On the heels of my last post on the careful use of predictive tools in screening job candidates, I have included some musings below on 4 other “tricky nuances” in talent management.
1. One of the predictors of “employee retention risk" that I’ve used in models is “recency of training.” Just like a prior ‘job-hopping’ pattern can foretell whether a job candidate might be EITHER a good or bad hiring decision (depending on the job), training an employee could have polar opposite effects on employee retention -- based on the broader "work experience" context.
The degree to which training increases/decreases the odds of an employee leaving (voluntarily) is often related to whether the skill(s) they are being trained in are valued more internally vs. externally. Presumably, something valued more internally (e.g., being skilled in a company-proprietary technology) means the employee will likely be compensated, engaged and generally treated better than if they were to leave … so better retention usually ensues. Conversely, if an employee picks-up a new skill or set of skills (e.g., Six Sigma Training) when the market is hungry for those skills –AND the organization is not expending much effort (or money ) to engage and secure that employee, the training offered can certainly result in creating an “employee retention risk.”
2. A tricky scenario that relates to compensation is where an excellent or above-average performer gets a very modest (or even meager) salary increase due to being on-top of the range for their salary grade. In an organization of say 10,000 employees, it’s certainly conceivable that 3-5% (or 300-500 employees) will be in this situation. If the average salary for this group is $70,000 and the average (small) increase given is 3% … that company has doled out $630,000 to over $1 million and will likely derive very little if any benefit from that! [Refer to #3 below for a possible way to mitigate this situation.]
3. A more technical situation where compensation can serve as a dis-incentive … Take the case of a married couple in the U.S. filing a joint tax return with a combined taxable income of $65,000. One of them then gets a nice 10% salary increase for being a great contributor -- cause for a nice dinner out -- or is it? Well, for married couples filing jointly, $68,000 is the cutoff between the 15% and 25% tax brackets. Consequently, this couple’s income (after taxes) is actually decreased by $8,000 after one of them gets a healthy 10% raise! How does an organization avoid rewarding employees well but getting the opposite of the desired effect?
One answer is to have an HR manager and a corporate accountant review a list of all employees potentially in this situation; and perhaps offer a non-monetary reward if the employee so chooses; e.g., flex hours or non job-specific training. At the very least, they should communicate with those employees to advise them of the consequences (e.g., short and longer-term) of this year’s comp adjustment.
4. I’ll end with an example in the realm of competencies … specifically, what might occur when the value an organization attaches to a certain competency changes in a material way? Case in point from investment banking … I suspect a few Wall Street firms (the ones that seriously signed-up for less risk-taking) have been thinking a lot lately about how to re-tool their workforce from a competencies standpoint. With “risk management” and “sound judgment” likely being valued much more in these firms, they must quickly transition to these “newly valued” competencies in a way that is minimally disruptive to the business.
Saturday, June 26, 2010
Tuesday, June 15, 2010
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.
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.
Monday, June 7, 2010
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.
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.
Friday, May 21, 2010
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
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
Monday, May 3, 2010
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!"
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!"
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