Among the countless challenges facing employers, one perennial thorn is showing promise lately of yielding to data innovation: the problem of finding, hiring, and retaining the right people. To a degree, it’s a challenge of seeing things differently. And as one of America’s leading entrepreneurial and innovation centers, tekMountain thrives on doing just that.

Think about nursing. The looming, historic nursing shortage isn’t going to ease if hiring and retention processes don’t evolve, and fast. Clinical internships have always been hospitals’ pipeline for human talent. Hospitals to this day collect hundreds, even thousands, of applications to fill a few dozen positions, then bear the high cost of onboarding. Every time a new nurse leaves, the hospital turns out its pockets for some $40K in lost investment.

As far back as 2008, Peter Cappelli, professor of management at the Wharton School and author of Talent on Demand: Managing Talent in an Age of Uncertainty, has been looking at this kind of dynamic a little differently than most had. He asserts that failing to manage a company’s talent needs “is the equivalent of failing to manage your supply chain.” Yet in most organizations, recruitment and retention widely continue to resist improvement. “Managing supply chains,” Cappelli says, “is about managing uncertainty and variability. This same uncertainty exists inside companies with regard to talent development.”

We at tekMountain look at the logistics of the nursing shortage as very much a question of data management—more specifically, identity management (IdM), a.k.a. identity and access management (IAM).

What is identity management?

As CSOOnline put it, “Broadly speaking, identity management systems (…) enable the administration of individual identities within a system, such as a company, a network or even a country.” Or, as Wikipedia handily captured more of its nuances, IdM “describes the management of individual identities, their authentication, authorization, roles and privileges[1] within or across system and enterprise boundaries[2] with the goal of increasing security and productivity while decreasing cost, downtime, and repetitive tasks.[3]

Naturally the conversation about identity and access management tends to focus most on security and productivity. But managing uncertainty and variability demands attention to recruiting and retaining the right people.

Standard hiring procedures are nothing if not imprecise and unpredictable. In most hospital settings, hiring a nurse is a subjective determination based on limited experience with that person as a clinical intern. We believe that Big Data has the potential of flipping this hiring model on its head by embracing emergent identity.

Painting the Picture: Emergent identity

If we hold up a person’s “true” identity, one’s total identity, as the ideal, then we must admit that most of what we think we know about people falls short. But every time a person interacts with a system or digital network; every time a student nurse fills out an application or writes a resume, new bits of information are learned and a more complete portrait begins to emerge. While observable, objective data about a person make up the scaffolding of a person’s identity, much more information is unstructured, qualitative and difficult to assess.That’s where machine learning and inferential analytics come in.

Inferential analytics, which is showing great promise in medical-device innovation, when combined with quantitative data, puts us on the path toward that 100-percent-true ideal of a person’s identity. With consideration paid to best security practices and data privacy, the current hiring-and-retention paradigm is revolutionized.

We can imagine how a clinical-compliance platform like CB BridgesTM (a powerhouse in the clinical-placement space from Fortune 500 company CastleBranch), can be further informed by machine learning and inferential analytics to complete the portrait, collating unstructured data from interns’ evaluations, social media, testing results, previous job applications, and more.

Why create such an elaborate portrait? Because the closer an employer comes to seeing the true candidate, then the better is that candidate’s user experiences, better is fraud prevention, and more streamlined are interactions across platforms .

Now when a hospital wants to hire, instead of posting a position and praying for the best, they can match the position requirements more precisely to eligible candidates in the most well-rounded way possible and target upwards of 80 percent of the most suitable graduating nurses.

Logistics well may hold lessons for human-resource management. James Heskett, writing for the Harvard Business School’s “Working Knowledge,” says, “It apparently depends on whether we can get beyond the initial negative reaction to what, for want of a better term, is the potential ‘commoditization’ of people.”

We envision great potential within this sector of the Big Data landscape. With the increased emphasis on IdM, for example, “Identity-Management-as-a-Service (IDaaS) solutions are rapidly becoming a critical aspect of the corporate infrastructure,” as recently noted. We’ll touch upon this space again in forthcoming posts.


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

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