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How Big Data Analytics is Disrupting Hiring and Management

By September 30, 2014Article

It’s clear that the use of high-powered analytics across business lines can drive corporate performance, and with the advent of technologies that harness Big Data and predictive analytics, it’s possible to use that insight to optimize multiple core business sectors across different industries. According to Bain, companies that use Big Data fall into the top quartiles of their sector. Humana and Netflix are just two of the companies using Big Data to make big changes in their industries in a way that’s human and impactful. 

We’re familiar with how Big Data can be used in entertainment, marketing and health. But those competencies are now leaking out at scale to other parts of the business to the point of revenue or “edge” of the enterprise. Why not let innovations in Big Data and predictive analytics touch the entirety of the business? 

For example, one unexpected way to use technology is in conjunction with human capital, specifically your frontline sales and service talent that have constant access to your customers and partners — and are critical stewards of a corporation’s P&L. 

Decisions associated with human capital have been some of the most difficult to adapt to the new world. But as contemporary players in HCM/CRM emerge, the operational infrastructure is undergoing rapid change as evidenced by the rapid growth of multiple platform players in the cloud. Specifically, the cloud can be harnessed to:

  • Find the best talent by using scientific principles to pinpoint exactly what makes an individual succeed at a certain role
  • Keep your best talent by giving intelligent assistance to managers interested in keeping their workforce engaged
  • Keep multiple entities within the operation in sync. 

Some early players have taken these systems and seen tangible, measurable and easily replicated success across their large bases of employees. Here are but a few examples of how human decisions yield massive bottom-line contribution across the hiring and management segments of the employee life cycle that I’ve seen in my own day-to-day work:

  • Dialog Direct: Upon adopting Big Data in their management practices, they hired employees who stayed longer and performed better, saved over $5.5 million in 2012 and reduced attrition by 39 percent.
  • Xerox: Before using predictive analytics, Xerox sought out employees with previous call center experience. However, when they examined actual workforce performance and tenure with the lens of newer technology, employees with previous relevant experience and others who brought no experience to the job performed almost exactly the same. This insight was important as it meant Xerox could consider a broader group of potential employees while reducing the higher payroll costs of experienced workers. 

Choosing to operate from a predictive analytics standpoint in both staffing and operational management of teams seems like an obvious choice but isn’t widely adopted yet. What are the barriers to this much-needed change in the way we hire and manage?

  • Legacy thinking: Critics point out that using algorithms in hiring and management takes the human process out of human resources. But let’s examine the process now: recruiters tend to invest a lot of time to hire people that look and act “just like them,” regardless of their qualifications for the position. Big Data helps remove bias by focusing on performance.
  • Lack of enough operational push needed from the line of business: Sure, it’s easy to fixate on certain parts of the business, like sales growth. But your talent contributes to revenue growth and retention in the same order of magnitude as marketing spend. These ambassadors that are in your sales in service sector directly impact your growth.
  • Executive education as to how Big Data and predictive analytics can impact functions of the business “out on the edge:” The C-suite isn’t always up to speed on the impact of certain technologies and often look at solutions as just expenses waiting to happen. But as more successful companies pave the way for others to buy into Big Data, it will be only a matter of time until using technology to boost management practices will be a best practice with the front-line workforce. 

What is the technology baseline for this? With today’s infrastructure, we can rely on the following systems to build upon:

  • Hadoop: It enables scalable cloud computing by providing a framework for large-scale clustered processing and storage of datasets. It’s an important building block for cloud and analytics technologies.
  • Machine learning: There’s a reason why accelerator Y Combinator is looking for more artificial intelligence startups. This technology will be key to deciphering data into actionable information.
  • Application of predictive algorithms: The latest technology is built on algorithms that can predict human behavior and outcomes, which is key in managing thousands of employees that produce millions of datasets. 

Though we have some of these advancements today, there is still room for improvement in adoption. But given today’s progress, what does the future look like for Big Data at work? 

By 2020, data will be pervasive in the way we hire and manage employees at large companies. And by 2030, we’ll see all sizes of businesses able to leverage data and analytics for the benefit of their workforce. 

We have a solid foundation today, but we must overcome two obstacles to make Big Data and work a universal option: 1) the perception in the limits of Big Data and 2) barriers to the technological innovation. 

Carl Tsukahara is EVP of product and marketing at Evolv, a San Francisco-based tech company that uses Big Data and predictive analytics to optimize the workforce.


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