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Uncertainties: Statistical, Representational, Interventional

The Unofficial Google Data Science Blog

by AMIR NAJMI & MUKUND SUNDARARAJAN Data science is about decision making under uncertainty. Some of that uncertainty is the result of statistical inference, i.e., using a finite sample of observations for estimation. But there are other kinds of uncertainty, at least as important, that are not statistical in nature.

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Decision-Making in a Time of Crisis

O'Reilly on Data

We know, statistically, that doubling down on an 11 is a good (and common) strategy in blackjack. But when making a decision under uncertainty about the future, two things dictate the outcome: (1) the quality of the decision and (2) chance. Mike had made the common error of equating a bad outcome with a bad decision.

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20 issues shaping generative AI strategies today

CIO Business Intelligence

They note, too, that CIOs — being top technologists within their organizations — will be running point on those concerns as companies establish their gen AI strategies. Here’s a rundown of the top 20 issues shaping gen AI strategies today. says CIOs should apply agile processes to their gen AI strategy. It’s not a hammer.

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Generative AI readiness is shockingly low – these 5 tips will boost it

CIO Business Intelligence

As genAI caught fire in 2023, many organizations rushed to test and learn from the technology and harness it to grow productivity and improve processes. 2 Key challenges include a shortage of talent and skills (62%), unclear investment priorities (47%), and the lack of a strategy for responsible AI (42%), BCG found.

IT 111
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Easily Build an Optimization App and Empower Your Data

Speaker: Gertjan de Lange

If the last few years have illustrated one thing, it’s that modeling techniques, forecasting strategies, and data optimization are imperative for solving complex business problems and weathering uncertainty. Discover how the AIMMS IDE allows you to analyze, build, and test a model. Don't let uncertainty drive your business.

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Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability

DataKitchen

The uncertainty of not knowing where data issues will crop up next and the tiresome game of ‘who’s to blame’ when pinpointing the failure. In the context of Data in Place, validating data quality automatically with Business Domain Tests is imperative for ensuring the trustworthiness of your data assets.

Testing 169
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Why your CEO needs to watch a coding video

CIO Business Intelligence

By Bryan Kirschner, Vice President, Strategy at DataStax As a software developer and coding instructor, Ania Kubow is always informative and engaging. And vectorizing any data to power those apps–including unique-to-your organization pools of your customer interaction data, proprietary work product, open data, or all three–isn’t hard either.