Best Practices for Getting Started with Predictive Workforce Analytics
APQC and Talent Analytics, Corp. research features case studies from Cargill, Gap Inc., IBM, Johnson Controls, and SAS
(Houston, Texas – October 26, 2015) – While there is much buzz about the use of data and analytics in human resources, most organizations have yet to establish predictive workforce analytics programs or figure out how such efforts could benefit their businesses. APQC recently partnered with Talent Analytics, Corp., a leader in using advanced data science to predict employee behavior and performance, to illuminate the pioneering work and best practices of five organizations currently using predictive workforce analytics to enhance talent acquisition and management—Cargill, Gap Inc., IBM, Johnson Controls, and SAS. The findings are contained in APQC’s report entitled Getting Started with Predictive Workforce Analytics.
Through structured interviews, the research team focused on the practices of the five organizations in implementing predictive workforce analytics and found that the practices are key for conducting any workforce analytics project, even if not predictive in nature. The early-adopter companies interviewed use predictive workforce analytics for such efforts as selecting job candidates who have the most potential to develop into high performers; identifying when critical employees are at risk of leaving the organization; and using social media to get a real-time understanding of employee engagement. The best practices identified include:
- Purpose—Articulate a vision for why your organization is adopting predictive workforce analytics as a business approach.
The early-adopter organizations did not speak about making a large business case before getting started with predictive workforce analytics. However, each did talk about having clearly articulated a vision for why their organization was adopting predictive workforce analytics as a business approach. Essentially, each had answered the broad, long-range question: Why predictive workforce analytics at our organization? Upon these broad, long-range visions, the early-adopter organizations craft short-term predictive workforce analytics plans. Typically, these are yearly plans to conduct discrete predictive workforce analytics projects that have a strong probability of success.
- Resources—Secure the specific resources necessary to carry out your organization’s short-term predictive workforce analytics plan.
The early-adopter organizations did not mention making large financial outlays to get started with predictive workforce analytics. For the most part, interviewees did not talk about supporting predictive workforce analytics with significant investments in technology or new staff. However, they did underscore the importance of securing the specific resources needed to carry out their well-defined predictive workforce analytics plans. Each articulated and then located critical predictive workforce analytics skills. Next, they assembled these skills into formal, dedicated predictive workforce analytics groups.
- Problems—Select predictive workforce analytics projects in response to true business challenges that your organization faces.
The early-adopter organizations do not conduct predictive workforce analytics because they see other organizations doing this work. Instead, their predictive workforce analytics projects arise out of true business problems. Moreover, their analytics projects are only predictive when predictive is the most appropriate method for answering the specific business problem at hand.
- Data—Don’t wait for perfect data before getting started with predictive workforce analytics. Assemble and validate data according to the requirements of your short-term predictive workforce analytics plan.
The early-adopter organizations have a common long-term goal to establish clean, organizationally consistent, and centrally-stored workforce data. Some of the early-adopter organizations started work on this goal years back and have made significant progress. Others are still in the beginning stages of data integration. One commonality among them all is the decision not to wait for perfect data before getting started with predictive workforce analytics. Instead, the early-adopter organizations assemble and validate workforce data on a per-project basis.
- Education—Educate end users about the basics of predictive workforce analytics.
The early-adopter organizations devote significant time to educating end-users about predictive workforce analytics. During projects, they present incremental results and solicit user feedback. Post project, they extensively socialize findings sharing consumable amounts of information, often in the form of a story. At all times, they provide varying levels of predictive workforce analytics education to HR and other areas their organizations.
- Outcomes—Measure and share the outcomes of your organization’s workforce analytics efforts.
All of the early-adopter organizations measure the results of their predictive workforce analytics projects. The stories and visuals they create with data promote action which they closely track as a key outcome of analytics success. Any positive outcomes that arise, they deliberately publicize in order to build the business case for continued investment in predictive workforce analytics.
“A lot of companies haven’t gotten started with predictive workforce analytics because it seems like such a daunting task,” said Elissa Tucker, Human Capital Management research program manager for APQC. “What we found in this research is that there is no reason to wait until everything is perfectly aligned or your data is complete and fully scrubbed. Rather, pick small, containable projects that enable you to apply predictive analytics to specific HR issues. In addition, we believe that the foundational practices for success in predictive workforce analytics engagements can provide similar benefits for other analytics projects.”
"This research again shows that organizations are embracing predictive analytics as an approach to solve complex workforce challenges. Business ROI will continue to add to the momentum. It’s no longer a question of “if” predictive analytics will be utilized for workforce challenges, the question is, when. Notable in this research was the discovery that predictive workforce projects do not require prior progress through all levels of analytics maturity or perfect data," said Talent Analytics CEO Greta Roberts.
APQC helps organizations work smarter, faster, and with greater confidence. It is the world’s foremost authority in benchmarking, best practices, process and performance improvement, and knowledge management. APQC’s unique structure as a member-based nonprofit makes it a differentiator in the marketplace. APQC partners with more than 500 member organizations worldwide in all industries. With more than 40 years of experience, APQC remains the world’s leader in transforming organizations. Visit www.apqc.org, call +1.713.681.4020, or follow @APQC and learn how you can Make Best Practices Your Practices®.
About Talent Analytics, Corp.
Talent Analytics, Corp. uses data science to optimize employee performance and attrition for high volume, individual performer roles including Call Center Reps, Insurance Agents, Sales Reps, Sales Engineers, Bank Tellers and the like. Much of our work predicts top and bottom performers pre-hire. Our predictive scoring algorithms are beautifully and easily deployed into the employee sourcing, recruiting, hiring and operations processes via our award winning cloud platform, AdvisorTM.
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