Data has taken a new position in the spotlight as the most important part of using AI. If the organization is using corrupt data, insights will vary wildly, and misinformation can damage the company's reputation. Poor data quality costs organizations at least $12.9 million per year on average, according to Gartner research from 2020.
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For the database world, the future looks extremely challenging—and, even more, exceedingly promising. Looking ahead over the next few years, organizations will be relying on their databases in ways never imagined, as leaders and decision makers look to their data resources for intelligence, real-time views, and adaptability to business changes.
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As someone who spent my early career in healthcare analytics, I've seen firsthand that it's not dashboards or models that slow us down, it's disagreement about what the data means. Enterprises don't fail at analytics because of their tech stack; they fail because teams can't agree on a shared vocabulary.
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AI adoption is rapidly accelerating, but data quality and data governance remain two of the biggest challenges to successful implementation. While AI depends on vast amounts of data, the quality and accuracy of the information it processes directly determine its effectiveness. Strong data governance allows organizations to mitigate various threats, while poor data quality leads to inaccurate insights and inefficiencies.
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