The Future of Business: Human Resources

"HR takes cues from marketing on Big Data"

The Economist Intelligence UnitThe Economist Intelligence Unit

eiu 5When people think of cutting-edge IT solutions for businesses, the transformation of human resources (HR) practices may not be the first outcome that comes to mind. Nevertheless, companies are discovering that harnessing the predictive analytical capabilities of Big Data is increasingly an essential component of improving the way they recruit, retain and track employees.

“People in HR will need to unlearn how they make decisions,” says John Sumser, HR industry analyst and editor of the HRExaminer Online Magazine. “The power of data will cause them to reconsider things that they thought had been answered once and for all. There are many things that today can be solved with data but that, until recently, had been considered impossible.”

Despite the past reluctance of many HR executives to embrace new technologies, recent indications suggest that HR execs are developing a strong interest in analytics and Big Data and are seriously considering these tools’ potential.

For instance, says Bill Kutik, a leading industry analyst of HR technology and a founder of the HR Technology Conference, “Two years ago, the Analytics 101 breakout session at the HR Technology Conference got the largest attendance of any of the 40 breakout sessions at the show. I really see HR coming around, and I see new analytics products being launched and the embedding of analytics in larger products that are making it easier for HR executives.”

Even as it moves to make greater use of Big Data, HR still has its work cut out for it. According to the findings of a 2013 study conducted by the American Management Association, human resources ranked last in analytical skills behind other key organisation functions—finance, executive team, operations, R&D, marketing and sales.

Learning from CMOs

Yet following the pack has its perks. Chief human resource officers (CHROs) can now benefit from looking to insights and lessons learned by chief marketing officers (CMOs) in marketing arena for a look at what lies ahead for HR.

Examples of CMOs’ successes in leveraging Big Data are many.

CMOs have particularly employed Big Data tools to help with the front end of the marketing process. Marketers use sophisticated Big Data models that draw on diverse sets of consumer- related data on characteristics and behaviour to create digital profiles for top customer prospects. New digital tools then use those profiles to identify, contact, communicate and convert prospective customers.

CHROs are now starting to use analogous approaches that draw on diverse data sources to help them create digital profiles for top employment prospects and then to reach out to recruit and hire the key talent that their organisations need.

Another important application of Big Data for CMOs that has been taken up by CHROs involves retention. In the same way that predictive modelling has helped CMOs get a jump on identifying  customers they are at risk of losing, CHROs are beginning to create digital models of employees who are at risk of leaving their organisations.

CHROs are also starting to gain access to analytic tools that can give them a much better understanding of the differential value created by individual employees. For example, one of the more intriguing HR developments arises as a variation on the increasingly sophisticated data analysis that has enabled CMOs to gain deeper and more accurate differential understanding of the lifetime value (LTV) that can be ascribed to individual customers. Marketers use LTV calculations as a tool for making decisions about the payoff of investing in acquiring and maintaining specific customers.

In a similar vein, CHROs are beginning to gain access to analytic tools that can give them much better understanding of the differential value created by individual employees.

Data, pay and the bottom line

“The long-time, historical, HR practice has been to treat everyone the same; that’s why there are pay grades,” says Mr Kutik. “Even if they are not manufacturing organisations, American organisations have continued to treat their employees as though they are industrial employees.”

However, says Mr Kutik, “HR is increasingly abandoning the idea of treating everyone the same. There is a growing understanding that there are people who need to be treated better, because they are better employees.” Using newly available analytic tools, CHROs are gaining a much better understanding of individual employees’ contributions and are moving to adjust pay schedules accordingly.

“[Better employees] make more money for the company or they do their job better or they are better leaders. HR is starting to get on board with that,” says Mr. Kutik.

These changes in pay policies are part of larger transition from managing an industrial workforce to managing a post-industrial workforce. In an industrial workforce, workers are seen as largely interchangeable parts, much like the interchangeable parts on an assembly line. A post-industrial  workforce, on the other hand, usually entails far greater variations in terms of the market value and the value-creation potential of each employee.

Formerly, the prevailing thinking had led companies to consider employees as cost centres; companies’ key objective was often to hold down labour costs. Today, says Mr Kutik, “The smart employers consider employees to be profit centres—and you want to invest in the profit centre for it to make you more money.” Big Data tools that incorporate financial and performance metrics will play a key role in making it possible to apply those kinds of approaches to the management of employees. For example, predictive algorithms can proactively identify high-potential employees, help design training programmes that are most effective in increasing productivity and help forecast the effects of bonuses and promotions.

In the past, “Data was really expensive, so you had to have a good hypothesis before you would start looking at data,” says Mr Sumser. “What you were taught in school is that you start with a question that is tightly defined so that the data that you need to get in order to answer the question is narrowly defined and therefore affordable.”

In contrast, the torrent of data that is now being generated has increased the volume, variety and velocity of data that are readily available. And, at the same time, costs are plunging for the capture, storage and analysis of such information.

“Today, the way that you use data to solve problems—and identify opportunities—is just the opposite [of what it used to be],” says Mr Sumser. “We can have as much information as we want, so the question is, ‘What can you see in the information that you cannot see otherwise?’ You look around to see what the data might be able to tell you, and then you form the question.”

As the sophistication of Big Data tools continually increases, so does the range of answerable questions. Big Data can shed light on ever more aspects of the employee lifecycle, including recruiting, onboarding, training and retention. Mr Sumser, for one, is optimistic about the future: “The amount of data and what we can do with it is exploding,” he says, “and it is turning our ideas of what is possible upside down.”

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