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Author's profile photo Mick Collins

Future-Proofing HR – Big Data, Big Brother, and Big Money: An SAP SuccessFactors Survey on Workforce Analytics

Most published surveys on the use of workforce analytics in HR tend to focus on the repetitive “why analytics?” and “why now?” Often, the results are predictably bland – most HR leaders agree that analytics is important and should be done better, answers that offer little insight and practical guidance into the specifics of how these organizations can improve their capabilities.

In creating a survey for the recent SAP SAPPHIRE NOW conference in Orlando, we (a combination of Industry Value Engineers and members of the SAP SuccessFactors Workforce Analytics team) wanted to look differently at the kinds of complex issues facing HR today – such as artificial intelligence and data ethics – as well as going beyond the headlines to see what kinds of myths or false assumptions leaders’ hold about their people.

Survey Background:

The study was conducted by SAP SuccessFactors during SAPPHIRE, held in Orlando, FL in May, 2016. Additional responses were gathered via the SAP SuccessFactors Workforce Analytics customer Jam group, and from Twitter. Participants represent HR, IT, and business leaders and are drawn from 68 organizations across 20+ industries.

Artificial Intelligence in HR

When asked “Do you currently use, or plan to use, artificial intelligence (AI) to automate an HR process? Examples might include software that manages volume recruiting processes or that administers onboarding activities”, only 10 percent of respondents said that they did currently apply artificial intelligence to a talent management activity.

This seems fairly low, but is there is no doubt in my mind that artificial intelligence – the development of computer systems able to perform (or automate) tasks that typically require human intelligence – will soon become much more prevalent in HR and talent management. Leading candidates for AI may be in Recruiting (where machines trawl data to identify passive candidates) and Learning & Development (where systems make recommendations on optimal career paths).

Data Ethics and Policies

Recent changes to the Safe Harbor framework for privacy principles and greater availability of tools to capture employee data (wearables, instant feedback mechanisms, etc.) are prompting HR and legal departments to take a fresh approach to data privacy. We asked “do you have a policy regarding the ethical/moral use of employee data? Examples might include sharing data on an employee’s mental health or analyzing keywords in email conversations.”

A full 60 percent of respondents do maintain a written policy on the ethical and/or moral use of employee data, consistent with our view that the democratization of data will force HR to re-evaluate how it collects, stores, and disseminates sensitive information. On the other hand, employees will expect to benefit from the data they provide; HR must seek out valuable new insights while managing the big brother “creepiness” factor.

Revenue Maximization Models for Talent Investments

Turning attention to HR’s adoption of other functions’ frameworks, we inquired about “which non-HR business model could most improve talent management decision-making? The top-ranked model was Revenue Maximization – Investing in the selection, development, and compensation of high-value employees that generate above-average results. Others receiving votes included customer segmentation, supply chain optimization, and the break-even framework.

In the context of talent management, Revenue Maximization means investing in the selection, development, compensation, and retention of high-value employees that generate above-average results. Too often, talent management practices over-reward average, but loyal, employees, with little correlation to business results. Applications such as those offered by SAP SuccessFactors, will help with this, by linking high-performer data across our Recruiting, Learning, Development, Compensation, Succession, and Workforce Analytics modules.

Being Retired on the Job

In my view, analytics can be harnessed to provide visibility and transparency into talent issues that fly beneath the radar. A lack of visibility into hidden costs or labor flow inefficiencies is a cause for concern. While many HR functions lead with analytics on recruitment and retention (highly visible, high-volume terrains), greater returns may be realized by studying problems that are less well-known but very expensive (e.g., contingent labor spend or cost of unscheduled absence).

As such, we asked “which “low visibility” areas of talent management are you most concerned about, in terms of negative impact on the business?

Full results are available in the survey report, but the most popular choice was that of “employees who are retired on the job”.  In the quest to boost overall engagement, firms may be overlooking one of the biggest threats to organizational capacity and agility.

Confronting Outdated Myths

Beyond the data, I always love to hear the personal stories about how analytics is changing the way HR does business and how leaders think about their workforce. Asking “what assumptions, or myths, about your workforce do you believe to be outdated or incorrect?” may suggest priorities for data investigation as well as shine a light on some of the fallacies that HR is seeking to overcome.

As you might imagine, respondents suggested a range of myths, from the seemingly intuitive (“attrition is always a bad thing”) to the more offbeat (“Excel spreadsheets work”). In general, however, the most common myths surround higher pay as a motivator of engagement, performance, and retention.

I’d love to hear your views on these questions and the findings we obtained. Please comment below.

You can read a summary of the results in this cool infographic. Contact me to receive a copy of the full study.

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