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Big Data: The Technology Behind Retailers Success

Smart Data Collective

Data-based insights can help make the right decisions, keep up with market trends and navigate the uncertainty. This global coffee brand has increased its revenue by 26% from 2016 to 2019. Thus, Starbuck defines areas that potentially will be successful and mitigate risks of opening in unprofitable ones.

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Belcorp reimagines R&D with AI

CIO Business Intelligence

These circumstances have induced uncertainty across our entire business value chain,” says Venkat Gopalan, chief digital, data and technology officer, Belcorp. “As Finally, our goal is to diminish consumer risk evaluation periods by 80% without compromising the safety of our products.” This allowed us to derive insights more easily.”

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Paul Martin: CIOs don’t retire, they go work on boards

CIO Business Intelligence

Two years of pandemic uncertainty and escalating business risk have sharpened the focus of corporate boards on a technology trend once dismissed as just another IT buzzword. I joined Baxter as CIO in 2011, and in 2016 I was presented with the opportunity to join my first public company board.

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Don’t Just Climb the Ladder. Explore the Jungle Gym

CIO Business Intelligence

You will always face uncertainty and unexpected challenges. Either way, you will have to face uncertainty. Whatever the case, it’s important to take calculated risks. Uncertainty will always be uncomfortable. Chet successfully took Apigee public before the company was acquired by Google in 2016.

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3 key mistakes leaders make today and how to overcome them

CIO Business Intelligence

Mistake 1: undisciplined growth Leaders are facing times of uncertainty, magnified recently with the collapse of Silicon Valley Bank and ongoing market turmoil. As we’ve seen over the last several months, irresponsible growth can lead to hiring freezes and limiting investments (at best) or mass layoffs and big risks (at worst).

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Simulation for better decision making

Cloudera

My narrower vision of the next advancement in analytics is driven (or biased) by my quantitative risk management background and the critical role that computational simulation capabilities have played in many advances in the world of finance. Derman (2016), Cesa (2017) & Bouchard (2018)). Blog Post, Nov-2016. Mauro Cesa. “A

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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

Crucially, it takes into account the uncertainty inherent in our experiments. Risk and Robustness Our estimates $widehat{beta}$ of the "true'' coefficients $beta$ of our model (1) depend on the random data we observe in experiments, and they are therefore random or uncertain. It is a big picture approach, worthy of your consideration.