Q: How is a specific line of business / business unit using your predictive decisions? How is your product deployed into operations? A: A product that we created called the Talent Index was shared with our senior leaders earlier this year, and the results contributed to goal planning and people management focus for 2017. The Talent Index is a tool we developed to measure how effectively our leaders are managing their organizations based on our core HR principles. It takes a comprehensive set of HR metrics, groups them into research-based factors, and produces a score through a series of weights and targets that reveals how closely they are aligned with our talent management practices. One aspect of the Index that helped it to be a success was the way it was designed. It was built with the end in mind, which was to give leaders a clear idea of where their people opportunities are. Leaders can look at their scores on the individual factors to identify what is driving their overall index score. Furthermore, they can look at the individual components within these sub scores to see what specific areas are drivers. This allowed our leaders to walk away with a very targeted idea of what they need to improve going forward, whether it be increasing engagement, providing more opportunities for high potential employees, or managing lower performers. Q: If HR were 100% ready and the data were available, what would your boldest data science creations do? A: HR has historically struggled with demonstrating the value of investing in people. If more data was available on employee preferences, attitudes, and day-to-day experiences, we could have a better idea of how employees are impacted by the organization. Then, if we have a better idea of how employees are impacted by the organization, we can connect this data to financial and operations targets and make a clear connection between people processes and ROI. This is already being done by some organizations, but not many are doing it well. This is still an area in which HR can make significant progress. Q: When do you think businesses will be ready for “black box” workforce predictive methods, such as Random Forests or Neural Networks? A: “Readiness” for these types of methods can vary between organizations based on their culture, resources, capabilities, and other factors. That being said, assuming the right systems are in place I think businesses are actually ready now. The utility of these methods is driven by the users’ ability to identify meaningful data, connect it to business-critical outcomes, and disseminate results to the movers and shakers in their organizations. In other words, if you use these methods for issues that are actually important to the business and you can articulate what your analysis means and why it matters, you can utilize more advanced workforce predictive methods. Q: Do you have suggestions for data scientists trying to explain the complexity of their work, to those solving workforce challenges? A: Early in my career I had a mentor ask me to explain a complex analysis “like I would explain it to my mom.” Now, my mom is very intelligent, but data science is not her specialty. The point was to consider the perspective of my audience. That has always stuck with me. Stay away from jargon and key words that are specific to the data analysis. You’re telling a story, so don’t be afraid to get creative. Make it interesting—use analogies to help explain your work when you can, especially if you know your audience and what would resonate with them. If you can’t avoid including complex terms or details, build up to these concepts by introducing key ideas one at a time. At the end of any presentation, conversation, etc., your goal is for the audience to walk away with the 2-3 key points. Highlight these key points early on in your discussions—don’t keep the audience guessing or lead them down a winding path. Q: What is one specific way in which predictive analytics actively is driving decisions? A: Predictive analytics is taking the guesswork out of solving workforce challenges. It is reducing the negative impact that results from bias and decision making based on emotions and/or opinions. In HR at CenturyLink, analytics is core to decision making especially for strategic decisions that have a big impact. We’ve leveraged analytics for identifying new engagement initiatives, changing workforce policies, validating our performance process, predicting successful hires, and predicting turnover among other workforce trends. Q: How does business culture, including HR, need to evolve to accept the full promise of predictive workforce? A: One of my favorite books that came out last year is called the “The Rise of HR” by Dave Ulrich, William Schiemann, and Libby Sartain (editors). A chapter written by Seth Kahan offers “12 predictions for a new world,” which proposes the challenges HR will be up against in the future. One of Seth’s predictions is that knowledge execution will become one of the most valuable assets in the world. According to his prediction, the ability to execute on knowledge will be more important than profitability, politics, and other powerful influences. This directly applies to how organizations need to evolve to accept the full promise of predictive analytics. Data has never been more accessible to organizations, and predictive analytics allows us to use this data to obtain knowledge that hasn’t been available before. Businesses that want to be successful in the future need to put predictive analytics at the epicenter of strategy and fully commit to making decisions based on these insights rather than biases and intuition. In an ideal state, predictive analytics is a central part of strategic decision making by connecting data across multiple business units.