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The Enduring Significance of Data Modeling in the Modern Data-Driven Enterprise

erwin

Q: Is data modeling cool again? In today’s fast-paced digital landscape, data reigns supreme. The data-driven enterprise relies on accurate, accessible, and actionable information to make strategic decisions and drive innovation. A: It always was and is getting cooler!!

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Using Data-Driven Lean Thinking to Optimize Business Processes

Smart Data Collective

Data-driven decision-making has become a major element of modern business. A growing number of businesses use big data technology to optimize efficiency. However, companies that have a formal data strategy are still in the minority. Furthermore, only 13% of companies are actually delivering on their data strategy.

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Optimizing Risk and Exposure Management – Roundtable Highlights

Cloudera

We recently hosted a roundtable focused on o ptimizing risk and exposure management with data insights. For financial institutions and insurers, risk and exposure management has always been a fundamental tenet of the business. Now, risk management has become exponentially complicated in multiple dimensions. .

Risk 99
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12 Cloud Computing Risks & Challenges Businesses Are Facing In These Days

datapine

It provides better data storage, data security, flexibility, improved organizational visibility, smoother processes, extra data intelligence, increased collaboration between employees, and changes the workflow of small businesses and large enterprises to help them make better decisions while decreasing costs.

Risk 237
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CIOs weigh the new economics and risks of cloud lock-in

CIO Business Intelligence

As CIOs seek to achieve economies of scale in the cloud, a risk inherent in many of their strategies is taking on greater importance of late: consolidating on too few if not just a single major cloud vendor. This is the kind of risk that may increasingly keep CIOs up at night in the year ahead.

Risk 125
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ANZ Bank’s Advances Data-Driven Analytics With Cloudera

Cloudera

Data and analytics are the essential engines in this strategy which entails upgrading the bank’s core banking technology to deliver reliable, efficient, and secure services for their customers while safeguarding critical data. The approach offered lower risks and allowed for comprehensive platform and application testing.

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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

If the relationship of $X$ to $Y$ can be approximated as quadratic (or any polynomial), the objective and constraints as linear in $Y$, then there is a way to express the optimization as a quadratically constrained quadratic program (QCQP). Figure 2: Spreading measurements out makes estimates of model (slope of line) more accurate.