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Decision Making with Uncertainty Requires Wideward Thinking

Andrew White

COVID-19 and the related economic fallout has pushed organizations to extreme cost optimization decision making with uncertainty. Everything Changes. As a result, Data, Analytics and AI are in even greater demand. Every decision by every executive leader need information: What investments to furlough or delay, or accelerate?

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Key Data Trends And Forecasts In The Energy Sector

Smart Data Collective

With the Coronavirus pandemic, the world has been thrown into complete uncertainty. The uncertainty comes with a major market shift, the dimensions of data software cannot be ignored. According to a report by Capgemini from 2019, up to $813 billion is feasible if we integrate the necessary tech. Effects of Analytics.

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Climate change predictions: Anticipating and adapting to a warming world

IBM Big Data Hub

According to the Geophysical Fluid Dynamics Laboratory of the US’s National Oceanic and Atmospheric Association (NOAA), “Climate models reduce the uncertainty of climate change impacts, which aids in adaptation.” Red Cross Red Crescent Climate Centre, 2019. Learn about the IBM Sustainability Accelerator. IPCC, 2023.

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How Digital Native & Cloud Native Will Influence FP&A

Jedox

In partnership with Microsoft Azure and NetApp , Jedox is blazing the trail forward to use cloud native technology to continue meeting the demands of a fast-changing world that often includes measurable uncertainties.

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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. Setting the optimal prices. This global coffee brand has increased its revenue by 26% from 2016 to 2019. Big data is a not new concept, and it has been around for a while. However, this process can be automated.

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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). Crucially, it takes into account the uncertainty inherent in our experiments.

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What you need to know about product management for AI

O'Reilly on Data

Machine learning adds uncertainty. Underneath this uncertainty lies further uncertainty in the development process itself. There are strategies for dealing with all of this uncertainty–starting with the proverb from the early days of Agile: “ do the simplest thing that could possibly work.”