For years, human resources was expected to deliver only basic administrative tasks to businesses: Set up interviews, get the appropriate paperwork to selected candidates, pay people correctly, and when necessary, process terminations efficiently. David Ulrich called this administrative efficiency. It was the core currency for many HR professionals, and it earned them a seat at the table — but HR was, as the saying goes, seen but not heard.
Many HR people became quite proficient at tracking data on what occurred in the past. Turnover reports, who attended training, who passed compliance training — “rear view mirror” metrics were the domain of efficient HR organizations. But while this may be interesting information, it is not compelling in terms of measuring business impact or effect, nor can it help predict future activities or outcomes. Unfortunately, many HR teams simply don’t know how to move from reporting on what happened to predicting what might happen.
A significant part of this conundrum is the sheer amount of data that HR organizations collect, with little if any capability to meaningfully retrieve or analyze the data. In addition, due to the complexities of many organizations, it is challenging at best to make sense of this data. So much of what is transacted in the typical HR organization is done manually by different people on disparate systems.
The fundamental reality is that many HR professionals are not experienced in, and perhaps not even aware of, analytics and the ability to connect data and make predictive assertions. Unfortunately, this has the real risk of making HR irrelevant going forward. In a recent study of more than 1,000 CxOs done by Oxford Economics and sponsored by SAP SuccessFactors, the message was very clear: HR needs to measure the impact of HR programs on the organization’s bottom line and predict the success of talent acquisition, learning, and total rewards programs as they are implemented.
Many business functions are doing that today. In the world of healthcare there are solutions now that can aggregate data from wearables, traditional monitoring devices, and more sophisticated machines and provide an integrated, real-time view of a patient’s current condition. This allows the treating medical provider to understand the patient’s health over a period of time, not just in the doctor’s office. The manufacturing and transportation industries are using the immense data being collected by machines to predict maintenance and efficiency and ultimately lower costs to provide higher margins.
Why is it, then, that we in HR are still trapped in the era of using simple data that is easily collected, ignoring the vast data collected by performance management systems, talent acquisition systems, job portals, and external social media that would help us create a more holistic view of current and potential employees?
As an example, what if an organization could utilize a broader set of data to make decisions around who should be hired, promoted, or even recommended for additional training? I am not suggesting that we move to a mathematical algorithm to determine who our top performers are, but what if we could truly understand and identify top performers by analyzing social media or email traffic—not so much the content, as that has privacy issues—but who are the “hubs” of communication within the organization? Who do other employees go to more frequently? Who is active on social media and has a strong cadre of followers? Organizations where information flows freely are more likely to be seen as innovators and first movers. Those that hold information in silos underperform on most important metrics.
Understanding who these top performers are and how they work could lead to a better profile and selection process, resulting in hiring more people who exhibit the skills that lead to success in the business. These “connected” people are described by Malcolm Gladwell in Tipping Point as “mavens” or “information specialists.” They are the people others go to for information. These people are often at the intersection of interesting ideas and the execution of those ideas. This where innovation thrives in successful organizations.
As HR organizations are asked to do more with less headcount and budget, we must learn to encompass more machine learning into what we do every day. We no longer have the luxury of allowing valuable people to work on routine tasks. We need to be more sophisticated in how we approach delivering HR solutions, and we must be able to use data to not only report on past activity but predict future impact.
Another key area to apply machine learning is through the routine processing of people activities and changes inside the HR system of record. To address this emerging topic we at SAP SuccessFactors have introduced a concept called Intelligent Services. This offering will transform the entire concept of HR service delivery, moving from a series of individual and isolated self-services into end-to-end intelligent services that can easily cross software modules and integrate disparate processes.
For example, when someone goes on leave, even the most basic HR systems can suspend pay and notify benefits providers, but anything beyond that is up to a service center or HR administrator to update. What if the system automatically reassigns approvals to the next person in the organizational hierarchy without someone having to go into the system and make the necessary changes? The data is usually in the system already, but often it needs manual intervention. Through Intelligent Services, a trigger to the employee and manager can be automatically generated to potentially update goals and objectives rather than waiting until the end of the review cycle when valuable data could be lost.
The possibilities are endless and the value is priceless. Forward-looking HR organizations must start thinking about how to use data more effectively and become as comfortable predicting the future as many are at reporting on the past.
It’s time to look ahead so you don’t get left behind. Transform HR so HR Runs Live.