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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In the AI era, we're constantly talking about how important data is—storing data, disseminating data, and protecting data. As data specialists, we understand bad data management leads to bad use of AI which leads, quite frankly, to bad business outcomes.
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Artificial intelligence (AI) is transforming industries by automating processes, enabling smarter decisions, and unlocking new avenues for innovation. According to recent Semarchy research, 74% of businesses are investing in AI this year to boost performance and efficiency. Although AI's effectiveness relies heavily on quality data, 98% of organizations report that poor data quality undermines their AI initiatives. The result? Inaccurate AI models, biased outcomes, and operational challenges. Put simply, AI's inherent speed, agility, and precision can become liabilities if it's fed flawed data.
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In recent months, the ground has been moving underneath the feet of the overseers of enterprise data and IT environments. AI is bringing new challenges, along with seemingly insatiable business demands for real-time insights. To explore the most compelling technologies promising to shape the data world in the months and years to come, we canvassed market leaders to get a sense of the top technological changes and potential issues that may arise.
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