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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As cloud adoption accelerates and systems grow more complex, traditional testing and monitoring approaches are no longer a match to preventing outages. Enterprises face increased pressure to ensure system reliability in the face of unpredictable disruptions, from traffic spikes to network outages.
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Economic uncertainty is prompting many companies to think about how to do more with less. But what if they're actually positioned to do more with more and just don't realize it? Many organizations already have the resources they need to improve efficiency and resilience in challenging times.
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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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