Workforce Reality

SAP and SuccessFactors, Let’s Not Forget About the Basics of What Makes Talent Management Effective « HRO Insights

2011-12-13T10:29:00.000-06:00

Genuine insights from Nelson-Hall. The wave of acquisitions and consolidation of technologies essentially makes technical capability a commodity but doesn't address essential issues in developing a workforce and effective as well as efficient workforce management.

There are lessons to be learned from Success Factor's earlier acquisition of InfoHRM. InfoHRM had excellent technology but their successes were clearly due to having a staff of smart consultants and smart clients. Where those factors didn't exist or weren't used, they had a minimum impact. After the Success Factors acquisition, most of the best consultants left. And there is little left of InfoHRM now to battle it out with legacy SAP and Business Objects tools within the big SAP tent.

SAP and SuccessFactors, Let’s Not Forget About the Basics of What Makes Talent Management Effective « HRO Insights:

'via Blog this'

The Big Idea: Before You Make That Big Decision... - Harvard Business Review

2011-11-29T08:11:00.000-06:00

Much of what passes for workforce analysis these days is based on assumptions about rational decision making. We assume that presented with facts, decision making will improve (and the more facts the better). Behavioral economics offers a different perspective. How do we incorporate these insights into how we do analysis and how we present the results of our analyses.

The Big Idea: Before You Make That Big Decision... - Harvard Business Review:

'via Blog this'

The Liar’s Contest

2011-10-17T08:15:00.000-05:00

I've been on both sides of this contest and in the customer role too -- all with some damage to people, schedules, and quality. Wisdom from Gerry Wineberg helps to negotiate to a better result.

The Liar’s Contest:

'via Blog this'

Google’s HR Practices Explained - Compensation Today

2011-06-14T18:55:00.000-05:00

Google is practicing Evidence Based HR...and coming to some conclusions and practices that should challenge your compensation practices.

Google’s HR Practices Explained - Compensation Today

Understanding Employee Turnover Rates

2011-05-15T21:36:00.000-05:00

Understanding Employee Turnover Rates

This might get a bit wonky, but stick with me. It’s important to know how to lie with statistics – not so you can lie, but so you don’t do so inadvertently and can listen with some appropriate skepticism to the lies of others.

Sometime ago I came across a web site promising an application to calculate the cost of employee turnover. I thought this both exciting and unlikely. If we could calculate the cost of turnover, it would be much easier to develop a business case for favorite HR interventions to improve employee retention: addressing issues of employee development and career progression, adjusting compensation plans, developing new coaching and mentoring programs, improving recruiting, etc. [1]

Alas, the web site lived up to my expectations rather than my hopes. The application simply asked you to provide data and metrics about turnover, cost to hire, cost of onboarding, cost of lost productivity and so forth and then did the simple math required to get to a number. Of course the hard part is getting to those initial metrics and the application provided only blanks to be filled in with those numbers.

The spiffy little application is a good example of what passes for Workforce Analytics these days. The first blank to fill in asks: “What is your turnover rate?” Good question and misleadingly simple; dangerous without a definition.

First question: Why do you want to know?

Understanding turnover is a part of managing workforce capacity requirements. That’s a process of balancing labor supply and demand. Turnover is an important component of the supply side; turnover rates help us predict how many people, how much labor, we will have under current conditions at a future point in time?

For workforce planning purposes, there is a complication here that we should note. From an enterprise point of view, turnover is equal to termination; it is defined as an employee leaving employment. From a departmental, or location, or job group considered in planning, ‘turnover’ or ‘attrition’ may mean employee termination or a transfer to another department, job group or location. For projecting future capacity supply for those smaller units, the distinction can be important. That said, we will ignore it here and treat turnover as equal to termination – think of the general concept as an ‘exit’ from an employment relationship, and the same considerations will apply.

I. Calculating Turnover Rates

The formula for calculating turnover rate at first seems simple: Number of Terminations / Headcount. Consider these data:

Starting HC	Terminations	Ending HC
12,757	5,382	10,623

Which Headcount do we use? If we use the starting headcount, we get

5,382/12,757 = 42.19%

If we use the end of period headcount, we get 5,382/10,623 = 50.66%

The most common solution is to use an average headcount for the period. So then we get

5,382 / (12,757+10.623)/2 = 46.04%.

Starting HC	Terminations	Ending HC	Using start	Using end	Using Avg.
12,757	5,382	10,623	42.19%	50.66%	46.04%

Even in this simple example there is a variation of 4 to 8%. That’s a significant variation and you’d better be sure that if you are discussing turnover data with someone, you are using the same definitions and same formula. Using the average seems reasonable enough given these data, but needs a closer look.

It’s pretty obvious that the starting headcount minus the number of terminations does not equal the ending headcount. (12,757 – 5,382) ≠ 10,623. So something – hiring or transfers – has added to the employee population while terminations were reducing it.

So what? Consider some other conditions and issues. From the data we have, we can derive the number of additions to this employee population:

Adds(Hires and Transfers in) = Ending HC - Starting HC - Terminations

Now consider these data and the variation in results with our different methods of calculating turnover rates:

*Scenarios*					*Turnover Rates*
	Starting HC	Terminations	Additions	Ending HC	Using start	Using end	Using Avg.
1	12,757	5,382	3,248	10,623	42%	51%	46%
2	12,757	5,382	8,000	15,375	42%	35%	38%
3	0	5,382	16,005	10,623	#DIV/0!	51%	101%
4	12,757	13,324	567	0	104%	#DIV/0!	209%

It is easy to see that we have some problems at boundary conditions in scenarios 3 and 4. If the employee population we are considering is in a start up entity with zero headcount to start or in an entity that is discontinued with zero headcount at the end of the period, we have divide by zero problems. More generally though, we see varying rates within each scenario. In the more common cases, scenarios 1 and 2, the variability is somewhere between 3 and 9 percent. That’s still a significant difference. In the boundary condition cases, the variability is much more. In all cases, we may have some problems if we want to compare our rates with those of peers or colleagues or whoever created that ‘cost of turnover’ application.

So what’s the right method? The answer depends on being clear about why you wanted to know the rate in the first place. For planning purposes, the correct answer is ‘none of the above’!

What we want is a ‘depletion rate’ for the workforce that enables us to project future capacity given the current state of our ‘inventory’ of human capital. Since we are projecting based on the current state, it should be clear that we can’t have a rate greater than 100% -- people can only end current employment once. We want to know what percentage of the current population is likely to terminate in the future. To get that number, we need to include in our headcount all those who had an opportunity to terminate in the period being measured: our starting headcount plus all additions to the population. Then we get another set of rate measures:

	Starting HC	Terminations	Additions	Ending HC	Using start	Using end	Using Avg.	Start+Adds
1	12,757	5,382	3,248	10,623	42%	51%	46%	34%
2	12,757	5,382	8,000	15,375	42%	35%	38%	26%
3	0	5,382	16,005	10,623	#DIV/0!	51%	101%	34%
4	12,757	13,324	567	0	104%	#DIV/0!	209%	100%

The significance of these differences is clear if we use the rates to project future turnover from the current state:

					Rates		Projected Turnover
	Starting HC	Terms	Additions	Ending HC	Using Avg.	Start+Adds	Using Avg.	Start+Adds
1	12,757	5,382	3,248	10,623	46%	34%	4,890.76	3,572.20
2	12,757	5,382	8,000	15,375	38%	26%	5,882.86	3,986.52
3	0	5,382	16,005	10,623	101%	34%	10,764.00	3,572.20
4	12,757	13,324	567	0	209%	100%	-	-

The ‘starting headcount plus adds’ formula delivers the most useful projection of future turnover.

Turnover over Time, Trends and Rates

Let’s add another complication. We’ve been looking at rate calculations for a single period or span of time. If the period is broad enough and aligned with a planning or budgetting cycle, that is appropriate. But another purpose for monitoring turnover is to identify trends and patterns that could impact critical workforce capacity and then take appropriate action before there is a significant gap or glut . Waiting on an annual budget cycle won’t due for that purpose.

So let’s look at our sample data on a monthly grain:

	Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec
Monthly Headcount	12,757	11,235	10,905	10,825	10,932	11,121	11,465	11,603	11,116	10,927	10,897	10,870
Monthy Terms	1,685	550	523	512	390	239	263	570	266	75	52	257
Terms to Date	1,685	2,235	2,758	3,270	3,660	3,899	4,162	4,732	4,998	5,073	5,125	5,382
Monthly Hires	163	220	443	619	579	583	401	83	77	45	25	10

The data here show that, like in most organizations, terminations don’t happen at a steady rate but have peaks and valleys. A graph makes that story clearer.

We can’t just take a month’s data and use that to project rates in a straight line into the future. There are several rates to consider then.

	Jan		Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec
Monthly Headcount	12,757		11,235	10,905	10,825	10,932	11,121	11,465	11,603	11,116	10,927	10,897	10,870
Monthy Terms	1,685		550	523	512	390	239	263	570	266	75	52	257
Terms to Date	1,685		2,235	2,758	3,270	3,660	3,899	4,162	4,732	4,998	5,073	5,125	5,382
Monthly Adds	163		220	443	619	579	583	401	83	77	45	25	10
Adds to Date	163		383	826	1,445	2,024	2,607	3,008	3,091	3,168	3,213	3,238	3,248
Monthly HC Change	(1,522)		(330)	(80)	107	189	344	138	(487)	(189)	(30)	(27)	(247)
Monthly Turnover Rate.	13.0%		4.8%	4.6%	4.5%	3.4%	2.0%	2.2%	4.9%	2.4%	0.7%	0.5%	2.4%
Annualized Turnover Rate	13.0%		17.0%	20.3%	23.0%	24.8%	25.4%	26.4%	29.9%	31.4%	31.8%	32.0%	33.6%
Rolling Average Rate	13.0%		8.9%	7.5%	4.6%	4.2%	3.3%	2.5%	3.0%	3.2%	2.6%	1.2%	1.2%
Annualized Avg Rate	13.0%		8.9%	7.5%	6.7%	6.1%	5.4%	4.9%	4.9%	4.6%	4.3%	3.9%	3.8%
Median Monthly Rate	13.0%	8.9%		4.8%	4.7%	4.6%	4.5%	4.5%	4.5%	4.5%	3.9%	3.4%	2.9%

Now, when we are asked for our turnover rate, at any point in time we can give five different answers! Better, we can begin with a question or an understanding of how our answer is to be used.

The Monthly Turnover Rate calculation uses the same formula we used for calculating rate for a single period above: Terminations / Starting Headcount + Adds. It answers the question ‘What proportion of our labor force did we lose in the month?’ These monthly rates give some visibility to the trend over time – ‘Are terminations increasing or decreasing?’ For some employers with a large seasonal workforce, it may be more reasonable to compare one year’s September turnover rate to last year’s September rate rather than comparing to rates for July, August and October.

The Annualized Turnover Rate applies our standard formula to the total terminations and hires for the year at each monthly point of measurement against the starting headcount for the year. Thus: Terminations to date / (Start of Year Headcount + Adds to date). This rate answers the question ‘What proportion of our labor force has separated year to date?’

If we want to monitor turnover on an ongoing basis as a benchmark we will want a metric that controls for peaks and a valleys in the data while identifying overall trends. The Annualized Monthly Average Rate is an ongoing avereage of all monthly rates. In this example, given a spike in turnover at the start of the year, this average may be overstated. Taking a 3 Month Rolling Average Rate focuses attention on more immediate trends. Using Median rather than Average gives us a Median Monthly Rate that is less subject to exceptionally low or high turnover in a single month.

Summing Up Turnover Rates

Given this sample set of data, we could answer the question ‘What’s your turnover rate?’ with numbers ranging from more than 50% to 1.2% depending on the time period and formula used. Some of these rates, while in use in some places, can probably be classified as mistaken. Using ending headcount to calculate an annual turnover rate yields for these data a rate of 50.7% -- but that is of little value in predicting future turnover. There is a wide range of practice in using other rates however. Despite the problems we have discussed, some industry authorities recommend using average headcount to derive a rate, and a good number of packaged software analytical applications use average headcount rather than the preferable starting headcount plus adds approach because that data is more easily available and computation less intense and expensive. On the other hand, statistically trained analysts will insist on a rate calculation that is bound between zero and 100 percent without regard to difficulty in obtaining data or the intensity of the queries required.

The most important consideration however is consistency. Imagine an annual workforce planning meeting without clarity or agreement on turnover rate calculations. One group argues for a rate of 46%, another for 33.6% and another claims 2.9%. What is more likely to ensue is an argument about whose numbers are right rather than strategic discussion about what to do about turnover. Worse yet is comparing your turnover rate to an industry benchmark calculated using a different method then using that variance to set new objectives and goals.

At any rate, precision in forecasting is not an attainable goal. Too many internal and external factors impact employee decisions to leave an employment relationship. But a consistent approach to calculating turnover rates can provide insight into trends and a baseline for analysis.

[1] See for example, http://www.staffing.org/library_ViewArticle.asp?ArticleID=489 “Measuring What Matters – The High Cost of Turnover”.

Watch this before you write a job description or design an incentive program

2011-03-26T07:46:00.000-05:00

Barry Schwartz at TED on our loss of wisdom and the virtue of virtue. Is anyone in HR listening?

Absenteeism -- A metric is not an analysis

2011-03-25T08:58:00.001-05:00

HR blogs and discussion forums on places like LinkedIn and HR.toolbox have a lot of requests these days for definitions of metrics. Some good discussion ensues, but the questions often seem naive and lacking context. Sometimes someone gets around to asking why the questioner wants to measure that and what industry or kind of employee population they are looking to measure. Interestingly, those questions often go unanswered.

I'm afraid that in some of these cases the intent is to play the role of "HR Cop" -- enforcing arbitrary benchmarks and policies without regard to business impact or strategy. That explains why some people still hate HR. It is not a way for HR to realize the aspiration to become a strategic business partner.

One recent post on the HR toolbox blog asked for help "Designing a metric to measure absenteeism in the workplace". The simple answer is to count absences and then calculate a rate based on scheduled work hours. That is as much a question of process -- how do you capture attendance data and hours worked? -- as a question of metric definition. Some responders provided those sorts of definitions and formulas. Others helpfully suggested that some types of absences -- e.g. maternity leaves -- should be left out of the count. Another suggested that absenteeism is not evenly distributed in a bell curve, and that HR should 'target' those with the highest absentee rate. That prompted my response:

It is true and important to note that absenteeism is seldom distributed evenly across a population, but what are we to make of that? What does it mean to 'target' the group with the highest rate of absenteeism?

Why to we want to measure absenteeism anyway? If the goal is to punish or 'manage out' those employees most often absent, the good suggestions in responses here about separating out causes like maternity and bereavement leave are important. I'd go further to note that some groups of workers -- e.g. single parents, people with disabilities or chronic illnesses -- will likely have a higher absence rate but 'targeting' those populations raises questions of fairness and diversity. I remember as well an article not too long ago in Workforce magazine that discussed the problem of 'presentism' during a flu outbreak that addressed the problem of workers showing up to work while ill and spreading the disease.

I'd suggest that the most important reason for gaining visibility to absences, including both scheduled and unscheduled time off, is to understand the impact on productivity, customer service, and overtime costs and the correlation to employee engagement. Those impacts will vary by industry and job assignments and the means to address them will also vary.

In some cases, an engaged knowledge worker may miss some time but pick up work upon returning and meet all deliverable deadlines. In such cases, there is really little negative impact and the 'issue' can be addressed by formalizing more flexible working arrangements, offering telecommuting, etc. Attempts to enforce an attendance policy in such cases will likely have a negative impact on employee engagement and eventually on voluntary turnover. In other cases, service or production worker absences may have a significant impact on productivity and costs and solutions might include changes in staffing levels and/or work design and addressing root causes of low employee engagement.

The more general point is that when asked for a metric, you should ask why. It costs money after all to capture data, derive metrics, and publish results to appropriate audiences who can take some action. Metric systems should have a business justification and objective. With all of the current buzz about Workforce Analytics, there is often a drive from HR departments to develop and implement metric systems without being clear about business objectives.

That is not at all to say that HR metrics and analytic systems are unimportant. There is likely value for most organizations, in this example, to establish visibility to absence rates. But establishing that metric is a starting point, not an analysis. To really add value to the organization, analysis should address the impact of absenteeism on business objectives, productivity and customer service, and then should decompose and seek insights into variance and causes of absences amongst different workforce segments, locations, and organizations. That insight can provide an understanding of real business problems and a guide to workforce management and strategy.

Think Tank: Fix the workplace, not the workers - Telegraph

2010-11-22T12:42:00.000-06:00

Daniel Pink on feedback and performance evaluation. "The workplace is one of the most feedback-deprived places in modern life"

Think Tank: Fix the workplace, not the workers - Telegraph

Laurie Bassi on Naturally occurring experiments

2010-11-15T19:43:00.000-06:00

"Only rarely is it possible to do so through a true experiment, including control and treatment groups. But if you are clever, it is almost always possible to use “naturally occurring experiments” as a way to learn about the linkage between HCM and KPIs."

McBassi | Blog

Manager as Work System Designer: 13 Essential Questions

2010-08-16T17:13:00.001-05:00

Check out this SlideShare Presentation: Are these questions built into your 'performance management system'?

Manager as Work System Designer: 13 Essential Questions

View more presentations from Esther Derby.

Agile learning agile 2010

2010-08-16T17:09:00.001-05:00

Check out this SlideShare Presentation:

Agile learning agile 2010

View more presentations from Declan Whelan.

Performance and Talent

2010-07-13T10:19:00.000-05:00

Human Resource Management, especially in the absence of much in the way of an analytic capability, is a style business. Styles come and styles go without much evaluation of the benefits or harm done to the people so many hollow mission statements declare 'our most important asset.'
The current style, formed in the wake of massive layoffs and labor markets that are both stagnant and turbulent, focuses on "Talent Management" and "Performance Management".
So it is refreshing and edifying to read this piece from Edwards Deming in the blog of my friend Esther Derby:

The idea of merit rating is alluring. The sound of the words captivates the imagination: pay for what you get; get what you pay for;motivate people to do their best, for their own good.
The effect is exactly the opposite of what the words promise. Everyone propels himself forward, or tries to, for his own good, on his own life preserver. The organization is the loser.
Merit rating rewards people who do well within the system. It does not reward attempts to improve the system.
W. Edwards Deming in Out of the Crisis

I believe most honest people can fill in the examples. The link above will take you to a number of postings and other links on Esther's valuable blog.

You can learn a lot ... from teachers

2010-03-09T10:14:00.000-06:00

Maybe it is just where I've been looking, but I find it interesting that the broad HCM communities that I'm a part of have little to say on the subject of workforce issues in public education. Pfeffer and Sutton have some interesting things to say in their Evidence Based Management blog, but I haven't found much else. There are a lot of interesting and familiar issues for HR and HCM there these days: dealing with a unionized workforce, performance management and the proper role for incentive and merit pay, retention of top performers, and developing capabilities and competence in both teachers and their students. These are critical issues for all of us.

This Sunday's New York Times Magazine has a cover story addressing these issues with a focus on what we would call Teacher Performance Management. There is an anecdote in the story that sheds some valuable light on the role of what we'd call Workforce Analytics.

Increasing use of standardized tests and the No Child Left Behind program have produced a good deal of interesting data (and some evidence of fudging and outright cheating) but haven't done much to improve education outcomes at this point. So much for performance measurement and scorecarding alone as a means to overall performance improvement. The Times article features the work of a teacher named Doug Lemov. "A self-defined data geek, he went about this task [of identifying the most effective teachers] methodically, collecting test-score results and demographic information from states around the country. He plotted each school's poverty level on one axis and its performance on state tests on the other. Each chart had a few outliers blinking in the upper right hand corner -- schools that managed to squeeze high performance out of the poorest students. He broke those schools down by grade level and subject. If a schools scored especially high on, say, sixth-grade English, he would track down the people who taught sixth graders English."

This is real workforce analysis. Lemov's goal was to identify "what works". Unlike too many in private sector HR, he wasn't simply interested (or mandated) to reward winners and punish losers. Public education is too important to us all to focus on that kind of performance management; we are all losers if we tolerate much losing. Rather he employed a very simple analytic technique: plotting a control variable that was well outside of his control, poverty, against standard measures of performance. Then he focused on variance -- identifying the outliers and "drilled down" to identify them further.

His next steps are also generally instructive. We talk about actionable metrics but seldom get beyond that paradigm of rewarding winners. His next step was to continue the investigation. He recognized that he didn't have enough data to draw actionable conclusions but focused on the goal of improving overall performance. So the NYT narrative continues: "He called a wedding videographer he knew through a friend and asked him if he'd like to tag along on some school visits." Those first visits "turned into a five year project to record teachers across the country....The odyssey produced a 357 page treatise known among its hundreds of underground fans as Lemov's Taxonomy." The book version is to be published under the title Teach like a Champion: the 49 Techniques That Put Students on the Path to College.

There are some real lessons here for those who look to Workforce Analytics as a means to improving any kind of performance:

Standardized measures of performance are important and useful but by no means sufficient to improving performance on a sustainable basis.
Focus on variance in performance as a means to identify methods to improve overall performance, not simple to reward top performers.
Any initial analysis is likely to produce new questions and demand further investigation. Getting on the phone, getting out of your office and collecting more data is often the first actionable response.

There is a starting point. Can we apply this to workforce management?

Is there a 'next generation' for HR Transformation?

2010-03-08T12:44:00.004-06:00

There is an interesting discussion on the LinkedIn Group One HCM Global Community. The discussion begins with a question: "What will achieving the next level of HR Transformation require, how will this deliver value to the business, managers and employees."

Reposting my response here for some more discussion:

We've been talking about HR Transformation for at least 15 years in my experience, and it is a good time to ask "what's new".
The basic outlines of transformation are pretty well established: create a shared service center, centers of excellence, move to self-service as a means to reduce transactional overhead, establish HR business partners aligned with business operations, ya da ya da ya da....
We've had some success as Bob points out above, mostly in cost take outs. We've had less success in making HR a 'strategic business partner' with a 'seat at the table'. My sense is that in a new business environment, it isn't going to get any easier, but enabling enterprises for strategic workforce management may be even more critical.
Part of the problem, again as Bob notes, is that transformation efforts have been historically underfunded. Especially in economic hard times with limited capital to invest, HR leaders must not only show a positive ROI on investments in HR initiatives, but must produce a higher rate of return than investments in say capital equipment, marketing, or acquisitions. Those other investments may produce conditions for HR to react to, but they don't necessarily win a seat at the table.
Overall costs for HR administrative activities, according to Saratoga, are almost always a single digit percentage of overall business expenses. For HR to be really strategic, it needs a broader view of the challenges facing enterprises in the 'new normal': managing capacity and capabilities for a workforce that is increasingly made up of contingent labor; refining a workforce value proposition after layoffs have dealt a mortal blow to traditional loyalties; engaging a workforce and managing performance with shrinking budgets for incentive pay and significant variable compensation; evaluating build, buy, rent or alliance decisions to deliver critical capacity and capabilities.
We need some new thinking and new solutions to address these challenges or 15 years from now we may be still talking about how "to win a seat at the table". Jason Averbook, of Knowledge Infusion, reports that these strategic activities and issues are increasingly devolving to operational business units who can't wait for HR. We know that simply reducing administrative overhead to free up time isn't sufficient to make HR strategic. We know that there are significant inefficiencies in a decentralized approach, but does 'HR Transformation' have an alternative?

Internal Social Network Analysis

2010-02-23T09:55:00.000-06:00

IBM launched an internal social networking application called Beehive -- think corporate Facebook -- in my last year there. The adoption rate, in my circles anyway, was relatively slow. In part, as the linked research suggests, that was because there were so many other ways of communicating and collaborating. In part too, it was also because management's commitment to the application wasn't clear or consistent -- would spending time there be rewarded, punished or ignored?
It turns out that the application represented, in part at least, an experiment from IBM Research: dimicco-cscw08-beehive-motivations.pdf (application/pdf Object)
I'm convinced that there is tremendous value and insight to be gained from analysis of interactions and connections through such social network sites. Oddly, I think, the analysis from IBM Research focuses on people's motivations in using the application. There is some value there, as Stacy Chapman suggests, in using that insight for talent segmentation in workforce planning. There are certainly some interesting findings about a corporate culture, variability by workforce age cohorts and roles.
But I think there is much more potential for using a social network application to gain visibility into the way work really gets done in many organizations. Consider a social network application that serves as a hub and aggregator for working interactions: messaging, email, meeting and event scheduling, employee directories and blog discussions. Then we could map the interactions and describe the way work in done in many organizations in a more accurate representation than what we have in common organization charts. The value in such a model derives more from the relationships identified than from specific content. A social network model could provide insight to guide action throughout the employee life cycle -- from onboarding to downsizing: Who are the critical links enabling communication and collaboration across business units? How are employees valued by their peers and relationships? What would be the impact of a termination on workforce communications and capacity?

The Case Against Layoffs: They Often Backfire - Newsweek.com

2010-02-12T14:18:00.000-06:00

Jeffrey Pfeffer on the evidence for negative impacts of mass layoffs.

The Case Against Layoffs: They Often Backfire - Newsweek.com

TechReach

2010-02-09T14:03:00.000-06:00

Manpower's TechReach
program is an interesting example of a collaborative solution to workforce development involving public and private sector parties and facilitated by a company with an understanding of the labor market, selection and deployment.
Why are there so few successful examples like this?

Dan Ariely: Bonuses boost activity, not quality

2010-02-04T09:24:00.000-06:00

Dan Ariely: Bonuses boost activity, not quality

Rethinking Performance and Compensation Management

2010-02-01T12:50:00.005-06:00

Let’s be honest. The financial crisis that triggered the Great Recession was created in good part by common performance management practices. Financial institutions competing against benchmark results by their competitors set short term, measurable objectives for employees and richly rewarded them for developing and trading increasingly complex and risky financial instruments. For many, performance exceeded expectations. We are living with the results.

Like performance management in those financial organizations, most performance management systems today are more focused on distribution of incentive compensation than on really managing employee development and performance. Pay for performance, both merit increases and bonuses, is now accepted wisdom and practice in most HR organizations.

There are real forces in an uncertain and volatile economy that drive business to move costs of production, inventory and labor to a variable rather than a fixed cost basis. But those forces do not necessarily provide justification for pay for performance programs. In fact, there is increasing evidence that suggests that for many kinds of work, including those most frequently tied to performance pay schemes, merit and variable compensation schemes don’t work.

“The seemingly self-evident premise underlying merit pay and other individual performance-based pay plans is that they produce higher employee and organizational performance,” writes Fay Hansen in the journal Workforce Management, “Most companies, however, do not test the actual impact of performance-based rewards on employee behaviors and financial results. The most comprehensive empirical studies flow from the academic world, where evidence is mounting that the assumptions underlying individual performance-based pay programs are wrong.”

There are two separable objectives here linked by a common set of assumptions and beliefs that, in many cases, don’t stand up to the evidence. The ‘New Normal’ exacerbates the problems affecting both objectives: managing performance and controlling labor costs.

Managing Performance with Incentive Pay
In most enterprises, especially in the private sector, HR administers an annual process to distribute wage and salary increases on the basis of ‘merit’. For salaried employees, an increasing percentage of compensation is based on variable, incentive pay plans of many varieties. Hewitt Associates reported that in 2009 variable pay plans as a percentage of payroll will reach 10.6 percent and that 90 percent of organizations will make use of some form of variable pay.

As a means of performance management, merit and variable pay plans are aimed at increasing motivation and alignment with business objectives. Shrinking compensation budgets in the Great Recession have challenged the effectiveness of this strategy. With little money to distribute, the motivational effects of merit pay increases should be open to question. Fay Hansen reports: “. The major salary budget surveys point to 2009 merit increases averaging 3.6 to 3.8 percent, with the highest performers receiving 5.6 to 6 percent. In effect, for the vast majority of employees, merit increases are unevenly distributed cost-of-living and market-adjustment increases couched in the language of performance rewards.” In fact, the problematic coupling of variable pay with performance has been open to question for some time. Mercer reports: “Most merit increase budgets have been flat for years, barely meeting inflation rates and having no relation to productivity growth or profitability trends. The pay increases are not enough to motivate employees – but they are enough to irritate them.”

But even if companies are able to fund significant differentiation in pay based on performance assessments, those pay increases cannot sustain high performance and in many cases drive the wrong behaviors. Jeffrey Pfeffer, a professor of organizational behavior and human resource management in the Graduate School of Business at Stanford University concludes “"Individual performance pay plans cost a lot of money and upset everyone." Pfeffer is a leading proponent of “Evidence Based Management” and a strong critic of common assumptions about merit and variable pay and performance management based on significant research.     Pfeffer cites studies showing that significant pay differentiation undermines engagement and teamwork, generates higher turnover, lower quality and sometimes serious ethical breaches and damage and destruction to businesses. Those without time to review the studies cited by Pfeffer can simply review the headlines for the financial institutions that brought us the Great Recession.

To develop new strategies for managing performance in the “New Normal”, we need first to critically evaluate the common assumptions about performance management developed in the “Old Normal”.

Even before the Great Recession, there was considerable dissatisfaction with many performance management systems. A 2004 Watson Wyatt study of employee attitudes and opinions found that only 30% of U.S. workers believed that their company's performance management program did what it was intended to do -- improve performance. Fewer than 40% of people felt that the systems generated clear goals or provided honest feedback, while almost 40% believed their performance was inaccurately evaluated and about the same number said they did not understand the measures used to assess their performance.    A 2004 Hewitt survey of some 350 companies reported that more than 80 percent of the organizations believed their pay-for-performance programs were at best partly successful or were not successful at all at accomplishing their goals.    A survey by Bersin and Associates reported by Leighanne Levensaler in the journal Talent Management found that “Traditional performance appraisals fail today’s businesses.   Some 41 percent of respondents don’t think their performance management processes adequately support the business; 54 percent do not believe performance management processes positively impact employee retention.”

In testimony before the U.S. Congress, Pfeffer summarized 5 common assumptions about performance management that can serve as a framework for evaluating pay for performance practices.

1) Money is an important motivator.
Employee earnings can, of course, have a significant effect on motivation. An employee whose pay is not adequate to support his or her needs, or an employee who finds that their pay is significantly less than their peers, will likely be looking for a new job as the economy recovers. Daniel Pink, in his new book Drive, cites evidence that employers who pay on average above the industry average actually reduce labor costs because of increased productivity.

But these facts do not support a strategy of merit based variable pay – in fact they may argue against it.

Much compensation strategy today remains aligned with the notions of Frederick Taylor, the founder of scientific management in 1911: “What workers want most from their employers beyond anything else is high wages.”   Whatever the validity of Taylor’s conclusion nearly a hundred years ago, he was describing a workforce engaged in routine factory production (and routinely paid low subsistence wages), not the conditions of most labor today where critical thinking, innovation and collaboration are key in many occupations.

The persistence of Taylor’s assumption is described by Chip Heath as an ‘extrinsic incentives bias’: people commonly assume that others are motivated primarily by money while they, themselves, are motivated by other factors. For example, a study of law students found that 62% described themselves as motivated by an intellectual interest in law and in service, but thought that 64% of their peers were motivated primarily by financial rewards.

2) Motivation is the key performance issue.
The common assumption behind most performance management systems is that employee motivation is key to performance. Motivation indeed matters; as does communication of business objectives and focus that allows and defines ways for employees to help meet those objectives. Most workers want to do a good job.

But too exclusive a focus on motivation, and particularly on individual rewards, can blind an organization to more systemic problems and opportunities for improvements in productivity and performance. Increasing motivation, in the short term at least, can only increase effort, not the skills or abilities of workers. The assumption is that harder work will produce greater results. But problems of productivity that are based on problems in information, infrastructure, work design and systems are not soluble by effort alone. “The simple, but frequently, overlooked point is that systems produce performance, not individuals.”

When business performance problems are rooted in systems and markets, individual performance goals and rewards may actually undermine motivation.

3) Individual performance can be reliably and unambiguously assessed.
Despite the dissatisfaction with performance management and assessment systems noted in the studies by Watson Wyatt, Hewitt, and Bersin above, the quest for a reliable, objective means to assess and reward individual performance remains a goal for many HR executives.

For some jobs that’s possible. The conditions for such a system demand that: 1) the worker’s objectives are clear and unambiguous; 2) the knowledge required to perform the task is easily obtainable; 3) there is little or no interdependence with other workers; 4) the output from the task is easily measured; 5) the quality of the output is measurable and directly accountable to the employee task performance. In such cases, individual performance may be objectively assessed and individual performance incentives can be shown to improve performance.

The problem is that few jobs today can be described by those conditions. The ‘outputs’ necessary for business performance from most jobs are multi-dimensional and require collaboration. Even in retail, where the primary objective of store clerks might be measured in sales volume, commission compensation plans based on individual sales have proven to have a negative effect on employee retention, customer satisfaction and loyalty, and even on overall store sales revenues.

“Managers who don’t know how to measure what they want settle for wanting what they can measure”, Russell Ackhoff observed, often to negative effect. For most jobs, individual performance assessments rely on subjective judgments. The validity of those judgments can be difficult to measure, in part because they are often self-perpetuating and self-fulfilling. But in many cases, there is clear risk and evidence of bias. Pfeffer notes that in “a study of a public sector workplace, research showed that, not surprisingly, supervisors gave higher performance ratings to people they had hired…. That study found that whether or not the supervisor had hired the individual being rated actually had a larger effect than objective performance on the rating that was awarded.”   Other forms of bias show a tendency to value employees based on how they ‘fit in’, reproducing current workforce demographic characteristics of race and gender, and reinforcing those who avoid challenging decisions and practices or who provide negative news. Such practices have more to do with social reproduction than with performance improvement and innovation.

4) Individual, rather than collective, rewards are important because of the need to overcome free-riding problems.
A seemingly obvious solution to some of the problems of individual performance appraisal is to shift the grain and assess and reward performance of groups that must collaborate to produce value. Collective rewards not only provide incentives for cooperation, they may also be more easily tied to more objective measures of productivity. And issues of poor performance may identify systemic problems rather than issues of motivation. In retail, for example, it is common to evaluate performance on a store wide basis to identify variations between like stores in peer groups to identify best practices and provide appropriate group rewards.

The common objection to this practice however is what economists have named ‘the free rider’ problem. In theory, the free riding problem is that some individuals may be rewarded based on their group’s performance even if their own performance is lacking. The problem with the theory, Pfeffer points out, is that there is little evidence to support it. In fact, peer pressure within self-managing teams is often a more effective means of improving performance than manager intervention.

5) Differentiation in individual rewards, a necessary and frequently explicit consequence of individual pay-for-performance systems, leads to higher unit performance.
Real productivity is difficult to measure as an independent variable at lower levels of granularity than a business unit or even the enterprise as a whole. Even if productivity or performance could be measured at a finer than enterprise grain, industrial engineers tell us, an increase in individual or even unit performance may not improve and may even degrade overall enterprise performance through suboptimization.
Nonetheless, conventional wisdom in talent management counsels ‘affirming the best and ignoring the rest’ . As envisioned, the ‘war for talent’ is conducted by highly rewarding ‘top performers’ and ‘high potentials’ and hoping that lesser performers will leave or actively be ‘counseled out’ of employment.
Ed Michaels, a McKinsey director who helped manage the study identifying the ‘war for talent’, claims "all that matters is talent. Talent wins."

There is certainly some value in this focus on managing people as a source of sustainable competitive advantage. But conflating talent with performance and individual performance with pay is a source for much confusion and bad practice. There is little evidence to suggest that highly differentiated pay serves as the key means to attract and retain talent and improve performance and significant evidence to support an opposing view.   And despite concerns with a shrinking labor pool due to an aging workforce and declining birth rates, this ‘talent management’ strategy offers little insight in how actually to improve the performance or ‘talent’ of the existing workforce.

A good deal of evidence suggests that highly differentiated merit pay systems generate bad behavior and bad results. When teachers in Chicago and Houston were offered financial incentives for increased student test scores, more teachers cheated by giving students answers in advance of standardized tests.
Recruiters for a large industrial manufacturing company were offered generous incentives to improve ‘time to fill’ metrics for open positions. To do so, they cut back the ratio of interviews to offers and increased offer rates for initial pay well beyond the median for the pay grade for which they were hiring. The net result being a dramatic increase both new hire and experienced worker turnover. And of course we still have the example of the highly talented and richly incented employees of the financial industry that destroyed companies and led to the Great Recession.

Real Performance Management in the New Normal
None of this is to argue that employee performance management is unimportant.   At a time when the Conference Board reports record low levels of job satisfaction, improving employee engagement may be a key to business performance. But the conditions of the New Normal, and the insights it has enabled thus far, demand that we decouple performance management from compensation management and focus on other means to develop and engage employees and improve business performance.

“Why do we need a performance rating? Why pretend to give merit increases now? Why not just a cost of living increase?” asked a CEO.   Indeed, decoupling performance management from compensation, makes performance management something more like…management! The New Normal has not yet produced much in the way of evidence of new insights for workforce management. But some old truths apply.
•    “The simple, but frequently, overlooked point is that systems produce performance, not individuals.”
•    Feedback is important. If employees want to do a good job, they need to know how they are doing. Pay changes are a poor substitute for management and peer feedback and do little to improve performance as a whole.
•    Goal setting helps to communicate business strategy. Apart from determining merit compensation, performance metrics tell employees what is important, where to focus, and how to align their work with business objectives. Setting objectives provides employees with essential information for doing their jobs. You get what you expect.
•    If team work and collaboration are essential, provide feedback and rewards on a team basis. An engaged team will take care of ‘free riders’ and help management to identify jerks that undercut team performance.
•    Employee perceptions of fairness are essential to engagement.

Managing employee performance and engagement is not, in the New Normal, separate from the design of work itself.

Labor Cost Management and Work Design
For most of the past decade in the U.S., companies have sought improvement in business productivity through control of labor costs. Globalization allowed companies to pursue absolute advantage through labor arbitrage, primarily by relocating work to locations with lower wage rates. Some saw lowering wage rates as an alternative to process improvements and new technologies. But even most on-site ‘Business Process Reengineering’ sought to achieve and measure benefits by reductions in headcount. Tom Davenport, an early proponent of reengineering, concluded “When I wrote about "business process redesign" in 1990, I explicitly said that using it for cost reduction alone was not a sensible goal. And consultants Michael Hammer and James Champy, the two names most closely associated with reengineering, have insisted all along that layoffs shouldn't be the point. But the fact is, once out of the bottle, the reengineering genie quickly turned ugly.” Reengineering became synonymous with downsizing. It should not be a surprise that these strategies failed to deliver much real innovation or sustainable increases in employee productivity. In some cases, employees might tell you, they were simply stupid.

In the New Normal, Labor Arbitrage and Downsizing continue to have some momentum, but they haven’t delivered sustainable benefits and have degenerated into a ‘race to the bottom’ and ‘shrinking to greatness’. As competitive strategies, labor arbitrage and downsizing don’t work.
Confusing of labor rates with labor costs
Several years ago, a group of colleagues provided some analysis for a major grocery chain that had grown by acquisition. Retail analytics classifies stores into peer groups with similar market demographics. Stores operating under one brand were compared to stores operating under another. Both stores were unionized. Stores in the first group paid three times the hourly rate paid by the second brand group and invested much more in recruitment and training; workers in this group were more involved in management and there were fewer layers to store management. Stores with these higher wages and higher cost HR practices showed a profit of 24 dollars more for every hour worked and half the staff turnover.

Managers who seek to reduce labor costs by reducing labor rates may overlook fundamental issues of productivity and competitive advantage. An IT company may seek to replace one software engineer with a 100 dollar an hour cost rate by offshoring to a location where two or three engineers can be hired for the same cost. But such calculations seldom take into account the associated communication and management overhead, training costs, and infrastructure required to support the new deployment of work. Nor do they take into account possible impacts on quality and innovation.

A big box retailer sought to reduce labor costs by increasing the ratio of part time to full time employees thus reducing wage rates and benefit costs. To their credit, they recognized a problem in less than a year. A good part of their value to customers was in the knowledge of their store clerks and their ability to provide advice and the right products to customers. Faced with cuts in wage rates or hours, many of the most knowledgeable clerks left to work for a competitor. The retailer faced a loss in market share and same store revenues and profits.

Pfeffer concludes “any organization believing it can solve its attraction, retention, and motivation problems solely by its compensation system is probably not spending as much time and effort as it should on the work environment--on defining its jobs, on creating its culture, and on making work fun and meaningful.”

Designing Work in The New Normal
Gary Hamel, writing for McKinsey’s What Matters Blog , asks “Suppose, for a moment, that ten years ago someone had surveyed the top 100 executives in each of the Fortune 500 companies—that’s 50,000 business leaders—and asked them if they could imagine a time when a disparate army of volunteers from across the world, with no formal control processes, no budgets, and no real funding, could create one of the most complex of all products: a computer operating system. I doubt that one executive out of a thousand would have said, ‘Sure, this will happen.’ Yet the Linux operating system was developed in precisely this way.”

Similarly, Daniel Pink observes “The mid 1990s, Microsoft started an encyclopedia called Encarta. They had deployed all the right incentives. All the right incentives. They paid professionals to write and edit thousands of articles. Well compensated managers oversaw the whole thing to make sure it came in on budget and on time. A few years later another encyclopedia got started. Different model, right? Do it for fun. No one gets paid a cent, or a Euro or a Yen. Do it because you like to do it.
Now if you had, just 10 years ago, if you had gone to an economist, anywhere, And said, "Hey, I've got these two different models for creating an encyclopedia. If they went head to head, who would win?" 10 years ago you could not have found a single sober economist anywhere on planet Earth, who would have predicted the Wikipedia model.”

Google is one of a few companies that have found a way to tap into this energy source. They offer their engineers “20-percent time -- so that they’re free to work on what they’re really passionate about.”
Their jobs page defines the employee value proposition concisely:
•    We work in small teams to promote spontaneity, creativity and speed.
•    We listen to every idea, on the theory that any Googler can come up with the next breakthrough.
•    We provide the resources to turn great ideas into reality.
More than 20 percent of Google’s applications and services originate in this 20-percent time.

Something is happening here that few companies, and even fewer HR professionals, have taken into account. In contrast to the richly rewarded and highly incented talent in financial services that drove their companies and the entire economy into the ditch of the Great Recession, people using simple tools for collaboration have come together to deliver real value.

Those that figure out how to tap into and support the creativity and collaboration of labor are the likely winners in The New Normal.

Rethinking Competency Management

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“As a foundation process for talent management,” Leighanne Levensaler wrote in the 2008 in IHRIMlink, competency management “is business critical….Of 62 talent-related processes examined [in a study by Bersin & Associates], competency management is near the top in driving general business value.” Yet the same study revealed that only 6% of the organizations surveyed believed they had a world-class competency management process.

Cataloging and making workforce knowledge, skills and abilities visible has been seen as a key to integrating the other components of a Talent Management suite: workforce planning, recruiting and staffing, learning and development, and performance and compensation management, and succession planning. Levensaler writes “Competencies serve as the common language to align disparate talent processes and are at the very core of any talent management strategy.” And yet, Levensaler reported in a recent presentation that an informal survey of early adopters, presumably from that 6% of organizations with a world class process, revealed that few made little or any use of the data captured in their talent management suites when making decisions about downsizing in response to the Great Recession.

There is a pretty fundamental problem here. Competency management is hard. To provide real value for both managers and employees, a competency assessment process must be seen as reliable, comprehensive and fair. Competency management applications that provide record keeping capabilities are only as valuable as the processes they support. Those processes can be expensive in terms of management and employee time, and that is an ongoing expense rather than a simple implementation cost.   Especially in difficult economic times, that investment is a difficult sell for talent managers.

A wide variety of knowledge, skills and abilities fall under the umbrella of competency management.

Many companies have defined some set of core and leadership competencies for use in performance assessment and succession planning. Most do a reasonably good job of maintaining records on required certifications and licenses for regulatory compliance. Job related and career path competencies are more difficult and less cataloged. Some difficulties are specific to each type.

The problem with assessments of core and leadership competencies used to identify ‘high potentials’ and successors is that such assessments are often self-fulfilling. An employee identified as ‘high potential’ will likely receive additional training, mentoring and rewards and progress through an organization more rapidly. There is nothing particularly wrong with this, but the process usually leaves open to question whether others receiving similar support would have similarly progressed.   Successors are usually identified by the match of their particular competencies and experiences to those of their predecessors. In many cases, this is appropriate, but it is not exactly a recipe for innovation or creation of new direction in new business conditions. And from such practices, glass ceilings are constructed.

The relative success many companies have enjoyed in tracking certifications and licenses builds on HR’s role in managing compliance with regulations. In some cases, information about certifications earned by employees may provide some flexibility in short term staffing by identifying candidates to fill roles that are not defined by their current job description. For example, a pharmacy chain found that some clerks had earned a pharmacy tech certificates and could be made available for tech roles to meet staffing shortages. Extending this certification model, using standardized testing even when not required by regulation, has helped some companies shrink applicant pools and identify pools of successors.

Tracking Functional and Career Path competencies or skills presents a different set of problems. Setting the right granularity for skill assessments is a challenge. Two employees may test equally skilled in a particular process or technology, for example, but one may have a much better grasp of concepts that will allow her/him to adapt to the next generation of processing or technology. Similarly, an employee may display competency in a particular skill at some point in her/his career, but, although few competency management systems include an expiration date on assessments, that skill may deteriorate over time through disuse or changes in technology. As importantly, many competency management systems catalog functional competencies at a granular level and don’t provide means to identify evaluate workers by combinations of competencies. The result is sometimes staffing teams with a large number of people with discrete skills – and an associated communications overhead – rather than workers with the right combination of skills and experience.

All of these problems contributed to the slow adoption and limited use of Competency Management at the core of Talent Management programs. The Great Recession and the emerging New Normal change the problems significantly. If limited investment of management and employee time limited abilities to implement and sustain a credible and reliable competency management process in richer years, those limits are even greater in the competition for investment in the New Normal environment. If we are entering an era of ‘Free Agents’ and will rely on 25 to 40% contingent labor, strategic partnerships and outsourcing, what contribution can competency management make when the ‘company’ doesn’t ‘own’ key resources? What information do we need in order to develop workforce and succession plans, engage and retain the right people, and seize opportunities for competitive advantage through people?

The answers to these questions are as uncertain as the overall economy. If we keep in mind the new problems we are trying to solve, rather than focus on old solutions, some directions are worth consideration and experimentation.

What management wanted from Competency Management was some measure of the value of their human capital and of individual employees. If that measure could be reliably established, then trends and the impact of management investments and initiatives could be determined and better decisions made.

To get to that measure, we need to look critically at some metaphors.

Sometime around the turn of the century, the definition of people management processes moved from “Human Resource Management” to “Human Capital Management”.   Human Resource Management, using Customer Relationship Management as a metaphor, focused on efficient delivery of services to employees and the employee ‘experience’; it established shared service centers and web based applications for employee self-service. Human Capital Management shifted emphasis to a Supply Chain Management metaphor; it focused on efficient delivery of labor power, balancing supply and demand, to meet business objectives.
Employee value in this new perspective could be measured by capacity and capability and measured against cost; competency management, in this supply chain metaphor, became management of available inventory of knowledge, skills and abilities.

The Supply Chain metaphor drove some progress: a focus on capacity management and workforce planning tied to business objectives, development of just in time methods for training, etc. It also had a limit in describing employee value. In a traditional view of a supply chain, raw materials enter a production process and the value of those raw materials is fully consumed in production and delivery of finished products. “Human capital”, on the other hand, is, in most cases at least, not fully consumed in a production process but potentially emerges enhanced with additional experience and skills.

What Talent Management needs is a means to recognize and measure that added value.   That value is developed in multiple ways. Position and project assignments allow employees to demonstrate and develop functional and career path competencies; those competencies can then be assessed and evaluated in conjunction with performance management processes. Tracing actual employee movements through assignments can, if work history is visible, can help to define career paths empirically rather than on an a priori basis and lead to some surprises. A company found a valuable internal pool of potential project and sales managers in its pool of administrative assistants, for example. But maybe most importantly, the value added to employees through experience is expressed in the social network of relationships they establish in that work. Peers who rely on teamwork and collaboration as well as the skills of their teammates, have a unique view of the value of those employees. That value can be measured not only through peer and 360 degree assessments, but also through analysis of the social networks that are established and persist beyond specific assignments.

Social Network Analysis is not a new concept.   Methods and metrics are well defined in a science of network management: e.g. Betweeness, Clustering coefficients, cohesion, geodesic distance, local bridges, and Eigen vector centrality. In the 1930’s, Warner and Mayo at Harvard developed the systematic study of relationships within a workforce. But little of this work has found its way into common practice in workforce planning and management.

In part the obstacle has been that early social network analysis required a good deal of field work to map the actual connections between workers in a workforce. Today however, much interaction – in emails, on-line forums and wikis, and social media – generates that data as a part of the communication itself. Social media sites like LinkedIn, now with 54 million members, map connections and degrees of separation between workers both inside and outside of company boundaries. Some companies have created their own social media sites, blogs, knowledge bases, and other means for interaction beyond standard reporting relationships. Vendors like Jive provide software for internal implementations, and some like Ning even provide a free service to create social networks that can be either open to all or restricted to invitation. Many of these sites provide some simple measures of connection and distance. Software for more sophisticated analysis is increasingly available – some as open source ‘shareware’.

What does social network analysis have to do with measuring the value of an employee and a workforce? There are several, reasonable assumptions involved in this argument. First, since connections and interactions in social media are voluntary, those connections represent the value co-workers find in relationships that provide access to knowledge, skills and abilities. Measures like Eigenvector Centrality measure the importance of a node in a network. It assigns relative scores to all nodes in the network based on the principle that connections to nodes having a high score contribute more to the score of the node in question. Radiality measures the degree an individual’s network reaches out into the network and provides novel information and influence. Second, it is a common observation that much of the work to be done in an organization today requires collaboration and teamwork. Thus participation in social networks is not simply a ‘free time’ activity but an essential way in which the work of innovation and quality improvement gets done. The value of a worker’s competency is determined not by obtaining a rating in an assessment process but by the exercise of those competencies in a collaborative environment.

Consider for example the decision making process in many organizations in the face of the Great Recession. Many found no alternative to layoffs as a means to reduce costs. “The risk,” notes Stacy Chapman of the Workforce Planning firm Aruspex, “is that the workforce is often the key to competitive advantage and to creating long term value – so all workforce cuts also potentially jeopardize value and long term success. The key is to target cost savings at the areas with the least risk – and investments in those which create the most value.” Many HR professionals share this worry today. Lacking sufficient data to make strategic decisions, many organizations spread cuts evenly across their organization like peanut butter to ‘share the pain’.

Some suggest that a more robust system for cataloging inventories of competencies and identifying top talent could prevent mistakes and loss of key talent in the next downturn. But if investments in a competency assessment process were limited in better times, those investments will be harder to come by in more difficult times.

A problem with ‘peanut butter’ cuts, and even with some more strategic cutting, is that the framework for distribution of those cuts was established by organization charts and formal reporting structures. A real risk is that cuts can eliminate key nodes in a social network damaging cohesion and disconnecting groups of collaborators. As illustrated in the figure , the significance and value of a node in a social network may not be visible in a formal organization chart. The result of cutting critical nodes in a network is at minimum disruption and a blow to morale and engagement. As a manager noted “I’ve just made 35 people redundant, and now 2000 think the company sucks.” Those disruptions will likely show as well in bottom line productivity.

Social networks don’t match formal organization charts. They are also not necessarily constrained to organizational boundaries. In this way, the development and evaluation of social networks speaks to the issues of a growing contingent workforce.

Few would suggest the development of a permanent and comprehensive competency management system for contingent workers, yet evaluation of proposals for contingent staffing is a critical task for organizations increasingly relying on contingent labor and partnerships. Although many contingent workers and their vendors have an ongoing relationship with companies, engaging and disengaging with demand for labor, most of those evaluations take place on a contract by contract basis. The competency, productivity and quality of contingent labor is managed by performance against statements of work and service level agreements – lagging indicators that generate crises when objectives change or are not met.

Engagement of contingent workers in an ongoing social network can potentially provide more predictive capability to assess the value of contingent labor. By establishing visibility to work histories and relationships, knowledge sharing, and development beyond and between contracts, social network analysis could improve planning and decision making. Use of social networks can provide visibility into factors as simple but essential as availability of contingent workers but also provides opportunity for ongoing engagement of contingent labor with business objectives and a contingent labor value proposition, a kind of “enterprise branding.” The value of social networks extends to free agents as well as to employers.

Can social network analysis completely replace competency assessments and catalogs? Probably not. But this perspective may suggest turning current approaches to competency management on their head. In the face of limits on investment and ongoing operational costs of credible competency management programs, some have suggested segmenting the workforce into strategic and commodity segments and focusing competency assessments on strategic workforce segments. But the analysis here suggests the reversal of that focus. Standard testing, credentialing, and certifications are reasonable means to measure commodity labor. The value delivered by strategic labor may be better assessed by identifying those key nodes in social networks that create value.

Defining the New Normal

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Ever since the name of the function changed from ‘Personnel’ to ‘Human Resources’, HR professionals have sought ways to articulate and especially to measure the value Human Resource Management adds to an enterprise. While many organizations’ vision and mission statements declare ‘our people are our most valuable asset’, HR organizations have faced serious internal competition for capital investment to realize that vision and deliver sustainable competitive advantage through people.

In recent years, the value added mission of HR has come to be described as ‘Talent Management’. ‘Talent Management’ describes a strategic functional role beyond common requirements for ordinary transaction processing; it aims at delivering ‘the right people, with the right skills and the right motivation to the right jobs in the right place at the right time and at the right cost.’ Driven by concerns about a shrinking traditional workforce due to baby boomer retirements, the demands of globalization and opportunities for labor arbitrage, and rapid changes in production methods and technologies, proponents of ‘Talent Management’ aggregated traditional HR activities like training, recruiting, performance and compensation management, succession planning, under a common strategic umbrella to meet those ‘right’ objectives. Talent Management reinforced a focus on some traditional metrics but also introduced new ones: a focus on retention of key talent, quality of hire, career progression and succession planning, and especially on cataloging and measuring the workforce inventory of competencies. Strategic workforce planning came to the forefront as a discipline aimed at competitive advantage.

Then came the Great Recession. No enterprise was immune from its effect. We face an uncertain ‘new normal’.

While some companies continue to pursue Talent Management strategies designed for the challenges of the ‘old normal’, the Great Recession hit talent management strategy like a tsunami, exposing limitations in those emerging practices and revealing new challenges.

Certainly some of the insights and ambitions that fueled the drive toward strategic talent management remain valid: how workers are acquired, engaged, and developed as well as rewarded can provide a real competitive advantage. But the conditions for these actions – and some measures of process and results – will likely be significantly different.

A Change in the Labor Pool
While the problem of longer term labor shortages due to retirements and changing demographics still exists, labor markets in many parts of the world are now overflowing. Unemployment rates in the US and Europe remain above 10% and the numbers for ‘underemployed’ drive that percentage much higher. The result is a large pool of available talent, and applicants threaten to overwhelm the capacity of many recruiting organizations to evaluate talent and make good choices for hiring.

On the other hand, some have suggested that the large pool of skilled talent offers aggressive companies a chance to do a ‘talent upgrade’ – replacing existing workers with more highly skilled workers that have now become available. The economics of the basic ‘build or buy’ questions for talent development have shifted with the large pool of experienced skilled labor now looking for jobs.

And even a recovery that allows the millions of job seekers a better chance to find employment will not likely shrink this pool. Recovery will likely turn many, who have held their jobs in recession but suffered pay cuts and freezes while seeing colleagues downsized, from passive to active candidates for new employment.

The Challenge to Re-engage Employees

A study by IBM’s Institute for Business Value several years ago identified Employee Engagement as the most commonly used HR metric. A number of studies have provided some statistical evidence of the impact of employee attitudes on bottom line results. In a Harvard Business Review classic study, Sears found that in increase in employee satisfaction was a leading indicator of customer satisfaction and then of increasing same store revenues. But in the wake of the Great Recession, a Conference Board survey found employee satisfaction at the lowest level in more than two decades. 61% of employees under the age of 25 are dissatisfied with their employment situation.

The New Normal has removed or at least temporarily disabled many of the levers HR has traditionally relied on to engage employees. Compensation budgets have shrunk (except it seems for some bankers). Bersin and Associates report similarly shrinking budgets for training. Downsizings certainly impact Employee engagement and loyalties. Ironically, one result of the Great Recession has been that older workers, the ones with skills we were worried about losing as baby boomers aged, now are extending their employment when possible to recover lost retirement funds. As a result, younger workers find their path and their motivation to gain more senior positions blocked.

A Permanent Move to Contingent Labor?
Something is different about recovery from recession this time. So far, economists have dubbed it a ‘jobless recovery’. Faced with ‘build or buy’ decisions about resources, many look to ‘rent’. Companies looking to increase production capacity in the face of economic uncertainty hire temporary labor and outsource more components of their production processes. Some see this as a temporary stage in recovery, a leading indicator of future full time hiring as economic confidence increases. But others, noting that the recovery has seen a significant rise in productivity, project a permanent change. In some industries, estimates are that the ‘new normal’ will see a workforce made up of up to 40% contingent labor.

“Gone are the days when a human resources executive can just focus on full-time exempt and nonexempt W2s while ignoring how other segments of the company's workforce are being recruited, hired, and managed,” writes Joe Caparella in Business Week, “Now HR and corporate managers must carefully evaluate every aspect of their talent supply chain in order to maximize cost-efficiency and productivity.”

There are good reasons to believe this shift is permanent. Even before the Great Recession, Daniel Pink documented the shift to a ‘Free Agent Nation’: “The residents of Free Agent, USA are legion: Start with the 14 million self-employed Americans. Consider the 8.3 million Americans who are independent contractors. Factor in the 2.3 million people who find work each day through temporary agencies. Note that in January [2008] the IRS expects to mail out more than 74 million copies of Form1099-MISC -- the pay stub of free agents.”

In part, this shift was driven by generational differences in the expectations of employment. The mass layoffs following this financial crisis dealt a final death blow to long term loyalties to companies by workers of all age cohorts. The impact on recruiting strategies, ‘employer branding,’ and employee value propositions will be profound.

It would be a mistake to view this shift through old lenses and see contingent workers comprising only ‘non-strategic’ temporary administrative, clerical and general labor. The contingent workforce now includes business and technical consultants and coaches, trainers, marketers, engineers, software developers, and other professional contingents. Outsourcing and strategic partnerships also provide alternative means to deliver the labor necessary to new business objectives. A good portion of these workers, whatever their employment relationship, will be deeply embedded in the value chain of many organizations: from research and development, through manufacturing, marketing and sales.

A New Regulatory and Risk Management Environment
In the wake of the Great Recession, both regulators and shareholders demand greater accountability and transparency from employers. Controversy about executive compensation and ‘obscene’ bonuses generates demand for new taxes from government and increased transparency from financial analysts and shareholders. In the wake of the massive layoffs of 2009, shareholders demand broader reporting on workforce capacity and capabilities from companies.

In the U.S., a second stimulus package, under consideration in early 2010, may include incentives for new job creation and an advantage for those employers who can determine the best deployment for new labor capacity. New restrictions and tax disincentives for outsourcing and offshoring under consideration in the U.S. Congress may make already shaky strategies for ‘labor arbitrage’ less of an option for controlling labor cost. The Equal Pay act of 2009 increases the stakes for pay equity and the liabilities for discrimination in pay. If Health Insurance Reform in the U.S. moves toward universal, affordable health insurance coverage and real portability, the mobility of some workers will be increased.

So the New Normal confronts companies and HR departments with new challenges while the old remain.

Talent Management and the New Normal

2010-02-01T12:49:00.008-06:00

“One of the most salient features of our culture is that there is so much bullshit. Everyone knows this. Each of us contributes his share. But we tend to take the situation for granted.”

Harry G. Frankfort, On Bullshit

The end of a decade always spurs a good deal of discussion about the future. We especially need that now since, in the wake of the Great Recession, economists and increasing evidence point toward a conclusion that economic recovery will not return us to an accustomed state of economic competition but will create an uncertain ‘New Normal’.

I’ve tuned in to a good deal of this discussion in the blogosphere and on webinars sponsored by major HR consultancies and vendors. It has been disappointing. There have been some moments of honest appraisal. Leighanne Levansaler, now of Workday, reported that few early adopters of talent management suites actually made much use of the data captured in those applications to make decisions about layoffs when the recession hit. Heidi Spirgi, of the consultancy Knowledge Infusion, declared flatly “Phase 1 of Talent Management has been a failure.” But another webinar, under the promising title “Talent Management and The New Normal” offered no assessment of the ‘new normal’ and instead simply repeated the prescriptions of the past: the value of an ‘adaptable’ workforce, global integration, cost management and innovation.

Much of the advice offered in such sessions and postings amounts to working harder and investing more in the solutions of the previous decades. Some of it is, simply put, bullshit. In a down economy, it is unlikely to expect greater investment in practices that did not deliver promised value in better times.

We need an honest assessment of the ‘new normal’ and what people management practices can produce measurable value.

How will the ‘New Normal’ impact the way people are managed and labor power delivered to meet business objectives?
How do we measure the value of employees’ contributions and capabilities and candidates’ potential? Are there alternatives to expensive and sometimes unreliable systems of “competency management”?
How do we manage employee performance with shrinking budgets for merit pay and incentive compensation? What value proposition do we offer employees in the ‘New Normal’? How can we rebuild engagement in the face of increasing job dissatisfaction and declining employee ‘loyalty’?
How can we manage labor costs and improve productivity?
How do we determine and deliver appropriate workforce capacity and capability? In the conditions of the ‘New Normal’, how do ‘talent managers’ deliver ‘the right people, with the right skills and the right motivation to the right jobs in the right place at the right time and at the right cost.’
How do we develop the capabilities and skills of our workforce? What are the most appropriate investments given shrinking training budgets and a highly mobile workforce?
Can HR make a difference?

We need to begin with an assessment of the New Normal, but The Great Recession also exposed limitations and false assumptions in the way even the most advanced companies manage talent in their workforce. The challenge is to reexamine those assumptions and rethink how we can deliver on the promises of Talent Management.
To do that, we need to examine each of the components of a Talent Management program critically and honestly.

Please Join these Discussions

Good Stuff from other places....

2010-01-19T15:37:00.000-06:00

Some resources for thinking about Talent Management in the New Normal

Jeffrey Pfeffer testimony on compensation and performance management before the US Congress:
Commentary: Research & Practice: Evidence-based Management (EBM)

Gary Hamel on forces transforming management:
http://whatmatters.mckinseydigital.com/organization/three-forces-that-will-transform-management

Dan Pink at TED takes on the science of performance management
http://www.ted.com/talks/dan_pink_on_motivation.html

Workforce Analytics and Planning in Kuala Lumpur Malaysia

2009-11-30T14:11:00.000-06:00

I had the opportunity last week -- 24-25 November -- to teach a class on Workforce Analytics and Planning in Kuala Lumpur Malaysia that was organized by CoreVentus. As almost always, the teacher had as much to learn from the students as the students, hopefully, learned from the teacher. It was a very broad class, covering Analytics from three functional perspectives -- Decision Support and Visibility, Workforce Planning and Strategies, and Performance Management. It was striking how many issues in Workforce planning and management are common across the world, or at least 14 timezones -- how to optimize the composition and deployment of the workforce; how to balance workforce supply and demand; how to motivate and measure workforce performance? And also striking how the participants in the class are addressing these questions and issues.
The agenda was very full. So this is a place to continue the conversation and an open invitation to those who participated in the class to offer new perspectives and ask new questions. Thanks to all for your participation.

Myths, Half-truths, and Corollaries

2009-09-08T09:30:00.026-05:00

"You can't manage what you can't measure", sellers of Business Intelligence solutions and especially those analytic applications for HR will tell you. It is a compelling argument for those buyers with 'manager' in their job titles or job descriptions. But at best it is a half-truth and depends on a somewhat constrained definition of 'management'.
Managers provide coaching and mentoring to their employees, make strategic decisions based on insight gained from experience, and communicate goals and business objectives in a variety of ways. Certainly, the 80% or so of HR Managers without a robust system for workforce analysis are doing some things that are of some value.
They could however be doing better.
A better rule is 'You get what you measure'; although some years ago in IHRIM Journal Robert Stambaugh offered a corollary 'You fudge what you measure.' The central premise of Performance Management is that measures communicate strategy and objectives and provide incentives to performance. Say for example, that you set a goal of developing key performers within an organization. To communicate that goal, you put metrics in place to measure the number of employees promoted into positions of greater responsibility by manager or business unit. Such a measure will focus management attention on employee development and identification of high potential employees and provide upper management visibility into which line managers are executing that strategy.
Considering Stambaugh's corollary, there is another important corollary: If you get what you measure be careful how and what you measure.
A number of years ago, some colleagues did a study of performance in an out-bound call center. Managers there were evaluated and rewarded based on the monthly performance of their teams against monthly sales quotas. The problem was that only those call reps on the roster at the end of the month were included in the metric. So some managers, following Stambaugh's corollary, figured out that if they fired those reps not meeting their quota before the monthly measure they could improve their 'management' rating. From the individual manager's perspective, this was entirely rational behavior; it improved their monthly bonus. But from an overall perspective it was a disaster. The metric did not take into account the cost of replacement hiring and training nor the history of employee performance -- some previous top performers may have randomly gotten a 'bad list' for that month. Naturally too, this practice had a terrible effect on the voluntary termination rate, teamwork, and morale within teams. There was no metric, and thus no direct incentive, for managers to spend time working to improve employee performance.
This is an egregious example of a more widespread, and often more subtle problem. A compensation analyst at a recent client noted that metrics for 'time to hire' and 'acceptance rate' for recruiters gave incentives for hiring above the average pay rate for employees in similar positions and created future compensation problems.
None of this is to argue against measurement. The half truth is partly true. Managers at all levels need baseline workforce information and metrics to do their jobs well, especially in current times. An HR exec at a large biomedical company who had just overseen an HR transformation effort to build an HR shared services center told me: 'we've saved money by reducing our field staff in HR, but I've lost some feedback as a result and don't get early warnings of problems with turnover or recruiting or capacity management.' Managers need baseline workforce metrics -- headcount, hire and termination counts, compensation, etc. -- to identify issues and plan actions to address workforce capacity and performance. And it is important that these metrics be defined consistently and reliably to prevent 'fudging'. Another HR exec told me: 'when I ask units to send me basic headcount data, I can see some managers trying to figure out why I'm asking. I'll get different numbers depending on what they think I'm going to do with the numbers -- allocate a bonus pool, order new chairs, or plan layoffs.'
It is important in defining and distributing even these baseline metrics to be clear about the intent of the metrics and to be vigilant for unintended consequences.

Getting Started: Finding a Starting Point for Workforce Analytics

2009-07-14T10:48:00.000-05:00

So...you've developed a value proposition for workforce analytics, and begun to address the challenge of a roadmap for incremental development of analytic capabilities . We've agreed to start small to manage the required investment and the risks and unknowns of development. We are left with the question of defining in some detail the scope of the initial effort, finding a starting point.
It is important here to remember that the goal here is building a capability to solve real business problems and meet business objectives; it is not just to create an analytics capability for its own sake. But the challenge is that in order to identify critical business problems some analysis may be required. A few years ago, a public sector organization was shocked to learn through a very simple analysis exercise that more than 50% of their current workforce would reach retirement age in the next decade -- a major staffing and development challenge they hadn't seen coming. In some other cases, a perceived business problem may be poorly defined or presume a solution without analytic backing. A CIO came to HR with a request for a 3 million dollar annual increase in compensation budget to meet industry benchmarks and stem a turnover problem. Data analysis, confirmed in some focus groups, found little correlation between pay changes and voluntary terminations and instead directed attention to opportunities for job advancement, and vesting in a company stock 401K plan.
Both of these examples point to the virtues of starting small. Real business insight and measurable value can be delivered from a small initial scope. An analytic capability is tactical but essential to transforming HR management and optimizing the contributions of the workforce.
That scope needs to be defined in at least 3 dimensions: content, function and delivery, and people.

Defining initial analytics content: While it is true that few organizations have a fully realized analytic capability in place for workforce metrics aligned with business objectives, it is also true that few lack any analytic practice or capablity. Often these capabilities are isolated in particular funcational areas like staffing, compensation or learning.
Some staffing applications provide reporting capabilites around standard metrics for time to hire, cost to hire, offer acceptance rates, etc. Compensation planning may provide reports on compensation in relation to salary surveys, compa ratios, and projections of cost for pay raises and variable pay. Learning Management Systems may provide reporting on enrollments and cancellations, certifications, failure rates, and costs.
Most of these measures are focused on operational efficiencies rather than effectiveness and leave many open questions. What is the success or failure rate for new hires? What is the net impact on workforce capacity from hires and terminations? What is the impact of compensation strategy or training delivery on retention and performance? Who requires training? What is the best balance of standard and variable compensation? Should staffing focus on entry level or experienced hires? How do new hire offer rates and pay compare to pay rates for existing staff and to terminating staff?
These pockets of existing metrics and analytic capabilities and the questions they leave open are important to consider in defining an initial content scope for an integrated workforce analytic capability. What's most often missing are standard, accepted definitions for metrics across functions and, probably most importantly, what business intelligence people call 'dimensionality' -- the ability to dissect enterprise level metrics to focus analysis on key points of variance by a variety of fundamental characteristics: job groupings and organizational assignments, work locations, employee demographics of age, gender, and ethnicity, employee tenure ranges and work experience and status, salary plans and compensation rates etc.
The good news is that most of this information can be derived from fundamental data in your HRMS. So the recommended starting point for content is to put these fundamentals in place. Establish standard definitions for basic transactional metrics for hires, rehires, voluntary and involuntary terminations, promotions, transfers, pay changes and the ability to accumulate and analyse these measures over time. Provide standard analytic dimensions for analysis of those measures. That capability will provide visibility to correlation of metrics -- say for the impact of hires and terminations on workforce capacity -- and the ability to drill down on metrics like termination by job, organization, work location and other dimensions to determine local problems and challenges. It should also provide the ability to view those fundamental metrics in a historical context -- how does this state compare to our position a month or a year ago? Is this metric trending in a positive or negative direction? Is this problem an anomaly or an historical pattern?
Don't expect the starting point for content to solve strategic business issues; rather starting with fundamentals should provoke additional questions, support new HR and management initiatives and investigation, and help to set priorities for future expansion of analytic capabilities through the definition of new metrics and dimensions and additional data integration.

Defining an initial user population and rollout strategy: It should be obvious that the real value of a workforce analytics capability comes from its use. The most value comes from broad adoption when analysis and 'evidence based HR' becomes embedded in the way business is done and the way the workforce is managed on a daily basis. But some attempts to implement a workforce analytics capability have floundered -- despite successful implementation and operations of an application -- due to a lack of adoption by target users.
If the goal for developing an analytics capability is transformation, developing that capability demands a focus and strategy for development of a broad user population in both HR and operations. That challenge is at least as great as getting the technical application right and should be the focus for much discussion on this blog.
What this means is that your strategy for an initial deployment of an analytic capability needs to address communications, change management, training and development of analytic skills, and the organizational impact of those needs.
Reliability and consistency of data is a critical success factor for acceptance of any business intelligence system. It is easy for any such system to lose credibility with users and often difficult to regain it. For HR data this can be a particular challenge since, especially when viewed in an historical context, HR data can be, well, funky. Frequently, the quality of HR data is only called into question in source systems when it impacts immediate transaction processing needs and putting that data in an analytic framework can expose a good number of incomplete, inconsistent and inaccurate data elements.
The problem is further complicated by the fact that while an organization may lack a common store of analytic data for HR, there are likely many managers in the organization maintaining their own data and metrics in individual and departmental spreadsheets and data bases. Metric definitions may vary and the refresh rate for those systems will likely differ from that of the common store and thus generate arguments about whose data is correct rather than actionable insights. Those problems can exacerbate some managers resistance to change in the introduction of any new performance measurement system.
So a critical success factor from the initiation of development workforce analytics is identification and training of a core group of resources committed to the transformation promised with analytics. The organizational form of that group may vary with the scale and culture of an organization: a dedicated Workforce Analytics Center of Excellence, a more loosely structured community of practice, or just a few dedicated tier 2 consultants in a shared services center. That group, however organized, should be the initial target for deployment of an analytic application in order to become familiar with the capabilities, data and metric definitions, and operations and navigation of the delivered information. They serve as a pilot group that can assist other users in analysis as the capabilities are deployed more broadly. Their role, in a friend's phrase, is 'to help the data tell stories'. They also need to be able to explain differences between existing local and enterprise standard definitions of fundamental measures and assist in a process to correct data omissions and errors in source systems.
The next tier of users should include HR and business executives, HR business partners and some key operations managers who have the authority and ability to define new initiatives and programs to deal with issues uncovered with analytics.
Timing for a broader deployment may vary. As with analytic content, the priorities will be affected by the insights gained from development and initial deployments. For deployment to line managers, the focus changes from using metrics to identify issues and problems and support planning to using metrics to communicate strategy and monitor execution.

Determining initial function and delivery capabilities: There are many ways to deliver analytic data and capabilities to users. Selecting the right modes for delivery is key to initial success in adoption and to developing a roadmap for future deployment. The kinds of capabilities that we are discussing here, the kinds necessary to support transformation, are not targeted to specialists with sophisticated understanding of statistics. Rather, we are looking for ways to deliver information and enable analysis as a discipline for a broad range of users with a variety of skills and a variety of responsibilities. Usability and accessibility are key considerations.
A core capability for any analytic application are interactive, metric based reports that allow uses to decompose -- a root meaning for analysis -- measures of results in multiple dimensions. That capability allows for discovery of variations in performance and relationships between measures that can inform action. There is a lot more to say about these capabilities that we will address in later posts, but some key considerations for usability are important. The key usability consideration here is that the core reporting and analysis capability is not 'ad hoc reporting'; the reporting capability should provide structure to guide the analysis and support enterprise wide standardization of metrics and key data categories. From a usability and accessibility perspective, it is also critical that analytic reporting capabilities provide graphical as well as tabular representations of data -- many more users can visualize a trend or a correlation in a graph than can calculate the slope of a curve or a correlation from raw data points.
As capabilities are rolled out for and developed in a broader range of management, people who have broader responsibilities than analysis of workforce data, the mechanisms used to deliver information must provide more focus to allow managers to identify quickly the issues that need attention and provide some quidance on factors to be included in analysis. Those capabilities need to include scorecard, dashboard, and impact analysis to enable full use.

Workforce Analytics needs a Community of Practice

2009-07-14T10:37:00.000-05:00

I was responsible for development of a Workforce Analytics and HR Data Warehousing practice within Price Waterhouse and later PriceWaterhouseCoopers and IBM. Delivering those offerings required resources with a broad mix of skills – knowledge of HR, HR processes and data sources, enterprise performance management, business intelligence design and technologies. Those resources were spread across multiple organizations within the consulting business, but all were critical to successful delivery.
To develop those resources, I led the creation of a community of practice. The community was an informal association of people from different groups but established regular communications through a weekly phone call exchanging experiences and problem solving across multiple engagements, a common repository of documents and tools, and face to face meetings and training sessions.
Over time, we grew the community to more than 60 members with experience in more than 80 client engagements. My role was to facilitate communications within the community and with the leaders of the different practice areas involved. I helped to charter and gain some funding for working groups that developed standard solutions and ‘accelerators’ to standardize methods and improve the value of client deliverables; I led and supported development of additional marketing materials and proposals. Delivering those assets was critical to building and maintaining the support of the home organizations for the key community resources.
Communities of Practice are valuable in developing resources, solving common problems, and improving client delivery. They also, as informal groupings, depend critically on the support of sponsors and champions in other organizations.
The following is from my posting on a Community of Practice group blog hosted by LinkedIn in response to a question about what an enterprise can and can’t do to foster the development of communities of practice.
Communities of practice are real but often fragile. I suspect that a sophisticated social network analysis would discover real, unrecognized communities of practice in most companies. CoPs are the way much work really gets done -- out side of standard organizational reporting relationships.
CoP's exist to solve problems. The key question for maintaining their viability and vitality is 'whose problems?'
Certainly there is increasing recognition of the business value that communities provide companies in reduced formal training costs, increased productivity, and cost efficient knowledge sharing. Indeed the communities that I've been a part of and led (in PW, PwC, and IBM) delivered many hours of 'volunteer' work on evenings and weekends to develop 'accelerators' that increased sales and sped delivery to clients, and for many, myself included, became, as William says, 'the single most rewarding part of my job.'
But to maintain vitality communities of practice must maintain a focus on the real needs and problems of practitioners. What sorts of needs and problems?

1) A sense of community. In an increasingly virtualized world, practitioners need a sense of a larger whole and mutual interests. When company loyalty is replaced by downsizings and outsourcing, informal communities can provide people with a sense of commitment to something more than themselves, a sense of engagement and trusted relationships. In a project environment where teams are frequently configured and reconfigured, engagement in a community provides insight into the skills and abilities of other practitioners that is difficult to replicate in a more formal talent management system.

2) Immediate -- unmediated -- help in solving problems. Long ago, Gerry Weinberg told a story about a decline in productivity when distributed printers replaced the need for programmers to wait (and socialize) outside a computer room for program listings. Calls to a help desk were not as productive as informal questions that brought new eyes and new experiences to programming bugs during that 'unproductive' wait time. Communities should give practitioners a sense that people 'have their back' in sometimes isolated delivery projects to solve problems that are technical, client relationship, and even in navigating internal relationships between business units.

3) A sense of personal development and learning. There is a fine line here however. Solving problems in community helps each practitioner to develop and broaden their skills by providing a broader base of experience than that of any individual. But Communities of Practice can lose their purpose if they become a forum for self-promotion. The best community I've been a part of was able to influence resource assignments to provide community members with increasingly challenging work with support from the community as a whole. Key here is actively engaging and enabling participation from every member of the community.
The difficult challenge here is scalability. A community that isn't open and encouraging to new members can devolve into a clique. On the other hand, it seems that the sort of community that I've described and experienced is something different than the community of 5000 members that Mark describes. Perhaps communities have a natural life-span before turning into something else.
I am afraid that some of the good work that has been done accounting for the real business benefits of communities has generated some unintended negative consequences. Enterprise support requires some subtlety and shouldn't focus simply on the metrics that are easy to measure like utilization rates and revenues. Certainly management should provide some recognition and some room for participation, but formal recognition and performance ratings should follow real performance and development that delivers real results. Otherwise the management temptation to get more easily measurable value from the community itself can undercut rather than increase the value of community to its participants.
In happier economic times, management supported the growth of a community of practice with a semi-annual face to face meeting of the community as a whole as well as working group sessions, some relaxation of utilization goals and influence on project assignments, and some community member shadowing and/or visits to other project sites in addition to a weekly community conference call and an online knowledge base repository. That resulted in a rapidly growing practice of as many as 120 practitioners that supported and harvested knowledge from more than 80 client engagements over three years.
Less happy times meant tighter cost controls and utilization targets -- at best as William describes ‘a few hours for leadership’ -- and declining participation in the face of demands for greater scalability. There is a tendency in such times for communities to devolve into something else, with a focus on top down communications of new products and offerings without a direct connection to the current practice and problems of practitioners and less bottom up participation. Communities can become simply a distribution list for such communications and scheduling of webinars and product demonstration. That’s not without value, but it is something different than an active network of practitioners collaborating to solve common problems. In the worst economic times, communities go underground and transform into networks helping practitioners to find new jobs.

Measuring Training Effectiveness.

2009-06-26T11:50:00.001-05:00

Cross-posted from a discussion on the HR Toolbox site around the question of measuring 'Training Effectiveness."

Before determining the effectiveness of training, it is important to sort out the objectives for training programs and offerings.There are a number.

1) Some training is mandated; those requirements will vary geographically by nation and state. In the US, training is mandated by government regulation for FDA Validation requirements, OSHA regulation, and by some professional organizations and associations. In these cases, the first measure of effectiveness is maintaining an adequate workforce capacity with appropriate certifications to meet labor demands.

2) Some training is essential to provide employees with base skills and knowledge to do their jobs. These types include standard on-boarding for new hires and training for affected staff in introducing new systems and new products. The first effectiveness measure in these cases, though not the only one, is timely delivery of the training to all affected employees.

3) More generally, training is aimed at developing employee skills and knowledge with two purposes. The first, of course, is improving the productivity and performance of the workforce in their current positions. But perhaps equally important, employee development is a key to the new employee relationship especially for younger generations. Development in this sense is a key to engagement and retention, replacing older relationships based on employment security and loyalty. Measuring effectiveness in these objectives is a challenge with multiple solutions that I'll return to in this note.

4) Similarly, other training programs are aimed at preparing employees for progression, developing a new generation of leadership. This objective requires metrics for the actual relationship between training received and career progression and, when available, between training received and readiness assessed as part of a succession plan.

In cases two and three, the best measures of training effectiveness are tied directly to variations in performance and productivity following training. These sorts of metrics are much more reliable and true measures of effectiveness than the subjective reports of employees or their managers. Beyond individual performance assessments, some of those data may be difficult to obtain and will vary by job assignments; the metrics may tie to increases in sales, in customer satisfaction, in manufacturing output, etc. Look at variations in performance and productivity between those employees in similar positions who have received training and those who have not. When such training metrics are key, it may be useful to segment the employee populations targeted and to roll out a new training program in a sequential order – measuring the effect on the first group receiving training before continuing rollout to others.

In case two, it is important to measure the impact of training on retention. But here some caution is necessary. Newer generation employees especially may view training as an important part of their employment relationship. But employees who receive training and develop new skills without a chance to use those skills may take them to another employer that affords them that opportunity. So measuring training hours lost through attrition is an important measure of overall employee development not just of training effectiveness.

Another set of issues for training effectiveness revolve around the multiple channels through which training is delivered. Training programs today may be delivered as traditional instructor led on-site classes, on-line webinars, or self-paced computer based or web based training. I’d argue as well that to meet the objectives above, we should broaden focus to ‘learning’ from formal training and include things like mentoring programs, tuition reimbursement, attendance at internal and external conferences, participation in on-line knowledgebases, blogs, and other user forums, and participation in communities of practice.

In these times particularly, the temptation is to measure channel effectiveness solely on the basis of cost, for example as ‘cost per employee hour of training’. Cost is certainly an important metric but by itself does little to measure the impact of training or learning on the multiple objectives noted here. Sub-optimizing around cost alone may be destructive and will not help to determine the most important content to be delivered or the most important populations to receive training. For those purposes, we need a more balanced scorecard that assesses impact on performance, productivity, progression and retention of key talent and high potential employees.