Dave Ulrich’s widely adopted model separates the roles in HR into four specializations (see Fig 1). The trouble here is that analytics has a strong part in all these roles, while it has historically been the domain of the Administrative expert, particular among those who run HR Information Systems/Services (HRIS). The technology is not easy to use, to say the least. But a new generation of companies began to see the opportunity here. At HR Tech Europe 2015, London , a gathering of HR practitioners, experts and organizations, I sat down with several companies that are moving the needle on how organizations can use data and analytics to advance workforce planning, recruiting and talent development.
In the Ulrich model, recruiting or staffing tends to fall into the role of the Change Agent responsible for organizational design and change management, with input from the Employee Advocate familiar with labor practices. Alternatively, it is also outsourced to agencies and recruitment firms worldwide to reduce the extensive time it takes to investigate and find talent in the market.
Fig 1. The Ulrich model of HR specialization (Image: Rawn Shah)
Depending on the level of formalism in the organization, a lot can go into the wording of job descriptions here. It starts with the hiring manager looking for specific skills and operational responsibilities of the candidate. Moves into the negotiation with HR where it fits in the performance and compensation plans, and with the hiring practices. Recruitment firms provide the added value of data on candidate availability to the given descriptions, in addition to their access to such candidates from their networks.
This point is where access to data on a market level becomes crucial. In a conversation with Meredith Amdur, CEO of Quebec City, Canada-based Wanted Analytics , with the right data companies can change their strategies not only for individual job descriptions but also bigger decisions like broader hiring, flight risk, or where to better place a new business office.
“Internal analytics doesn’t tell you enough. You need to see everything in the industry around them,” said Ms. Amdur. Their service crawls job boards, corporate sites and the Web to consolidate data to answer key questions: Given a job description, figure out how hard it is to fill this role, what is the supply, who is also hiring that role, which cities, what salary ranges, and what job roles. The software than gives a score of the difficulty to fill the role as extrapolated from their Big Data system.
For example, if you were searching for data scientists (Fig 2.) globally, you might first consider the Silicon Valley area because of candidate supply. But if you look wider, London has nine times as many and is easier to fill the positions on the hiring scale. Manchester would have fewer candidates but is much easier to hire. The availability of salary ranges depends on what is actually posted. These are based on actual openings and supply of candidates, not predictions.
