as datasets grow. 
  • Train and Appoint a Steward. Assigning a stakeholder (or multiple stakeholders for larger organizations) to oversee your data ecosystem is another critical step of a modern data integrity plan. This provides employees with a resource for any data issues and also helps establish data accountability and ownership.
  • Audit and Validate. These data stewards are often responsible for monitoring audit trails. This information shows any changes that have been made, who made them, and the date they were enacted. As such, tasking stewards with monitoring these trails ensures not only that bad data is identified but also that it is tracked to its source.
  • Test and Test Again. In order to achieve data integrity, organizations need a regular testing system that supports a strong validation process, including ensuring that data isn’t being entered into conflicting field types.

With every major analyst firm predicting huge surges in enterprise data with the growth of edge computing, widespread adoption of 5G, and other disruptive technologies, it’s clear there is a significant opportunity for companies to mine new data sources for a competitive advantage. In order to do this effectively, however, it’s vital to ensure data integrity.

For more on the above considerations and other elements of a modern data integrity framework, take a look at this recent Datanami article.