Thomas C Redman
Author
Publisher
MIT Sloan Management Review
Pub. Date
2023.
Language
English
Description
At many companies, gaps in organizational structure get in the way of data science success. A new type of role, connectors, can bridge those gaps to help line-of-business and data science professionals work together better and deploy more projects. This article examines examples of organizations that created connector roles and explores the management challenges for this valuable group of professionals.
Author
Publisher
MIT Sloan Management Review
Pub. Date
2022.
Language
English
Description
There's enormous potential for new digital capabilities to improve business performance -- if business departments can start to trust their IT counterparts. Leaders can take an active role in helping their teams of executives and IT professionals better respect each other's skills. Only when that happens can they begin working together in more productive ways to improve business performance and create more satisfying jobs.
Author
Publisher
MIT Sloan Management Review
Pub. Date
[2024]
Language
English
Description
Today's AI developers struggle to predict which algorithms will work. AI lacks a basis for inference: a solid foundation on which to base predictions and decisions. This makes AI tough to explain, creates mistrust, and dooms many AI models to fail in deployment. However, help for AI teams and projects is available from an unlikely source: classical statistics. This article explains how business leaders can apply statistical methods and engage statistics...
Author
Publisher
MIT Sloan Management Review
Pub. Date
[2022]
Language
English
Description
Artificial intelligence has been applied successfully in thousands of ways, but one of the less visible and less dramatic ones is improving data management. The authors describe five common areas of data management -- classifying, cataloging, quality, security, and data integration -- where they see AI playing important roles. They also discuss the vendor landscape and the ways that humans are essential to data management.
Author
Publisher
MIT Sloan Management Review
Pub. Date
2023.
Language
English
Description
Today's senior business managers have the power - and the responsibility - to prevent AI project failures. But in order to do so, they need to know how to evaluate the data sets and models being used. This article offers a framework for identifying the right data set for your business problem and suggests six tough questions to ask developers before and during the deployment of artificial intelligence models.
Author
Publisher
MIT Sloan Management Review
Pub. Date
2020
Language
English
Description
The United States has had many problems coping with the coronavirus. A critical - and underappreciated - problem is bad data: Without good data, planners can't plan, epidemiologists can't model, policy makers can't make policy, and citizens don't trust what they're told. The U.S. needs a robust program, with professional management of the data supply chain, to develop trustworthy data about pandemics and other public health crises.
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