Research

For most companies, their current pre-hire talent assessments are wasted data. Results are delivered in an individual report that cannot be analyzed or aggregated. For most “legacy” talent assessments, it’s difficult or impossible to determine what positive (or negative) business effect the assessments are having. It often comes down to the question of “how much the HR person believes the results.” This is a bad measure of success. But it doesn’t have to be that way.

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Is your organization doing all it can to modernize your data collection and analytics processes? Barely a decade ago, networks like AMC had virtually no information on consumers. Today, they are able to capture information at a level not possible until very recently.

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We only need the first and last letters to decipher more than half of words in the English language. This has implications for game shows, and more importantly, the business world.

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Much like the Fibonacci sequence appears repeatedly in nature, there are recurring patterns in data that, once recognized, can improve both our analytics and our efficiency in creating them.

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HR leaders can learn a lot from the experiences of MLB teams and sabermetrics. Teams who successfully use these tools have been able to field better teams, recognize true contributors, identify top performing teams, and understand the really contributing factors to better performance. Those are the same goals our organizations have. Ultimately, it’s about bringing the right people to the right roles and the right time—for baseball teams and corporate teams.

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Perhaps more than any industry, healthcare analytics inspires a sense of purpose among data-evangelizing types like myself. When transactional data involves steps in disease treatment, and personalization refers to the individual’s experience in a clinical setting, it’s easy to get caught up in the many data-driven approaches to saving lives and improving outcomes.

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How to Help Humans Work Better With Smart Machines

By Thomas H. Davenport, May 25, 2016

Perhaps the most important leadership issue is preparing your employees for roles in which they augment smart machines, and vice-versa. There will be new jobs involving implementation and oversight of these technologies—getting them installed, monitoring their daily performance, and improving them over time. Employees with some aptitude need to be groomed for such roles.

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How artificial intelligence explains analytics

By Kris Hammond, May 19, 2016

No one would ever work with a person that just spits out answers and then walks away. Why would we expect people to work with intelligent machines that do any less?

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Corporate recruiters have a very important and difficult job. They predict who will be a top performer in certain roles, and protect against non-performers getting inside the business ecosystem.

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We’re all generating a lot of data about ourselves and how we live day to day. There are personal fitness devices, preferences and opinions expressed on social media, details on when we’ve come and gone from the house from our security systems, and more. It isn’t just data that companies are collecting from us, but data that we are directly generating ourselves. What should we be willing to do with it and at what price?

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