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Score the Best Data Protection Solution for Your Organization

CDW Research Hub

Every year companies invest resources on data collection, and employees spend countless hours mulling over that data to make informed business decisions like how to market to their target audience or where to expand the business. Will just any data protection solution get the job done? Know your security risks.

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Human-centered design and data-driven insights elevate precision in government IT modernization

IBM Big Data Hub

A pain point tracker (a repository of business, human-centered design and technology issues that inhibit users’ ability to execute critical tasks) captures themes that arise during the data collection process. The pain point tracker clusters the foundational data in which value metrics are then applied.

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Themes and Conferences per Pacoid, Episode 13

Domino Data Lab

Paco Nathan’s latest article covers data practices from the National Oceanic and Atmospheric Administration (NOAA) Environment Data Management (EDM) workshop as well as updates from the AI Conference. Data Science meets Climate Science. At the EDM workshop, I gave a keynote about AI adoption in industry.

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The Data Behind Tokyo 2020: The Evolution of the Olympic Games

Sisense

Not only does it support the successful planning and delivery of each edition of the Games, but it also helps each successive OCOG to develop its own vision, to understand how a host city and its citizens can benefit from the long-lasting impact and legacy of the Games, and to manage the opportunities and risks created.

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Your Effective Roadmap To Implement A Successful Business Intelligence Strategy

datapine

Improved risk management: Another great benefit from implementing a strategy for BI is risk management. Before going all-in with data collection, cleaning, and analysis, it is important to consider the topics of security, privacy, and most importantly, compliance. Think of security, privacy, and compliance.

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Themes and Conferences per Pacoid, Episode 9

Domino Data Lab

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. He talked about the risks of reductionism and Descartes’ questionable legacy. Ergo, less interpretable.

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Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

For this demo we’ll use the freely available Statlog (German Credit Data) Data Set, which can be downloaded from Kaggle. This dataset classifies customers based on a set of attributes into two credit risk groups – good or bad. 1 570 0 570 Name: credit, dtype: int64. References. Explainable planning.

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