Amazon Web Services went down earlier today, kicking off at 3 am ET at AWS' main US-East-1 region hosted in northern Virginia—the heart of its cloud services. According to the website Downdetector, user reports indicated problems at sites including Amazon, Disney+, Lyft, the McDonald's app, The New York Times, Reddit, Ring doorbells, Robinhood, Snapchat, T-Mobile, United Airlines, Venmo, Verizon, and more.
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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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