Making employees the focus of analytic activity has become a strategic competitive advantage for organizations at least as important as customer data and product performance. HR traditionally looked backward, reporting on past performance. Today the talent supply-chain game is all about talent intelligence—forecasting to gain the competitive advantage in matching the best talent for the work you need done.

And, oddly enough, many organizations are lagging. More than half (51.4 percent) of organizations surveyed by Deloitte in 2017 have no vision, not even a definition, of digital HR, much less a digital strategy. Several reports from Human Resources Today in the past few months, like this one and this one, reiterate the theme that “analytics is relatively new for HR” and “HR has been slower to adopt an analytics approach than other parts of the business.”

And that’s been true for some time. According to a two-year Deloitte study published in 2014, while a huge majority of businesses viewed talent analytics as a high priority, 86 percent of the companies surveyed then had no analytics capability in HR. The pain they feel is acute.

Deloitte researcher Josh Bersin, principal author of that study, found that the highest-performing companies in terms of shareholder value were those most proficient in talent analytics.

Human resource management needs to continue shifting from reporting to analytics as “data-oriented organizational culture will be positively related to the use of analytics for strategic talent management.” And the outcomes are all about business performance (Sharma and Bhatnagar).

Researcher MaryAnne Gobble recently explained,

There’s no getting around the fact that, in order to compete for high-demand talent, HR today must be data driven. A key manifestation of this is the emergence over the last several years of talent analytics—in the words of  Michael Moon, in a report for the Aberdeen Group, “the application and management of data for use in advanced analytics … to identify linkages between an organization’s talent and financial outcomes.” Identifying  patterns in the data a company collects about its recruitment efforts, hiring processes, and employees can help the company make better decisions about its people. And, as Moon points out, those insights are also critical  “in enabling HR to link human capital activity to business outcomes.” (59)

A cloud-based talent ecosystem

Meanwhile, observers are noting the ever-increasing fluidity of the talent pool. Writing for the Harvard Business Review, researchers John Boudreau, Mara Swan, and Amy Doyle described an emerging, disbursed talent ecosystem, resident more in the cloud than in your acoustically partitioned office space. Forecasters see employers facing the increasing challenge of even defining who their employees are, as workers function more remotely, for a greater number of employers than ever before.

The disbursed, cloud-based talent ecosystem further enables a collaborative model to address talent supply chain management. But challenges to implementation still exist:

  1. Educational institutions (“talent suppliers”) are slow to respond to market changes.

  2. Building relationships between employers and talent suppliers is costly and slow.

  3. Internal silos can stymie implementation, as when HR is disconnected from R&D and lacks the data it needs to create a comprehensive analytics program.

  4. Opportunities for retraining and continuous learning (the “return” concept) may be challenging to implement (Makarius 503).

What might such a collaborative approach look like? We see a few right off the bat:

  • Incubators collaborating by combining resources, leads, mentors, and events.

  • Commercially sponsored academic tech curricula

  • Strategic relationships among colleges, employment agencies, employers

  • Physical proximity of talent suppliers, employers and employment agencies to speed access and communication.

Researcher M. M. Gobble writes, “Achieving   the pace of innovation required to survive  will require data-based decision making. At the same time, the data could become even more dispersed and fragmented. Clearly, R&D and HR will have to align around a shared strategy, one that supports the company’s strategic goals.” ( 61).

As you might expect, Google was among the early adopters of analytics to HR. The company’s “people ops,” developed in 2006, applied data to talent supply to identify those traits that make a candidate “Googley.” People ops took on a four-part emphasis:

  1. Effective leadership,

  2. Productive environment,

  3. Predictive modeling to forecast problems and identify opportunities, and

  4. Diversity (Gobble 60).

If Google’s people ops is any indication, the nexus of TSCM thought leadership is shifting toward commercial tech and away from centers of business scholarship: “Data-driven cultures, Google discovered, respond well to data-driven change,” according to HBR.

HR analytics is maturing

HR analytics—a.k.a. training analytics, people analytics—is no longer the forgotten backwoods of business. Citing his High Impact People Analytics study in December, Forbes’ Josh Bersin writes that now 69 percent of companies are integrating data to build a people analytics database. “In prior years,” he writes, “this was always about 10-15 percent of the organizations we surveyed – this huge change in investment is a sign that this discipline has grown up.”

CEOs and CHROs have fully bought into analytics, understanding its central importance to running a high-performing company.

Ninety percent of level 4 companies surveyed—a small number, to be sure, but world-class companies—”believe they have accurate people-related data, 95% believe they have strong practices for data privacy and security, and 75% believe they have consistent data definitions.” And one of the primary reasons for this, Bersin reports, is “a new generation of integrated cloud [human capital management] systems that require a company to implement a more consistent system of record.” Today some 40 percent of companies have cloud-based HCM systems.

In our view at tekMountain, collaboration is like the synaptic network that builds competitive talent intelligence. We have long promoted the strategic alignment of entrepreneurs and investors with HRtech stakeholders such as universities and with predictive analytics that are revolutionizing HRtech. As thought leaders in the tech-innovation ecosystem, we leverage collaborative incubation and seek strategic relationships among colleges, employment agencies and employers, as well as among startups and investors. The bank of knowledge, experience and spirit of innovation that effective talent intelligence requires is native to the ecosystem in which we live.



Gobble, M. M. (2017). The datafication of human resources. Research Technology Management, 60(5): 59-62.

Makarius, E., and Srinivasan, M. (July–Aug 2017). Addressing skills mismatch: Utilizing talent supply chain management to enhance collaboration between companies and talent suppliers. Business Horizons, 60(4): 495-505.

Sharma, A., and Bhatnagar, J. (2017). Talent analytics: a strategic tool for talent management outcomes. Indian Journal of Industrial Relations, 52(3), 515+.


This blog was produced by the tekMountain Team of Sean AhlumAmanda Sipes, Kelly Brown and Zach Cioffi with lead writer Bill DiNome.

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