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Archive for the ‘analytics’ Category

The answer is 42

Posted by Ken Klaus on March 25, 2008


For those familiar with Douglas Adam’s The Hitchhikers Guide to the Galaxy, you may recognize the number 42 as “The Answer to the Great Question of Life, the Universe and Everything” given by Deep Thought after seven and half million years of computational analysis; and, as I’m sure you will recall, not everyone was happy with the answer. Poor Phouchg (probably the VP of HR) grasped the seriousness of the situation right away, “We’re going to get lynched, aren’t we?” While Loonquawl (I’m guessing he was the CIO) was sure the problem lay with Deep Thought (and by association the software vendor who supplied its programming), “Is that all you’ve got to show for seven and half million years’ work?”  But the problem, as Deep Thought explains, was not with the answer: “I checked it very thoroughly and that quite definitely is the answer. I think the problem, to be quiet honest with you, is that you’ve never actually known what the question is.”

Like those who were present on the great day of The Answer, many of us look to our software applications to answer the really hard questions around performance, potential, risk-of-loss, and succession. The promise of predictive analytics and the possibilities associated with data mining lead many to the false hope that the answers to these difficult questions lie buried deep within their data warehouses. Many C-level executives believe it is possible to quantify a persons potential or risk-of-loss in the same way a mathematician uses a predefined formula to discover an unknown variable. They long to replace the personal (subjective) aspects of the appraisal process with a dispassionate (objective) analytic tool. But the human experience is anything but objective. Our experiences, relationships, thoughts and feelings are as unique to each of us as our fingerprints; and the practice of measuring qualities like job satisfaction, potential, and performance requires a distinctly human touch.

Now before I get escorted from the building, let me clarify what I’m saying. Well defined competency models, clear organizational goals, and well integrated talent management applications are critical tools, which every manager should utilize, especially those who are new to their role. But managers must not abandon their responsibility in bridging the gap between the objective statistics generated from a data warehouse and the subjective nature of the human experience. As a colleague of mine is fond of saying, “managers need to have some skin in the game.” Calculating and calibrating a person’s performance and potential should be the natural outcome of a manager’s relationship with their employee and not a task to be completed once annually. Manager’s need to provide clear, honest, sincere feedback well before the appraisal period begins. This means meeting regularly with the employee, getting to know them, understanding what they like and dislike about their jobs, and helping them play to their strengths. These are tasks that can only be done by a person. Analytic tools may provide a good starting point for the evaluation, but they cannot replace the relationship between the manager and employee; because it is the manager, and not the application, who will understand that getting the right answer means asking the right question.

Posted in analytics, hr transformation, management | Tagged: , , , | 3 Comments »

The Mismeasure of Talent

Posted by Mark Bennett on March 12, 2008

A recent column from WSJ highlights the challenges facing us when dealing with the intangibles that often dominate talent work. It shows that measuring this “invisible work” is a challenge that often leaves talent without a sense of achievement. Moreover, when measurements are insufficient or incomplete, or when the wrong measurements are being used by management to compensate, it can cause more harm than good. When something is hard to measure, we know it often doesn’t get measured and what’s easier to measure gets measured instead. Since intangibles such as “quality”, “productivity”, “satisfaction”, etc. are seen as too difficult/impossible/imprecise to measure, they often don’t get measured. But we’ve seen in books like Patrick Lencioni’s “The Three Signs of a Miserable Job: A Fable for Managers (And Their Employees)” that immeasurability is a key destroyer of engagement. So what does that mean for your talent, where a lot of what they contribute isn’t easily measured, often doesn’t get measured, and thus makes it so they can’t assess their contributions or success?

There is a joke about the drunk who dropped his keys in the dark alley but spends all his time looking under the streetlamp “because the light is better.” That joke often comes up in discussions about measuring the intangibles related to talent. The column has several examples showing folks being measured on things that are readily available like timeliness and budget, but not on harder to measure things like “doing things right” for instance. Measures like timeliness and budget can be very important but often only describe part of the picture and are insufficient to making good business decisions. For example, how can you make the tradeoff between timeliness and “doing things right” that is acceptable from a risk/reward perspective if you aren’t measuring “doing things right”? What ends up happening is people start to focus only on the timeliness measure and both customer satisfaction and employee engagement falter because “doing things right” just isn’t happening like it used to, but nobody is really sure by how much or why (if it’s even noticed at all.)

Of course, the question arises of what does “doing things right” mean, but that doesn’t justify ignoring it. In fact, it misses an opportunity to actually figure out what it means so that it can be measured. Something like “doing things right” or “calming an angry customer” might be activities that produce the very outcomes the company really needs to achieve strategic success. The outcomes are something that can be measured and if you can find a relationship between those activities and an increase in desired outcomes, then you are on your way to making the intangible more visible and measureable. In addition, this helps the employee feel relevant by showing how their job really makes a difference. Measurability and relevance go together and support each other. They are employee engagement concepts that fit directly into a framework for making better decisions regarding talent.

It’s really management’s responsibility to provide the connection between the impact talent has on strategic success and what measurements should be used for determining talent’s effectiveness in achieving that success. Not providing a way to measure that contribution objectively and in the context of the company’s goals exacerbates employee disengagement. However, it’s also management’s responsibility to accomplish this by listening more to both employees and customers and tackling the challenge of taking that input and transforming it into useful measurements. Imagine what benefits would be gained if management listened more to the employees who knew about “doing things right” or to customers who were once angry but now satisfied, as described in the column.

Measurement does not have to be an “all or nothing” affair either. At times, it is sufficient to just know with reasonable confidence that something got better or worse (e.g. went up or down, perhaps) when an input changed. Other times, it’s enough to know with reasonable confidence that something went above or below a certain threshold, or cutoff point, without having to know by how much. Both of those can be determined with lower cost, for instance, than trying to determine exactly how much an outcome changes when an input factor is altered by a certain amount.

In addition to Lencioni’s book that shows how relevance and measurability impact employee engagement, check out “Beyond HR: The New Science of Human Capital,” by John Broudeau and Peter Ramstad. It shows how those two concepts fit very well into their HR Bridge framework that improves your strategic success through better decision making regarding talent. Also check out “How to Measure Anything: Finding the Value of Intangibles in Business” by Douglas W. Hubbard, which shows how to avoid the trap of trying in vain to be overly precise when measuring intangibles when in actuality the most relevant, useful, and actionable information might be obtained at a fraction of the effort. Much of that is enabled by having a purpose to the measurements defined by the framework presented in Beyond HR.

Posted in analytics, engagement, management | 3 Comments »

Yes, We are All Individuals

Posted by Mark Bennett on February 12, 2008

Number 6: What do you want?
Number 2: We want information.
Number 6: Whose side are you on?
Number 2: That would be telling, we want information, information, information.
Number 6: You won’t get it.
Number 2: By hook or by crook, we will.
Number 6: Who are you?
Number 2: The new Number 2.
Number 6: Who is Number 1?
Number 2: You are Number 6.
Number 6: I am not a Number, I am a free man!

The Prisoner (1967-1968)
We know that part of being able to improve business results through talent is being able to measure talent in the first place. There are challenges not only in being able to do that, but also in the resistance to doing that. A recently published book, “How to Measure Anything: Finding the Value of Intangibles in Business” by Douglas W. Hubbard, covers three common objections to the measurement of intangibles. The first, and perhaps only truly valid one from a business perspective, is the obvious economic one where the cost of measurement exceeds the benefit. The second is based on the common misunderstanding of statistics that it can be easily manipulated (“Lies-damned lies-and statistics”), which can be addressed through better understanding of statistics, probabilities, and risk. The third, and perhaps most relevant to the topic of talent management, is the ethical objection, based on the perception that it is dehumanizing and threatening to measure certain things about people, such as their value to the company (current and future), their risk of loss, and other touchy subjects that are often brought up in HR predictive analytics.

Hubbard argues that decisions are still being made regardless and that making those decisions under intentional ignorance could be worse, so measurements need to still be made. Of course, the question is which measurements are really useful and pertinent to the decision. That at least gets the ethical discussion going in the right direction. In fact, it bolsters the argument that:
  1. The decision should be connected to achieving business goals (i.e. the impact on success).
  2. The decision in turn should be driving what measurements are needed.
If the measurements are pertinent to the decision being made and the decision helps achieve business goals, there is at least a logical purpose to the measurements and therefore the benefits can be weighed against the ethical issues. This is better than mindlessly gathering data for its own sake, especially if the data sheds unflattering light on the population being measured.

What gets people upset is if the measurements have an error factor (and almost all do), then a member of that population runs the risk of being described unfairly or worse yet, completely false. In addition, if there are correlations between the measurements made on a person and some undesirable outcome, condition, or risk factor for the population, then people are afraid of being pigeonholed, ejected, or otherwise unfairly labeled.

The problem is not in the measurements themselves, assuming they meet the criteria of being pertinent, but the way in which they are acted upon, which is usually management’s fault. For instance, a company might find that certain factors correlate highly with employee theft across a sample size of the company’s workforce. Used properly, that would help the company develop effective programs, policies, etc. that would lower the amount of employee theft. Used improperly, managers would target or otherwise treat employees differently who, while they might exhibit some or even all of these factors, never stole from the company. The point the managers missed was that the measurements and correlations were useful tools to see if the programs and policies were effective and that’s where it ends, period. They are not about predicting any one individual’s behavior, nor set them apart as “high risk”, etc. That’s the marvelous thing about people. They’re all different and managers have to remember that fact when they study population data. There is no shortcut in managing people.

Posted in analytics, management | 3 Comments »

Interesting Talent Management Use of Internal Prediction Markets

Posted by Mark Bennett on January 22, 2008

Thanks to Jake for pointing out this recent post about a different and interesting talent management approach to prediction markets, showing how they can be used to gain a better understanding of the flow of intangibles in your organization. That is key for both getting the most out of your talent as well as helping your talent feel engaged.

Bo Cowgill at Google and two economists, Justin Wolfers (Wharton) and Eric W. Zitzewitz (Dartmouth), have been for the last two and a half years studying Google’s internal prediction markets. As the NY Times article states regarding the markets:

“At Google, they are used, of course, for business. In the last two and a half years, 1,463 employees have made wagers with play money (Goobles, as in rubles) on questions like: will Google open a Russia office? will Apple release an Intel-based Mac? how many users will Gmail have at the end of the quarter?”

People typically look at prediction markets for the most part as a “Wisdom of Crowds” tool (where they are also known as “decision markets”). Internal decision markets can provide value to the company by, for example, getting a jump on the competition by spotting something or some trend sooner to generate new business ideas, saving costs by shutting down projects that sales forecasts predict will do poorly, etc. The prediction market acts as an aggregation mechanism for the collective wisdom of the people participating in the market, and it is most effective in its direct application to making informed decisions when the group members are diverse, independent, and decentralized.

Even when those properties aren’t fully present, like many other things where people are involved (crowds, surveys, etc.), the value in the prediction market isn’t just in the answers to the direct questions you ask of itThere is value from what those answers are telling you about other things regarding the people you are studying. That’s exactly what the paper, “Using Prediction Markets to Track Information Flows: Evidence From Google,” drills into, and it applies very much to ways in which companies can derive more value from their talent. Here’s what the paper’s authors summarized:

“…we illustrate how markets can be used to study how an organization processes informationWe find strong correlations in trading for those who sit within a few feet of one another; social networks and work relationships also play a secondary explanatory role. The results are interesting in light ofrecent work on the importance of geographical and social proximity in explaining information flows in firms and markets.”

These kinds of findings are very useful from a Talent Management perspective. Using internal prediction markets as a way to understand how information flows within your organization is an area to pay attention to, especially in conjunction with tools like social network analysis, which also help you understand the flow of knowledge in your organization. Knowing how intangibles flow within your organization is a key competitive advantage, both in helping to find ways to improve that flow as well as helping to increase employee engagement.

Posted in analytics, wisdom of crowds | Leave a Comment »

How Does Your Talent Help You Win?

Posted by Mark Bennett on January 2, 2008

Before you start deciding what investments to make in your talent, it helps to understand as best you can how your talent can actually help you win. Without at least some understanding of this, you have reduced your ability to know where or how much to invest, which leads at the very least to opportunity costs that put you at a competitive disadvantage. However, relying strictly on conventional wisdom and/or intuition, while perhaps giving the illusion of understanding, doesn’t substitute for what “Hard Facts, Dangerous Half-Truths And Total Nonsense: Profiting From Evidence-Based Management” authors Pfeffer and Bob Sutton describe as “evidence-based management.” They point out fallacies in much of the conventional wisdom around how talent affects organizational performance. In addition, Michael Lewis’ “Moneyball: The Art of Winning an Unfair Game“, showed how your opponents’ mistaken conventional wisdom about what helps you win could be used to your advantage if you have a better model.

“Moneyball” showed how current decision makers in baseball still relied on measurements that were developed under different circumstances and were biased by the subjectivity of the statistics gatherers at the time. These measurements resulted in a distorted view of how player talent could contribute to winning games. For instance, the venerable “Batting Average” measure was developed under the assumption that if a batter was walked, it was solely the pitcher’s fault, and therefore the batter was not credited for getting on base, even when the result was just as good as if they had a base hit. It turns out that a batter’s ability to hit influences whether they are more likely to walk. In the end, there is a chain of effect between getting on base (particularly so in not getting out), which leads to scoring runs, which leads to winning games. An “on base” measure was shown to be statistically more able to predict winning games. Finding and developing that talent would therefore contribute to winning more games.

Another example was the conventional wisdom that stolen bases and sacrifice plays were key to winning games, but the statistics showed otherwise. Here, the statistics also point to how the conventional wisdom might have gone off track. Both stolen bases and sacrifice plays are ways to execute strategy that seemed to make sense intuitively (advance runners into a scoring position), but the cost in number of outs turned out to be a losing proposition in the long run. In comparing a “winning games” equation based on conventional wisdom vs. one derived through regression analysis, it’s as if the “weights” for stolen bases and sacrifice plays were overvalued and the costs of outs undervalued in predicting wins. The conclusion: perhaps talent at stealing bases is overvalued and therefore not something to seek or invest in as much.

How does this lesson translate to other organizations? There are many instances where, just as in baseball, companies substitute conventional wisdom or intuition for hard facts when it comes to how talent helps them win. In many cases, this is not only an opportunity cost for the company in missing out on a better way, but it can have a damaging effect in the long term. In their book, Pfeffer and Sutton question some of the reasoning behind the conventional wisdom regarding talent. In many cases, these “half-truths” can obscure how your talent is really affecting your strategic success. In the end, it’s best summed up in that because successful execution of strategy is a complex thing to fully understand, with a complex interaction between individuals and systems, there is a natural tendency to look for shortcuts; to do what the latest fad, group consensus, or conventional wisdom says to do. That’s not to say it’s necessarily wrong, but without it being tested, you don’t know if it’s right for your company, either.

So instead of just following the herd, work at getting a true understanding of how your strategy’s execution is affected by your talent and organization capabilities. While this work is challenging due to the complexities involved, there are ways to approach it intelligently. To sum up how “Beyond HR: The New Science of Human Capital” authors Boudreau and Ramstad put it, if you have a real strategy, it’s about how you are different and therefore, your success in executing that strategy hinges on whether your talent has the required unique capability. This insight allows you to focus on what really matters most instead of having to spread your attention and analysis across all the capabilities you are willing to cede as being basically the same as your competition. With that focus, you can then apply HR and financial analytics to mine performance and profile data for patterns. Then, you can develop and confirm your hypotheses as to how those pivotal roles in the organization and the pivotal competencies within those roles result in strategic success. While it covers mostly analytics in areas such as finance, manufacturing, customer relationship, etc., “Competing on Analytics: The New Science of Winning” by Davenport and Harris, describes how beyond professional sports, businesses are now making use of HR analytics to gain a competitive advantage, especially in talent management.

The continual testing and development of the analytical model of how your talent helps you win is key. A big mistake, much like the blind use of conventional wisdom, is the misapplying of analytics by blindly following a model that was built once to every situation going forward. In fact, a huge opportunity is being overlooked by not always reevaluating the results, restrictions, accuracy of prediction, etc. of the model. Doing so is a way to further increase your understanding of how your talent helps you win by causing you to reconsider variables and how they interact. As author Ian Ayres describes it in “Super Crunchers: Why Thinking-by-Numbers Is the New Way to Be Smart“, it’s the interplay of intuition in the form of testable hypotheses, and the incredible analytical power we now have at hand with huge, interconnected datasets to test them, that result in competitive advantage. Intuition by itself is too susceptible to human errors of bias and perception, while mindless number crunching is scattershot and too likely to miss what really matters.

Posted in analytics, management | 3 Comments »