Analytics on HR Analytics: What Really Works

As in other fields, HR professionals often seek new shiny objects (ideas) that will help them better perform. For these new insights to shift from distractions to sustainable value-added practices, they need to be examined more rigorously.

Analytics has become a recent elixir that is believed to enable HR professionals to be effective. The analytics panacea shows up in conferences, speeches, and books intended to guide HR professionals. Before jumping on the analytics bandwagon, performing analytics on analytics will help determine what really works.

As discussed in other articles (Have HR Professionals Made Progress? The 30-Year Evolution of HR Competencies and The 2017 HR Competency Study & What It Means For You) and in the book Victory Through Organization, we have collected data with 22 global regional partners on 4,000 HR professionals as assessed by 28,000 associates in 1,200 businesses. We collected information on 123 competencies of these 4,000 HR professionals and 30 characteristics of the 1,200 HR business units. 

Impact of the Individual HR Professional Analytics Competence on Outcomes

For the 4,000 HR professionals, the 123 individual competencies were factored into nine competence domains. We then assessed the impact of these individual competencies on three outcomes: [1] personal effectiveness, [2] stakeholder value, and [3] business performance (a six-item index of business results). One of these nine domains was “analytics designer and interpreter” and included the following eight specific competencies an HR professional might demonstrate:

• Identifies important questions about the organization that can be answered with data
• Uses data to influence decision making in $ORGUNIT$
• Translates data into useful insights for $ORGUNIT$
• Identifies $ORGUNIT$'s problems that can be solved with data
• Effectively uses HR analytics to create value for $ORGUNIT$
• Understands the limitations of data in ambiguous situations
• Accurately interprets statistics
• Excludes low quality data from decision processes

We then regressed this “analytics” domain on the three outcomes with the noted results: 

1. In predicting individual effectiveness, analytics is the 6th (out of nine) most relevant competence (explaining 8.2% of overall individual effectiveness). 
2. Likewise, knowing analytics has relatively low impact on stakeholders, including customers (10%), investors (11.4%), communities (7.6%), regulators (12.8%), line managers (8.4%), and employees (negative 6.8%). 
3. In delivering business value, analytics is the 7th most important competence (8.8%). 

For individual HR professionals, knowing analytics has less impact on all three outcomes than other HR competencies (like “strategic positioner,” “credible activist,” “paradox navigator,” or “culture and change agent”).

Impact of HR Department Analytics Capability on Outcomes

For the 1,200 HR departments, we collected data on 30 characteristics, which factored into four capabilities (employee performance HR, integrated HR practices, HR analytics, and information management). The HR analytics capability had four items (the extent to which an HR department):

• Measures and tracks HR performance
• Measures the impact of HR actions on business outcomes
• Uses HR analytics to improve decision making
• Effectively utilizes HR analytics to drive (ORGUNIT)’s business performance 

We were then able to determine the relative impact of these four capabilities on overall business performance and key stakeholders. As shown in table 1, the HR analytics capability has modest impact on business performance (17.3% of the total impact), positive significant impact only on regulators as stakeholders, and negative impact on employees. Integrated HR practices has positive impact on line managers and employees inside the firm, and HR information has impact on customers, investors, communities, and regulators outside the firm.

Table 1: Relative Impact of HR Department Activities on Different Stakeholders



(NOTE: The rows sum to 100%, representing the percentage of explained variance in the model that can be explained by each factor category.)

Implications of These Findings

The analytics on HR analytics suggests that this new HR elixir has not yet fully achieved the stated or desired impact for either HR professionals or HR departments. HR professionals who can do analytics are not being seen as more effective, delivering greater value to stakeholders, or creating greater business impact. HR departments with an analytics capability are not delivering business results or stakeholder value as much as they would with other department capabilities. 

These are remarkable findings. As we have pondered and explored them, we have a number of observations for how to think about and do analytics that turns the lure of the idea into a sustainable and value-added HR practice.

1. Analytics matter. No one can deny the importance of rigor and analysis to improve decision making. It is axiomatic that decisions made on evidence more than intuition are much more likely to be effective. 
2. Focus analytics on the right issues. In statistics, a type 3 error is when the wrong issues are being studied or understood. For example, statistics could show that storks create babies because the correlation between the number of storks and birth rate is high. But both are caused by a third variable: the number of storks and birth rates are both correlated to being in rural areas versus urban areas. In HR, we would suggest that there are four stages of analytics: [1] HR scorecards to [2] HR insights to [3] HR interventions to [4] business impact. Much of the (low impact) HR analytics work is likely done in the first few stages, with HR creating scorecards and/or insights based on big data (what we would call a type 3 statistical error). These analytics do not start with or explicitly connect with business impact. As colleague Dick Beatty says, “the scorecard of HR is the business scorecard.” Any assessment of an HR practice should begin with and show impact on a business result that matters (e.g., customer, investor, community value, or business financial result). As we note in table 1, it is interesting that focusing on information has dramatically more impact on stakeholders than doing HR analytics. Getting the right information to the right people to make the right decisions has more impact than analytics per se. 
3. Manage expectations. Perhaps HR professionals being traditionally stereotyped as non-analytical or evidence-based gets in the way of using analytics for business impact. Previous expectations get reinforced. For example, Robin Williams, a Julliard–trained actor, was often typecast in his original Mork role (e.g., Good Morning Vietnam). HR professionals need to overcome expectation biases by being even more capable of linking their work to business results. 
4. Build analytics on previous research. HR analytics are not new. HR research is over 50 years old, with hundreds of studies showing the impact of HR on many individual and business outcomes. Science does not advance with isolated studies but with a series of studies, each building on each other to create more rigorous and robust insights. In HR, studies too seldom build on previous work. HR analytics are too often isolated events rather than cumulative building blocks. A book, article, presentation, or firm announces new insights that do not take into account what has been done and how to build on the base of research. These studies are like sandcastles, too often washed away at the next high tide rather than built on the solid foundation of cumulative research. 

For HR analytics to evolve from being a quick-fix elixir to having sustainable impact, HR needs to focus on the right issues in the right ways. Sustainability comes when HR is not about HR but about stakeholder and business impact, and when each individual study becomes a puzzle piece of a larger HR mosaic.

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