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3 key digital transformation priorities for 2024

CIO Business Intelligence

The analyst reports tell CIOs that generative AI should occupy the top slot on their digital transformation priorities in the coming year. I wrote in Driving Digital , “Digital transformation is not just about technology and its implementation. Luckily, many are expanding budgets to do so. “94%

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Systems Thinking and Data Science: a partnership or a competition?

Jen Stirrup

How can systems thinking and data science solve digital transformation problems? Understandably, organizations focus on the data and the technology since data retrieval is often viewed as a data problem. How is it possible to enable data-driven decisions in a systems thinking approach?

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SAP Datasphere Powers Business at the Speed of Data

Rocket-Powered Data Science

Data collections are the ones and zeroes that encode the actionable insights (patterns, trends, relationships) that we seek to extract from our data through machine learning and data science. Instead, what we really need is for our business to run at the speed of data. Datasphere is not just for data managers.

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Digital KPIs: The secret to measuring transformational success

CIO Business Intelligence

Regardless of where organizations are in their digital transformation, CIOs must provide their board of directors, executive committees, and employees definitions of successful outcomes and measurable key performance indicators (KPIs). As a result, outcome-based metrics should be your guide.

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CDOs’ biggest problem? Getting colleagues to understand their role

CIO Business Intelligence

That’s according to a recent report based on a survey of CDOs by AWS in conjunction with the Chief Data Officer and Information Quality (CDOIQ) Symposium. The CDO position first gained momentum around 2008, to ensure data quality and transparency to comply with regulations following the housing credit crisis of that era.

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7 Lessons to Ensure Successful ML Projects: The Dataiku Take

Dataiku

Regularly thrown around are the myriad of reasons that data science and machine learning (ML) projects fail, with some of the popular ones including data quality issues (missing or incomplete data, for example), problems around tooling (i.e.,

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“Data is the closest thing to magic in the modern world…”

Timo Elliott

They discussed how medium and small sized enterprises should handle the digital transformation, and the concrete roles of Data Protection Officers and Innovation Evangelists during this process. “We Yves: Do you think people are already fully convinced about the real added value of digital transformation?