Prediction: People analytics will augment the workforce and the workplace


Posted by Kathi Enderes on December 4, 2018.

Ninety percent of the data in the world has been created within the last two years alone, and the continued emergence of new technologies will likely increase that rate even more. HR leaders have been attempting for years to use people analytics to turn this vast amount of data into actionable insights, but many still struggle with how and where to apply people analytics to maximize the return on investment. In the coming year, more and more organizations will start to apply people analytics in a new way, with a direct focus on the individual, rather than through HR or leaders—a bottom-up approach, as opposed to just top-down.

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Prediction: Data will reveal relationships that ignite productivity


Posted by Timothy Davis and Jeff Mike on November 30, 2018.

Last year we noted that organizations were in the early stages of adopting Organizational Network Analysis (ONA) tools to measure followership and mentorship, as well as help leaders understand how teams and the company as a whole communicates.1 In 2019 we will see organizations transforming through the use of ONA, revealing the informal linkages that show how work actually gets done.

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Prediction: Organizations will rethink work for a more productive workforce.


Posted by Kathi Enderes on November 29, 2018.

The world of work has become incredibly complex. Workers are trying to navigate a maze of hierarchies, work processes, and never-ending new communication methods that are all meant to make them more productive, but ultimately having the opposite effect. The rise of the social enterprise means that organizational boundaries are becoming permeable, while what and who constitutes an “employee” will be redefined with broader, more inclusive concepts. And teams are rising to the fore as work processes become project-based, even as many organizations cling to industrial-age hierarchies.

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Putting big data to work

Organizational Network Analysis helps reveal insights “hiding in plain sight” in untapped email data


Posted by Greg Szwartz and Nikola Andric on November 13, 2018.

We recently had the opportunity to work with a leading global Life Sciences company to leverage some of its “dark” email-based metadata. This is data that companies generally don’t tap into, let alone turn into valuable information. What we found yielded surprising insights into how the organization and its people work and interact. This knowledge can now be applied to fuel an insights-driven High-Impact HR operating model—with a more systematic and quantified perspective on ways to boost new employee success, reduce turnover, and lift the overall productivity of the entire organization. Here’s how it happened.

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Upping your game: From functional experts to business advisers

Learning Measurement Part 1

Posted by Erin Clark , David Fineman, Praveen Kaushik, and Mariana Aguilar on August 6, 2018.

For many businesses, our behaviors as consumers—our clicks, our scans, our searches—drive decisions about how, where, and when to place certain information in front of us. And that placement, determined by our own behaviors, can result in higher sales and greater revenue per advertising dollar for many of those businesses. Data and the analytics horsepower that yield these kind of insights are quickly becoming table stakes for the way most organizations interact with and engage with customers. A business strategy without a data strategy is often an indicator of a company’s naiveté,1 and many are becoming data-driven businesses. Why, then, do learning leaders often continue to struggle with the age-old battle of evaluating the value of learning to the business? Why do they continue to find it so difficult to justify increased investment? The answer: They are thinking about the problem all wrong.

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How well are you managing workers’ compensation claims?

The right analytics approach could yield substantial savings

Posted by Matt Carrier and Frank Zizzamia on May 17, 2018.

The “80/20” rule is commonly applied to workers’ compensation claims: namely, that 80 percent of the cost comes from 20 percent of the claims. What if you could consistently predict whether a claim would fall in that costliest 20 percent so you could handle it in a way that mitigates the risk?

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Upskilling HR in People Analytics


Posted by Madhura Chakrabarti on March 20, 2018.

People analytics has rapidly become an essential tool for improving individual performance, enhancing employee experience, and achieving business goals. But while many companies now have people analytics teams in place, HR practitioners are not all conversant with data or analytics yet. In fact, in nearly 60 percent of companies, basic data literacy skills are not yet in place across the HR organization.1

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Is your contingent workforce program becoming obsolete?

Posted by Brian Proctor, Kathryn Charlton, Dana Flynn-Rea and Dave Yerks on October 4, 2017.

Your organization, like most of those we see, is probably already incorporating contingent workers in your talent mix, and likely seeing year-over-year increases in the number of contingent workers in your workforce. In Deloitte’s 2016 Global Human Capital Trends report, 51 percent of global executives surveyed said they plan to increase or significantly increase the use of contingent workers in the next three to five years, with only 16 percent expecting a decrease.1 But, even if you think your contingent workforce program has been up and running efficiently, it’s important to revisit it regularly. The contingent market is evolving rapidly, and keeping your program on autopilot could risk it falling far behind.

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Using people analytics to improve health care performance

Using people analytics to improve health care performance

Posted by Brian Augustian on August 18, 2017.

Health care providers tend to lag other industries in the adoption of new business processes and technologies, and we find this to be consistent in the industry’s use of big data and analytics to improve organization performance. This is likely due to a number of factors, including the nature of health care work, tight financial margins and limited funds, and historically conservative cultures. Yet, the successful use of data analytics in other industries could indicate that it’s time for health care systems to up their game.

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