The modern data-driven organization has a peculiar problem: we’ve never had more information available to help us make decisions (thank you, dashboards! thank you, AI!), yet we often have less confidence in that information than we should. Maybe we notice inconsistencies in the reporting, questionable data provenance, or simply have a nagging gut feeling that something doesn’t add up. Either way, these are potential risk surfaces that shift the focus from the decision to the data – a time- and attention-eater that good governance can largely eliminate.
A 2024 survey from Anomalo of more than 300 enterprise data professionals found that 95% had experienced a data-quality issue that directly affected business outcomes. Gartner, meanwhile, estimates that poor data quality costs organizations at least $12.9 million a year on average. The real cost, however, isn’t simply the money spent correcting deficient data. It’s the decisions delayed, opportunities missed, resources misallocated, and risks accepted because nobody is quite sure what is true.
Information governance establishes the conditions under which data can be trusted: authoritative sources, common definitions, accountable ownership, appropriate quality standards, and clarity about how information is created, changed, used, and retained. Even the most sophisticated AI model cannot compensate for inconsistent vocabulary, incomplete information, outdated data, or uncertainty about its source. In fact, automation can make a bad problem worse by allowing bad assumptions to propagate faster.
The objective isn’t to govern information for governance’s sake; it’s to reduce the risk of making decisions based on information that isn’t fit for purpose – while giving decision-makers the confidence to act when it is.