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Data’s dark secret: Why poor quality cripples AI and growth

CIO Business Intelligence

As technology and business leaders, your strategic initiatives, from AI-powered decision-making to predictive insights and personalized experiences, are all fueled by data. Yet, despite growing investments in advanced analytics and AI, organizations continue to grapple with a persistent and often underestimated challenge: poor data quality.

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Why Should CIOs Champion AI Literacy?

Dataiku

AI is rewriting the rules of organizational capability at lightning speed. Today's CIOs face a slightly more complex challenge: ensuring their organizations can effectively navigate the AI revolution without getting lost in translation.

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Teaching tech by example at RMIT University

CIO Business Intelligence

Its purpose is to create transformative experiences for students around the world, and Sinan Erbay, the public university’s CIO, breaks down its value proposition as an applied learning style. “We Take on some innovation initiatives and champion them, but also be collaborative. Move out of your comfort zones.

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The CIO’s call to action on gen AI

CIO Business Intelligence

Generative AI has taken the world by storm and is being discussed in C-suites and boardrooms daily. While this “overnight success” has been decades in the making, we’re just now getting a glimpse of the impact and implications of generative AI and the massive disruption that comes along with it. We need that kind of leadership.

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The Gartner 2022 Leadership Vision for Data and Analytics Leaders Questions and Answers

Andrew White

Which trends do you see for 2022 in AI & ML technology and tools and tool capabilities? – In the webinar and Leadership Vision deck for Data and Analytics we called out AI engineering as a big trend. I would take a look at our Top Trends for Data and Analytics 2021 for additional AI, ML and related trends.

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Your data’s wasted without predictive AI. Here’s how to fix that

CIO Business Intelligence

When I meet with CIOs or executive sponsors, one of the first things I do is map out their analytics maturity curve. Ive worked with organizations that had hundreds of dashboards but still couldnt tell you why churn was increasing or how to reduce it. What should we do? What will happen? Each one on its own can derail momentum.