Interest in an evidence-based approach to decision-making in HR is booming, but HR as a whole is still learning how to set up an effective analytics function. Contributing to the lack of adoption are two key challenges and barriers persisting across the industry.
First, HR only recently has been staffed by professionals focused on the business consequences of HR actions, rather than on policy, procedures, and people. The second roadblock has to do with budget. There has been a struggle to show the return on investment that CEOs expect in order to justify the capital expenditure on analytics.
These are two very stubborn obstacles, leading to claims that we’re in for a long ride before HR reaches the type of analytical capability we see in other business functions, such as marketing and sales. However, there is genuine cause for optimism concerning the speed of workforce analytics adoption in the near future.
Reason for Optimism
Just a few years ago, developing analytical capabilities was prohibitively expensive. Early adopters in the finance and marketing industries had to deal with high deployment costs, but that situation is no longer relevant. Today, because of cloud and Software as a Service (SaaS), organizations can purchase the required technologies on a modest budget.
HR departments also are gaining competence in the realm of analytics much faster than was the case for any of the other functions, because they are learning from that groundwork. Since other business units already have navigated and documented the business case challenges, HR can reduce implementation time. As an example, customer dissatisfaction leading to customer churn has analogous concepts and relationships in HR —in this case, employee engagement and staff turnover.
Finally, newly emerging professional HR degrees and qualifications focus specifically on analytics, meaning any historic lack of analytic capability won’t exist much longer.
While these are all positive developments, in order for analytics to become a viable function, HR must systematically plan for success. The first 100 days of any effort is critical to success, so we’ve constructed a framework that can apply to that window of time to help organizations establish HR analytics capability.
Phase 1: Setting Direction
Before even identifying data sources, which often is perceived as the obvious first step, it is critical to articulate the objectives for your HR function. Without knowing the vision for analytics in HR, the developmental path is likely to wander without direction.
Prioritizing projects that will lead to a quick win is an important aspect of this initial phase. A quick-win project is one that affects at least one portion of the business, or uncovers an important insight to stimulate executive discussion. The quick win will establish credibility, and make it easier to secure further funding for projects that require more significant change management and deliver even greater value.
The establishment of a governance model also should be linked to this objective setting. It specifies how your function will interact with stakeholders and will lay out legislative constraints and privacy practices.
Phase 2: Defining Your Approach
Now we get to the data. While it’s not necessary to wait for perfect data to make decisions, there’s little point in analyzing data known to be inaccurate. So, key to the second phase is understanding what data you have and the quality of that data. In some cases, it may be better to collect a small, new data set rather than analyze a big existing data set that doesn’t contain the necessary variables or cases to answer your questions accurately.
Overall, we recommend a pragmatic approach to data quality in HR. In some instances, even if a set of analyses is not perfect, as long as it’s sufficiently valid and prompts rigorous discussion of alternative courses of action, it serves a useful purpose.
Another key aspect of this phase is deciding your third-party support requirements and technology options. In terms of third-party support, the options are usually to insource, outsource, or partner with external providers. For smaller organizations, the most common option is to outsource or partner, while larger organizations are better positioned to develop an internal capability.
Phase 3: Growing Your Capability
Regardless of how you structure third-party and internal support, establishing an internal HR analytics leader is crucial. The best leaders don’t need to be the best statistical analysts, even though that is certainly an asset. Rather, consider HR analytics leaders who have line-of-business and change management experience. These types of leaders are more adept at getting ideas in motion and building the political support required for HR analytics projects.
Key characteristics that make for a strong internal analytics team member include deep interpersonal and consulting skills, as well as analytics and data management capabilities. These traits are often difficult to find in the same individual, so most organizations succeed when they build a team with complementary skills.
Finally, we recommend you develop a business plan, adopting a consulting model approach that specifies milestones, dates, responsibilities, and precisely who your “customers” will be. This will help secure the resources you need and serve as a yardstick to measure success.
Following this three-phase framework and paying attention to lessons learned from other functions will help speed up the implementation of analytics in HR.
It’s our belief that HR has the potential not only to catch up with its functional peers, but to leapfrog them with insights into how the people in organizations directly link to improved business performance.
For more information on the HR analytics framework for the first 100 days, download the IBM Smarter Workforce Institute report: Building an analytically enabled HR function: The first 100 days
Nigel Guenole is a researcher with the IBM Smarter Workforce Institute and a senior lecturer in Management at Goldsmiths, University of London, where he teaches courses on leadership and statistical modeling. He is known for his work in workforce analytics and psychological measurement. Dr. Guenole is the current external examiner for organizational behavior programs at London School of Economics (LSE) and University College London (UCL). He is a Chartered Occupational Psychologist and an Associate Fellow of the British Psychological Society (BPS). He is registered with the Health & Care Professions Council (HCPC) in the United Kingdom, is a member of the Academy of Management (AoM), and is an international affiliate of the Society for Industrial and Organizational Psychology in the United States (SIOP).
