Predictive tools in Talent Management (e.g., Recruiting) – proceed with caution!

Fact: Pure scientific method actually includes testing the hypothesis -- and the opposite of the hypothesis.

Opinion: I believe this purist approach to science and predictive tools unfortunately evades many modern-day “salaried scientists” … including some I-O (Industrial – Organizational) Psychologists who develop assessment tests to predict job success or flag questionable job candidates … sometimes only looking to confirm what they believe to be true. This is probably a function of the intense pressure to get verifiable "scientific" results more quickly in order to demonstrate business value to internal / external clients.

Many recruiting experts would agree that “false negatives” (rejecting a job candidate that would have been a great contributor) are much more harmful to an organization than “false positives” (hiring a job candidate that turns out to be a bad hire). This is generally the case because – as Bill Gates maintains – losing a potential star developer to a direct competitor can be the equivalent of losing $1 billion over the career of that developer. In contrast, most poor hiring decisions are usually addressed / ameliorated within 3-6 months; so on average, they are rarely costing an organization over $40-50,000 for the average professional. So – potentially, a “false negative” can be 20,000 times more costly than a “false positive” --- ok, maybe worst case.

In this context, perhaps we should be concerned that many assessment tests given to job candidates include this particular item to generally screen them out:

• A previous “job-hopping” pattern is often used to predict which job candidates would likely not be an ideal hire, even though the opposite may well be true for certain positions or job situations. For example, a business development exec that changes jobs every 2-3 years probably has a considerably bigger network of contacts to call on … and likely even a broader selling skills repertoire, than a sales executive who has been with one or two organizations over a long sales career. Moreover, a job-hopping sales exec may have been so good that their Sales Comp plan did not adequately reward them, driving them elsewhere.

• The same job-hopping disqualifier or “yellow flag” as a predictive tool also runs counter to the notion that people who have worked in a variety of organizations are exposed to many different ways of doing things, including a broader range of best industry practices.

Bottom line --- predictive tools can be very powerful and useful in Talent Management (e.g., Recruiting), but caution should be exercised in the form of not applying the same conclusions across all types of roles, candidate / manager behavioral profiles and work situations.

HR technology implementations -- fewer failures on the horizon

Last week a $30 million lawsuit was filed by Marin County, Calif., against Deloitte Consulting, alleging the consulting firm misrepresented its expertise in SAP's technology. Deloitte is planning to file a counter-suit over the County's failure to pay, and claimed the County failed to provide Deloitte with written reports detailing system deficiencies.

Decade after decade, we've read about consulting firms and enterprise software vendors blaming each other for failed implementations when these unfortunate situations are largely preventable – particularly when you’re dealing with consultancies and software solutions which have come through many hundreds of times before.

The root cause of these failures, or in some cases major delays or re-starts, is typically not defective software as delivered. Companies offering defective enterprise software simply don’t stay around very long.

Two fairly common causes of failed HR technology implementations which are gradually being neutralized in the HCM solutions arena relate to change management and the customization of on-premise, installed software.

For many years, change management was the segment of tasks in an implementation project plan that were often short-changed due to resource constraints, being managed by project directors or accountable managers with limited (change management) experience, or being outsourced to firms/practices often brought in during the later stages of the project … vs. focusing on change management throughout the entire implementation --- a far superior approach.

Change management is partially about changing attitudes and behaviors, and as HCM systems are increasingly being viewed as company-wide assets for everyone’s benefit -- instead of “the system HR insists that we use” – the need to change attitudes and behaviors is perhaps no longer as intense. Now these exercises have a better chance of succeeding as project teams can focus on change management aspects that are more tangible like process changes needed, targeted training, etc.

Another reason for failed HR technology implementations is also becoming less pervasive – namely, instances where on-premise, installed software gets customized to better meet a customer’s “unique” business requirements. As everyone knows by now, the degree to which enterprise software gets customized is directly correlated with the prospects of a failed or at least under-performing implementation. The good news is that instances of customized software are clearly trending downward with much better configuration toolsets being introduced by software providers --- and SaaS delivery models becoming much more prevalent.

20 things you should never short-change when buying/implementing HR Technology

1. the “what's in it for me” perspective of each class of user; e.g., employees, applicants, line managers, executives
2. considering the downside of “imposing across-the-board standardization” when it unduly compromises other key business objectives
3. the change management effort in planning & executing the implementation
4. the training effort in planning & executing the implementation
5. the process & technology integration effort in planning & executing the implementation
6. subscribing to the “trust but verify” approach with respect to vendor / product claims
7. IT buy-in and having their criteria for support understood and transparent
8. end-user buy-in and having their criteria for support understood and transparent
9. exec buy-in and having their criteria for support understood and transparent
10. leveraging your company’s brand / value as an end-customer in making expectations known to your vendor partner; e.g., expectation to have some input to product direction or priorities if possible/practical
11. your organization’s previous experience with new technology adoption and roll-out
12. critical linkages between the various pillars of the Talent Management value chain, including those that are “focus areas” more than processes; e.g., employee engagement
13. internally marketing the benefits of implementing the new HR/HCM system or module --- before, during and after the system is implemented
14. the importance of “quick wins” to create support and momentum in the early stages
15. the importance of end-users being in control of (and being accountable for) data quality
16. focusing on business drivers, how they might be changing over time, and how the HR/HCM system aligns with those drivers
17. lessons learned from similar companies with similar implementations
18. the contingency plan for transitioning away from each HR/HCM vendor you partner with
19. creating & maintaining a “risk and opportunity” log from before Day 1; i.e., during the planning stage --- thru post-implementation
20. using a meaningful decision-support process and tool for prioritizing system enhancements needed -- or (if there's no other viable options) customizations pursued

Financial impact of data-driven workforce decisions --- MIND THE GAP!

To illustrate the bottom line business impact of making optimal workforce decisions based on properly analyzing data --- and data relationships, here's one example that most companies face all the time:

For every gap in workforce capacity ... either in terms of numbers, skills, competencies and/or physical location, there are 5 viable options:

(1) train an incumbent to address the gap
(2) re-deploy another employee ...
(3) promote another employee ...
(4) hire a regular employee ...
(5) hire a contractor ...

Taking a recent benchmark from Staffing.org on the average fully-loaded cost of hiring an exempt (non-executive) employee (= approximately $12,000) ...

Consider an organization with 500 exempt-level 'workforce gaps' to address in the course of a year that is not in a position to -- or by force of habit doesn't -- make data-driven workforce decisions.

Let's assume for this example that the 500 workforce gaps are addressed as follows (excluding the cost of labor):

- 80% or 400 gaps were addressed by an external hire (400 x $12k = $4.8 million)
- 20% or 100 gaps were addressed by an internal redeployment, half of which (or 50)created other (cascading) gaps to fill (50 x $12k = $600k)
- None of the gaps were addressed by training as it was not considered an option
- None of the gaps were addressed by hiring more costly contractors

So in this fairly typical example, the total cost of addressing the 500 workforce gaps is $5.4 million -- excluding the cost of labor itself.

Now let's further assume that IF the appropriate data -- and data relationships -- were analyzed so as to optimize each individual workforce decision, the breakdown would have looked like this:

- 40% or 200 gaps were addressed by an external hire (200 x $12k = $2.4 million)
- 40% or 200 gaps were addressed by an internal redeployment, half of which (or 100)created other (cascading) gaps to fill (100 x $12k = $1.2 million)
- 20% or 100 of the gaps were addressed by training an incumbent at an average cost per training instance of $2,000 (100 x $2,000 = $200k)

So in this decision-optimized example, the total cost of addressing the 500 workforce gaps would be $3.8 million -- excluding the cost of labor itself.

The difference -- $1.6 million ---- for EVERY 500 workforce gaps to fill.

A large organization of 10,000 employees would likely have at least 1,500 workforce gaps to fill annually ... so they would enjoy a cost savings of approximately $5 million!!!

Based on many years in and around HR functions, I believe this example is quite plausible and realistic in highlighting the benefits of a data-driven decision process in HR.

As they say in London --- "Mind the Gap!"

4 Burning Questions about the HCM Solutions Market

I have my own ideas on these important questions, but welcome hearing from others:


(1) Why do Tier 1 or “large brand” HR-ERP solutions continue to dominate market share worldwide when (according to considerable, recent research) their total cost of ownership is 3-4x that of Tier 2 solutions, when less than 2/3 of their core functionality is ever utilized, and when expected business benefits are achieved less often than with their “Tier 2” counterparts?

(2) Why do the most expensive enterprise solutions in the market seem to require the most customization --- when it should perhaps be the opposite?

(3) Given that robust Talent Management Solutions are supposed to enable business strategy, why (again, according to recent research) are so few end-customers satisfied with the underlying HR process that supports that (Talent Management) strategy -- as is the case with the satisfaction of Performance Management processes?

and ...

(4) With a Talent Management Suite market estimated at $3 billion, mostly participated in by enthusiastic customers who have already spent substantial sums implementing an HR-ERP, why do customer satisfaction ratings only hover between low and medium for players in this market segment? Moreover, why are these solutions apparently having such a negligible impact on such a cornerstone of talent management like Employee Engagement?