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We are excited to announce a new strategic partnership with Salesforce. This partnership will bring data governance and deep insights around data lineage into Salesforce Data Cloud. With this bidirectional integration, Alation joins the Salesforce Data Governance alliance.
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It's analyst report season! In the next few months, analyst firms will provide guidance on how to approach the data catalog market and key criteria for choosing a data catalog. These reports can be helpful starting points. But they should be just that: a starting point. With over 12 years of experience and a customer base of nearly 600 enterprises, including 40 of the Fortune 100, we've developed a comprehensive understanding of what truly matters in a data catalog solution to deliver business success. Here are our key criteria uncovered:
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As applications increasingly migrate to the cloud, security has become a critical priority. In fact, 80% of companies experienced cloud-security incidents in 2023. Cloud security encompasses several key components, including:
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Enterprises are awash in data. How can leaders transform all that raw data into information that delivers business value?
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Key takeaways
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Key Takeaways
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The hype around AI is seemingly unstoppable. On the vendor side, over half of the companies on the Forbes “Next Billion Dollar Startups” list are AI-related. On the buyer side, and fueling the hunger for AI products and services, are organizations buying into said hype. Research by Cisco finds that nearly all (98%) of organizations currently “feel urgency around AI,” with exactly half dedicating 10-30% of IT budgets to AI. And, a new forecast by IDC says global spending on AI will more than double by 2028 with a compound annual growth rate of just under 30%. Bain & Company sees even greater growth, saying the total market for AI will grow by up to 55% annually “for at least the next three years.”
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The importance of effective data management is paramount to growth and competitiveness, especially in today's environment of hyperspeed, volatility, and uncertainty As organizations increasingly rely on data to drive business decisions and power AI-driven innovations, the sheer volume and complexity of data can present significant challenges.
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The rapid evolution of artificial intelligence (AI) in healthcare is reshaping how we diagnose, treat, and manage diseases. But with great technological power comes great responsibility. As AI technologies are increasingly integrated into clinical settings, the necessity for ethical frameworks and regulatory guidelines becomes paramount to ensure patient safety, equity, and effective healthcare outcomes. Addressing these concerns is critical to ensuring AI-driven innovations improve patient care while maintaining fairness and safety.
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Reinforcement Learning from Human Feedback (RLHF) is a machine learning technique that uses human feedback to optimize AI models to learn more efficiently. Unlike traditional supervised learning, where models are trained on labeled datasets, RLHF allows models to learn through trial-and-error experience and feedback from humans.
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As organizations increasingly embrace artificial intelligence (AI) to drive innovation and efficiency, the need for trusted data has never been more critical.
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