By Scott Mondore, Ph.D.
Research cited by Forbes estimates that more than half of companies sampled (over 60 percent) are investing in big data and predictive analytic tools for use in guiding human resources decisions.i Problem is, not all analytics are created equal. Not all technologies can measure the specific ROI of HR investments—or make the results actionable for front-line leaders.
This post provides an overview of how big data and predictive analytics may be applied to HR, as well as a few considerations of what analytics are and are not. To move the business needle, keep these points in mind before enlisting an assessment/analytics vendor or conducting analysis in-house.
Although often associated with complex analysis, big data is actually a simple concept – it is the collection and accumulation of numerous pieces of information that can be used to uncover connections between various concepts. For HR, these concepts may include employee behaviors, attitudes, skill or knowledge levels, performance metrics, turnover data, and much more. The ways the data accumulates can range from manual, such as the deployment of a selection procedure, to entirely automatic, such as machine scanning for resume selection. Yet, even manual systems are less manual today as they are inevitably aided through technological advancements (e.g., job knowledge assessment taken, scored, and stored electronically). Although the concept of big data is not new, the surge in the collection, administration, and accumulation of data has grown exponentially with advances in technology.
Despite the fact that it is possible to collect more and varied kinds of information, the collection itself is not that interesting or useful to organizations. For example, consider an organization that wants to decrease turnover in the coming year. Using attrition data gathered from HR, one can calculate how many individuals left the organization last year and the goal set for this year. But, without including other data inputs, not much else beyond examining baseline numbers can be done. Consequently, the real utility of big data comes when it is used in predictive analytics – the ability to show cause and effect links to real business outcomes. For example, if an organization is interested in identifying the key drivers behind employee turnover, predictive analytic methods can be used with data gathered from an employee survey coupled with HR data on employee turnover to determine which employee attitudes are most strongly linked to employee turnover (e.g., satisfaction, job fit, perceptions of managerial support). This allows organizational leaders to know which levers to pull to see the greatest impact on reducing employee turnover. In other words, predictive analytics, such as structural equation modeling, can identify which attitudes are “causes” of employee turnover.
Questions to Determine Real Analytics for HR
- Are the analytics you’re being provided limited to slicing and dicing HR data? Keep in mind that data visualization adds little value if there is no diagnosis.
- Are the analytics true cause-effect (not correlations!) and predictive of real business outcomes (not Engagement!)?
- Are the analytics actually reported and actionable to all front-line leaders (not just displayed in sleek corporate PowerPoint presentations)?
- Is an actual business impact being shown? Yes; real ROI.
Guiding Principles for Business-Focused Metrics
- There are no magic metrics that work for everyone.
- Every element on the scorecard must be directly linked to business outcomes.
- HR Efficiency Metrics are fine for Internal HR tracking but not for senior business leaders.
- HR Metrics must be predictive.
- For every metric you should be able to answer yes to these questions:
o Can I articulate why this really matters to the business?
o Do I know what a good number should be?
o Can I articulate the business value of moving this number up or down?
o Why would senior and front-line leaders care about this metric?
The Benefit: Competitive Advantage
HR must begin understanding and adopting true predictive analytics. Big data coupled with predictive analytic capabilities can provide a myriad of benefits to organizations—if done correctly.Whatever the organizational goal (e.g., profit, reduced turnover, improved hiring success, increased ROI of your HR initiatives), predictive analytics will not only allow for the identification of key drivers, but will also allow for their prioritization, making planning and resource allocation straightforward and giving a company a massive competitive advantage—the quality of people.
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Dr. Scott Mondore is co-founder and managing partner of SMD, an HR technology company with the only patented assessment tools that link data to business outcomes with customized action planning tools. He is an executive, turnaround-CEO, technology innovator and best-selling author and speaker with over 17 years of experience in the areas of HR technology, analytics, strategy, talent management, measurement, sales and customer experience. He is the co-author of two best-sellers: Investing in What Matters: Linking Employees to Business Outcomes (SHRM, 2009) and Business-Focused HR: 11 Processes to Drive Results (SHRM, 2011). He has also published numerous articles in prestigious business and psychology journals in the areas of leadership effectiveness, employee litigation, assessments, and employee safety. Scott recently won the HR People + Strategy Walker Award for "best advances in state-of-the-art HR practice."
Find out more about Scott at http://www.smdhr.com/scottbio.pdf
Follow Scott on Twitter at @ScottMondore
[i] Bersin, J. (Oct. 2013). Big data in Human Resources: A world of haves and have-nots. Forbes. Retrieved from http://www.forbes.com/sites/joshbersin/2013/10/07/big-data-in-human-resources-a-world-of-haves-and-have-nots/