Measuring Invisible Talent

When we think about measurement, we usually think about measuring objects—their length or weight. The concrete nature of this sort of measurement is easy to understand and predictable: A meter is always a meter.

Talent measurement, in contrast, is not easy to understand, and it’s unpredictable. Measuring talent has its challenges, but it’s one of the keys to organizational learning and employee motivation.

When it comes to measuring talent, we look for consistent and concrete measures, just as we do in the physical world. We hope for honesty in the mathematical precision offered by measures and metrics.  We hope for less wiggle room and more candor. We hope measurement data provides less theory, better insights, and obvious decisions. 

Comparing physical and talent helps us to see the value and potential of measurement. The value is high, but if we cling to the metaphor of physical measurement, we will grow frustrated. We may also miss one of the real strengths of talent measurement.

Talent measurement is different and it is complicated. Among the complications, it has a special attribute: measurement motivates. To access the benefits of talent measurement we need to consider how physical and talent measures differ. 

Talent Is Invisible

Talent measures aren’t concrete, like their physical counterparts. Many of the most important assets of today’s world are essentially invisible—think of wealth, power, relationships, personality, or intelligence.  Because the aspects of talent that we care about are invisible, it can be difficult to know what we are measuring.    

It’s not just that talent is invisible. We also need to remember that measures are just a representation of talent. This adds complications. We all know that someone’s height in inches isn’t the person. It’s easy, however, to confuse a measure of potential with the value of a high-potential employee. The measure of potential is a representation of an underlying capability, and the measure is accurate only in a probabilistic sense.   

Invisibility and representation are two reasons that talent measures tend to be less precise, and less consistent, than physical measures. Have two managers rate how well an employee completed a difficult task, and you’re likely to get two different answers. Ask two skilled carpenters to measure the length of a cabinet, and you’ll probably get two nearly identical answers, accurate to within one-sixteenth of an inch. 

Despite this, organizations often act as though their measures are nearly perfect. For example, some management consultants recommend that employees be ranked annually. Let’s be clear about what ranking actually means: Employees will be listed in order, from best to worst. To truly rank employees, there would need to be a distinction between the 10^th and 11^th best employees. Without a perfect measure, this is impossible. Since we don’t have measures that are up to this task, we would have to use other means to rank employees, such as intuition. 

Ranking employees by a physical measure sounds easier, but even this is complicated. Let’s say you’re at a family gathering and you’re taking a photo of your grandparents, siblings, nieces, nephews, and so forth. You’d ask people to organize themselves by height—shorter people in front—so that the camera can capture their faces. We often think about employees this way: We can just line them up according to some feature, such as performance. We’ll keep the best, or the tallest, and get rid of the rest.

Of course, it’s not that simple! Even physical measurement is imperfect. Imagine trying to get 1,000 employees to stand in order, by height. I can hear the questions already. Does big hair count? Should we take our shoes off? In the end, we’d probably need to ask ourselves, "what is employee height, anyway?"

If we take physical measurement to this logical conclusion, it provides a useful lesson: measurement is more complicated in practice than in theory. We may think we understand what leadership is. When it comes to measuring it, we need to get pretty specific in our meaning.  

People React to Being Measured

In general, physical measurement has few side effects. If you measure the length of a cabinet, you don’t affect the cabinet, and the cabinet isn’t likely to react. Talent, unlike inanimate objects, is affected in complicated ways by measurement. People react to measures.

In fairness, some physical objects are affected by measurement. When checking tire pressure, a small amount of air escapes. This affects the tire pressure. This is a simple example of an observer effect, which has been well documented in physics. For example, a glass thermometer absorbs thermal energy when taking a measurement. 

Observer effects on physical objects are generally unsurprising and small. Talent’s reaction to measurement is complicated and can be large. 

The effect can be positive. Measurement can lead to motivation, increased effort, and more focus.  Feedback and reasonable goals often lead to higher levels of performance, as we discussed in past blog posts.  

Practical experience however, shows that this isn’t always the case. Unfortunately, measurement can change talent in counterproductive ways, depending on the context. Measurement can de-motivate and distract. If measures are linked to very difficult goals, employees sometimes give up, or—even worse—lose faith in the organization and disengage. 

To make things worse, reactions to measurement can also motivate talent to corrupt or game the measures. Physical measurement never has this issue. Humans, however, have a major preoccupation with gaming measures. It’s so common that the famous methodologist Don Campbell, discussing program evaluation in 1975, described what has become known as Campbell's Law:

The more any quantitative social indicator is used for social decision making, the more subject it will be to corruption pressures and the more apt it will be to distort and corrupt the social processes it is intended to monitor.   

Given these complexities, perhaps talent management should steal the nickname “dismal science” from economics:

• The underlying dimension you wish to understand is invisible 
• Measuring talent is imprecise and probabilistic 
•  Humans react to measures, sometimes by changing themselves, and sometimes by changing the measure.

 

So, Do We Stop Measuring Talent?

It’s been said that since we can’t measure talent perfectly, we should simply give up and acknowledge defeat. For 30 years, some consultants have advocated getting rid of performance appraisals altogether. I’m amazed this impractical idea is still being considered.

If we don’t measure human performance, we lose a powerful motivational and learning tool. As with many aspects in life, we simply have to manage the dilemma and tension. 

Measurement is flawed and we must use it.  We can’t hide our head in the sand and hope these flaws will go away. The flaws are inherent in measuring, especially something as complicated as talent. If we step into the real world of human idiosyncrasies, measurement is a powerful tool that can help us improve organizational performance, ensure educational excellence, and motivate personal growth. 

In the coming blog posts I will further elaborate the myths of talent measurement and how we can think more clearly for organizational learning, motivation and growth.

Charley Morrow

Motivating with Measures: Accountability, Incentives and the Dark Side

The benefits and risks of using measures for motivation are amplified when employees are made accountable or incentivized.

Measurement and Accountability

Measurement is at the heart of accountability. In the dictionary, accountability has a neutral meaning: an obligation or willingness to accept responsibility for one’s actions. This is the denotative, or literal, meaning. In a work setting, the denotative meaning of accountability is a goal that defines who will do what by when.

Accountability in this sense is the basis of management by objectives  (MBO).  While MBOs were popularized in the 1950s, they remain a central element of most organizations’ annual performance appraisals. 

While some objectives are task-based, the best objectives are measurement-based. We have found that the most effective examples of accountability-based motivation use SMART goals—goals that are Specific, Measurable, Agreed upon, Realistic, and Time-bound. Describing expectations in terms of measures at the beginning of a project motivates performance. 

Accountability is a word often loaded with connotations. How would you feel if you were told in a business meeting that you will be held accountable? Queasy? The phrase suggests that you’re in trouble. This isn’t actually accountability—it’s scapegoating. This is a connotative meaning, and the connotations of accountability are negative. The fear of negative consequences can lead to all sorts of dysfunctional behavior.

If the meaning of the measures isn’t managed, then accountability is more likely to instill a culture of fear than it is to motivate employees and support the organization’s strategic goals. 

To motivate with measurement-based accountability, the meaning of the measure must be managed. Use measures to describe expectations before the employee works to achieve results.  Articulate both the formal, denotative meaning (how the measure works) and the connotative meaning (the implications for the employee).

• If you’re building a measurement system, remember that the connotations are probably more important than the measures. Consider how the measures will be seen by employees. Develop a list of actions employees could take to influence the data.  Be sure to include actions that the system intends to encourage as well as unintended actions. Adapt your system accordingly to especially encourage the intended and discourage the unintended.
• If you’re managing employee accountability with measures, be sure to talk with employees about both the denotative and connotative meanings. It’s important to develop a shared vision: This is a true leadership communication task.  If there is clear agreement on the meaning of the measure as well as the level of performance expected, accountability can be positive. 

Accountability is simply responsibility. Measurement can help top build responsibility for results and the rewards or consequences of the results.

Incentives and Measurement

If goal setting and accountability work, why not add incentives to make them work even better? Why not juice the motivation system? Many of us have worked, or currently work, in an incentive system. Sales people work on commission. Managers get bonuses and stock options.

There is a whole industry of compensation consultants trying to create incentives that work. Since the industrial revolution, we’ve been trying to get incentives right—and some of us are starting to wonder if incentives are just wrong.  

Research summarized by Daniel Pink suggests that incentives lead to lower performance in completing tasks that are complex or involve creative thinking. I’m sure we’ll find that this relationship is true in many circumstances. 

I am also sure there are many circumstances in which incentives lead to improved performance, even in complex and creative tasks. As with many aspects of human performance, there are complexities.

Life isn’t one-size-fits-all; there are individual differences and nuances of context that influence how incentives affect performance. Long-term goals, which are difficult to study experimentally, may work better with incentives.  Mr Pink presents the world in black and white; i am confident there are many shades of gray.

There is a bigger problem with linking incentives to measures, however.  Incentives, or consequences, have the tendency to put the focus exclusively on moving the needle—on affecting the data and the measure rather than addressing the underlying goal. Too much focus on the connotations of the measure, as opposed to the meaning of the measure, leads to gaming.

The Dilbert comic strip may seem ridiculous, but as is always the case in Scott Adams’ cartoons, absurdity reflects reality to an uncomfortable degree (many of his cartoons are based on real-life examples submitted by readers). Incentives can have unintended consequences, often encouraging employees to behave unethically. For example, if you were earning a subsistence wage as a packer for Green Giant, and the company announced that a bonus would be paid to every employee who could find and remove insect parts from packages of frozen peas, what would you do? Possibly what many of the employees did—bring insect parts from home to earn the incentive.

There are, of course, more troubling examples of the dark side of measurement-based motivation. In the sad story of system-wide cheating in Atlanta Public Schools, 178 employees, including both teachers and principals, are now suspected of inflating scores on standardized tests to earn the significant rewards that come with rapid improvements in school performance. Outright swindles, such as Bernie Madoff, are all too common.

In sales departments there are more subtle examples of gaming incentive systems. Sales departments have been known to count all sales in the current quarter toward commissions—even though many of the sales are not actually closed. 

Conclusion

It’s dangerous to rely too much on measures for motivation: The more you emphasize measures, the more apt the measures are to cause dysfunctional, even unethical, behavior.  If you need to use measures for accountability and incentives, be careful.  Measures can’t replace management; they are a management tool.  It is necessary to make sure that the measures are reasonable – not gamed – and that accountability is understood and positive. 

Comparison, Context, and Connotation: Turning Data into Insight

How can we turn inert data into dynamic insights? We turn measurement into data, data into information, information into meaning, and meaning into insight. Each of these four steps is critical. If you are building talent measurement systems or using talent measurement as part of your management responsibilities you should know the three key Cs.

Turning Data into Information: The Key of Comparison

Many organizations have generous amounts of data filling their databases, but few organizations make use of it. Data by itself is meaningless. And at this point, it’s clear that our processing skills haven’t kept pace with our ability to collect terabytes of it. 

Imagine looking at a column of numbers. What does it tell you? Nothing, I imagine.

Now imagine that you can look at the same column of numbers with other data points for comparison. You might compare each piece of data to:

• Time (each data point is part of series across time)
• Employees (each data point is one employee’s performance)
• A norm (one number represents typical performance, and another number represents actual performance)
• A goal (one number is the target, and the other number is the actual amount)
• An implication (one number is an employee’s performance, the other number is an incentive payment associated with it).

Data is useless unless it is compared to something. Trends matter. 

The need for comparison isn’t always obvious, however. Occasionally, I’m asked to analyze an employee survey and there’s no good way to compare the data. Often, data is collected without considering how it will be turned into information. you have to compare across time, subgroups or at least across different questions. To turn data into information, you need a comparator.

To understand a survey, comparator can be previous survey results, a goal, normative responses, or the results of measurement in similar organizations (the basis of benchmarking).   In a pinch, you can compare survey questions to each other, but this provides limited information.

Turning Information into Meaning: The Key of Context

Information, however, is also never useful in itself. To have meaning, it needs to  be placed in context and interpreted. It’s critical to ask why.

• Why does the pattern of numbers vary with time?
• Why do employees’ levels of performance vary? Are there differences in aptitude, skill, or motivation? Or are they working in different environments?
• Why is our data above or below the norm?
• Why are (or why aren’t) we achieving the goal?
• Why am I receiving a smaller bonus than other employees?

In many situations, meaning is elusive because it requires a broad understanding of context. If two employees have very different performance results, are they working in the same context? Does one employee have more difficult tasks—a more involved project, a larger territory, more complex machinery to run? Only when you’ve determined that the context is comparable can you infer that different levels of skill or motivation underlie the difference in results.

Management is drowning in information. As we collect more and more data, comparison becomes easy. But, putting information into context requires bridging different data sources, integration, and creative thinking. The strategic and operational environment matters. Important information is relevant, given the context. 

Turning Meaning into Insight: The Key of Connotation

Given the process of translating data into meaning, it’s easy to see why there is miscommunication. Two people looking at the same data can have completely different interpretations—and they may not even know it. 

This may seem paradoxical at first; after all, measures are precise way of communicating. We wouldn’t say a car “doesn’t need much gas.” Instead, we use mathematical precision to talk about miles per gallon. Imagine the reactions of shareholders and analysts if an executive talked about “a pretty good investment,” rather than discussing a percent return on equity.

Nevertheless, people with different perspectives or goals see measurement results very differently. As with other types of communication, measures have denotative and connotative  meaning. Making a distinction between the two types of meaning is the key to understanding measures.

To illustrate the difference, let’s look at the term re-engineering. In literal (denotative) terms, re-engineering is a way to understand business processes and optimize them. In subjective (connotative) terms, there are implications of re-engineering—mass layoffs. I once used re-engineering as an example in a speech that I was giving in a company and received a strange and hostile response.  It turns out that the there was a history of using the term euphemistically!  That speech never recovered—the mere use of the term destroyed any trust between the audience and me.  

In measurement, the denotative meaning is often defined mathematically. It is the connotative meanings, however, that often matter to employees. In other words, the implications of the measures are more important than the measures themselves.  

It’s important to remember that the implications of measures are personal. How employees interpret measures, and how they react to performance appraisals, are influenced by their upbringing, their personalities, their motivations, and their worldviews. 

For example, if a salesperson is focused primarily on money as an indicator of success, he may consider performance measurement only in terms of the size of his bonus. It’s likely this will lead to misunderstandings with a company executive, who is looking at the measures to answer different questions: Does the salesperson need more training? Is the product “good.” Did the customers have good experiences?

In another common example, it may not be possible for an employee to receive measurement-based feedback constructively for any number of reasons. If the employee is perfectionistic or competitive, she may only be able to receive the feedback as criticism. Another employee may be so consumed with feeling miserable about failing to meet last year’s goal that he can’t engage in an authentic conversation about the future.

In my experience, many employees will try to avoid measurement because they distrust management, or are afraid of being targeted in a blame-oriented culture. This example of connotation is probably all too familiar to the readers of this blog.

Of course, personal perceptions and assumptions can be influenced. To build meaning, and ultimately insights, organizations must spend time decoding both the denotative and connotative meaning of measures. Unless both the denotations and connotations are addressed, there is little chance of communicating with the measures successfully, or gaining insight from the data.

Decoding measures is a dialogue. As a consultant, I need to discuss both the objective and subjective implications of measures with my clients. Managers need to do the same with their employees. It is only through dialogue that the measures’ contrasts and contexts, the meanings and implications, can be understood.

This is the path from data to shared insights. Of course, the path is littered with suggestions from many sources. But the salient point remains: To reach shared insights from measurement, organizations need to confirm that there is shared meaning. We can think about this as a four-step process:

There is no data without measurement, no information without data in comparison, no meaning without an understanding of information in context, and no insight without communicating shared meanings.

Is your organization taking steps to make sure that insights are gained from measurement?

In the next posts I’ll talk about motivation, organizational learning, and accountability. In the meantime, I welcome your thoughts.

Charley Morrow

Employee Performance Measurement is Increasing But Will Not Increase Efficiency

Employee performance has been measured since the start of the industrial revolution in the late 18^th century. By 1910, scientific management—the attempt to improve efficiency by applying engineering principles and measurement, in manufacturing —was reaching its peak. 

Although scientific management as a school of thought had faded by the 1930s, it continues to influence the way we measure and manage, and it provides fascinating historical insights into industry. Scientific management was largely focused on per-worker output, how to increase output by finding the right employees, incentive schemes and best practices to ensure that systems function optimally. 

Does this sound familiar? It should. Management, and our society in general, continually focus on these topics. Our use of measurement has only expanded. Today, 97% of organizations have an employee appraisal process, and many organizations are working to increase the number of appraisals to several times each year.  

With current social and technological trends, I expect human performance measurement will continue to increase. I also expect that people will continue to be surprised at the real outcomes and consequences of measurement. I’ll make another prediction: We’ll see little value from all this measurement, unless we start cultivating wisdom in our use of measures.

Frederick Taylor’s Underestimated Influence

The influence of Frederick Taylor, generally considered the founder of scientific management, has been compared to Darwin and Freud.  As Taylor wrote in his 1911 best seller Scientific Management:

… the end of our coal and iron is in sight. But our larger wastes of human effort, which go on every day through such of our acts as are blundering, ill-directed, or inefficient … are less visible, less tangible, and are but vaguely appreciated. … And for this reason, even though our daily loss from this source is greater than from our waste of material things, the one has stirred us deeply, while the other has moved us but little.

Taylor is the father of modern management.  At the turn of the century, management primarily were concerned with budgeting and hiring. Teams of workers defined the tasks. Workers in a sense were still in somewhat of a guild system and had some level of autonomy. Ideas presented by Taylor, and then Henry Ford’s production line, changed things considerably.

   Frederick W. Taylor

While famous for time and motion studies, Taylor had two bigger ideas associated with measuring employee performance:

• System improvement (also known as organizational learning)
• Employee accountability (to achieve targeted levels of performance).

These ideas continue to drive improvement efforts to this day. Taylor believed that per-individual output should be measured, and that the measure should be studied to understand both the system of work and how it can be improved. He also advocated for developing challenging targets for employee output.  

Taylor’s most famous example was of moving pig-iron, which was made completely by men’s labor.  To illustrate scientific management, he described an intervention at Bethlehem steel. He found that the average man moved about 12 ½ tons of pig iron each day, but the best handlers loaded 47 ½ tons. (I find both values astounding.)  By selecting the right men for the job, specifying how the loading task should be completed, and setting appropriate goals, he was able to improve per-employee pig-iron loading output. The loading tasks were streamlined and proper rests were enforced to ensure that a high level of output could be maintained for the entire day. Pig-iron handlers were accountable to achieve the goal of 47 ½ tons per day. Failure to consistently achieve the goal would result in reassignment.

Similarly, through careful study Taylor found the optimal shovel load was 21 pounds. This finding suggested many system-wide changes—in tools, team organization, and supervision. The organization provided different shovel sizes and shapes for different material density and characteristics. Workers that had been managed as teams were now managed as individuals, each accountable for performance targets.  Management structures changed to oversee these major changes.  

While Taylor’s time-and-motion studies are no longer relevant, given robotics and increasing service work, the measurement themes continue, and the themes of system improvement (organizational learning) and employee accountability continue to this day. In fact, these are two ideas with growing currency.

Organizational learning, which goes by many names, is simply the idea that organizations are entities that can learn and adapt. By looking deeply at the organization’s environment, processes, structures, routines, and culture, we can understand and improve them. There are many methods for encouraging organizational learning.  Whole conferences and societies have been developed around organizational learning, feedback, and measurement. In many situations, measurement of employee performance is the basis of organizational learning. 

Given the increased need to adapt organizations in our changing political, economic, social, and technical environment, I expect that measurement will increase. I worry, however, that organizations do not think clearly about the performance measures they track.

As organizations grow more complicated and employees more specialized, accountability for results has replaced the idea of managing tasks. Taylor was concerned with a moving pig-iron and micro-tasks such as lifting and carrying between stations. Now an employee is likely to have more complicated assignments comprised of dozens of tasks (for example, creating a new software feature or maintaining a database). Now we measure value-added and organizational results.  

Today, the essence of progressive management thinking could be stated as, “I do not want to micro-manage, so I am going to assign accountability for results and let my employees use their creativity and capability to get the job done.  In order to hold them accountable, I am going to measure their performance or productivity.” Many management theorists, such as Daniel Pink, argue for a result-oriented/only work environment (ROWE).  (Note that Taylor was indeed a micro-manager, so the parallel ends quickly.)  

I am a fan of managing results, not tasks. If you have ever been micro-managed, you’re probably a fan of managing results as well. I’m not completely sure, however, that we have the expertise to make it work. I see many organizations struggle with measures of performance—with pushback from employees and a blind push from management. ROWE works best when there are clear measures of performance that are linked to valued organizational outcomes.  These measures do not exist in many situations-- for example in support functions.

The final driver of increased measurement of employee performance is the computer-based work environment. Due to increasingly inexpensive databases, this environment allows more measurement and data-tracking. As computers facilitate more work, keystrokes, conversations, tasks, and transactions can be—and often are—automatically recorded. These digital trails can be compiled very cheaply. Footsteps, driving routes, and bathroom breaks can all be easily recorded with easily available technologies. It is inevitable that new measures of performance will be dreamed up, developed, and implemented. 

What Have We Learned About Managing and Leading With Measures?

My concern is our ability to manage, learn from, and improve from all this measurement. If we’re not careful, the additional performance measurement will only lead to additional misunderstanding and organizational chaos, misalignment, gaming of the system, and, in general, dysfunctional behavior. I’m not sure that our wisdom has kept pace with the amount of data and performance measures.  

In fact, problems associated with employee performance measurement appeared soon after the advent of scientific management.  Measuring employee performance often leads to strange outcomes. 

This is demonstrated by what’s known as the Hawthorn Effect, which showed, as far back as the mid-1920s, that when you measure employees’ performance, they react. Sometimes they react positively, sometimes negatively. Not surprisingly, it can be difficult to predict which outcome you will find.

The Hawthorn plant manufactured telephone equipment, and was attempting to identify the best practices to increase employee output. In one study, they focused on lighting. They increased the brightness of the lights in the plant, and employee output increased. They dimmed the lights, and, surprise—the same thing happened. Employee output increased whether the lights were brightened or dimmed. It wasn’t the quality of the lighting, but the change that employees were reacting to.

Additional studies from the Hawthorne plant, referred to as the "bank wiring room studies," revealed that social forces were affecting the output. Employees were aware of the measure and how they were studied by management. As a result the employees used the measure to communicate to management that they were, in fact, doing a reasonable job. Concerns, such as layoffs based on the measures, affected employee output. In these studies, employees attempted to communicate to management that they were working in a steady and reasonable manner.  

These studies have made it clear that measurement does not cause efficiency —there is always an underlying and very human mechanism that is a reaction to measurement. The reaction may or may not be improvement. These very human processes hinge on reactions to the measures that include communication and messaging, image management, and social interaction.

In day-to-day work environments, we continue to ignore what studies published 90 years ago clearly demonstrated. Many attempt to improve performance with measures, without giving adequate thought to managing the underlying human processes that will lead to improvement. 

Consider the testing mania present in American public schools.  These schools are expected to improve, largely because they are being measured and expected to improve. There is a blind belief in accountability. 

Similarly, consider the 97% of organizations that have performance appraisal systems that attempt to provide feedback to individuals in the hope that it will automatically improve performance, learning, and motivation.

Unknown to most, the ghost of Fredrick Taylor is alive in many of our modern organizations. Blind beliefs in simplistic ideas about measurement continue. We need to use measurement to increase efficiency, but a naive belief that productivity will increase without addressing the very human element of work is defeating the purpose, and the effectiveness, of the measures.

If you have examples of simplistic thinking behind employee performance measurement, I’d love to hear your story.

Charley Morrow

Human Performance Measures: Start of a Series

I’ve been working with people measures for more than 25 years. Nearly every day, I see strong reactions to these common leadership tools.  Some embrace measurement as a tool for positive change, and others are nervous. Some question the measures, and others hide behind the authority of the data. 

These reactions to measurement and data fascinate me. They also hold the key to getting results from measurement systems. 

When measurement systems work well, people develop understanding, gain insight, become motivated, and set new directions. Just as often, however, measures simply do not work. In these cases, people ignore the measures or build elaborate defenses to dodge, manipulate, or diminish the data.

Over the next few months I’ll be writing about how systems and people respond to measures of human performance and how organizations can get beyond negative reactions. This is a topic I’ve been researching for years, and it may be my strongest and most nuanced area of understanding. 

I started my career focused on measurement systems. I took enough graduate courses in statistics and methodology to work as a psychometrician, and my dissertation combined the disciplines of psychology and economics. 

As I matured and worked in the real world of organizations, I started to see that the value of measurement can be found less in precision and mathematical finesse than in communication and learning. The most elegant performance management system is useless unless it is genuinely called on to help people communicate, learn, and adapt. 

In other words, measures need to be applied to produce data; data needs to be reviewed and interpreted to be useful; and useful information needs to be considered in context if people are to learn and improve. 

I can say with confidence that measures and data alone will not change organizations or behavior. There are too many psychological, organizational, and social factors that can prevent measures from translating into learning and improvement.

As a society, we spend huge sums of money on human performance measurement—and we start measurement early. All of us are familiar with the U.S. public education system, which now tests every student in the third through eight grade annually. In a number of states, databases are being developed to link these test scores to school, teachers, and student demographic information. 

When we graduate from the public education system, we find that most large organizations rely on annual employee appraisal systems. A manager can spend a few months each year rating employees, summarizing the information, and providing feedback. 

Despite the intensity of the data-gathering, improvement is not obvious. Many are dissatisfied with the measurement systems.  As a result, these measurement systems are often re-imagined and implemented with great hope and promise, only to fail. I don’t think much of this activity and investment. Don’t misunderstand: I’m a fan of measurement, because it’s critical to precise feedback and growth. But I’m an advocate for thoughtful investment in measurement. I’ve seen its transformative power. 

The public education system is still experimenting with measurement systems, and will be for years to come. Some corporations rethink their annual appraisal systems regularly. 

Technological and social trends suggest that performance measurement will only increase. Some argue that this investment is inappropriate. Addressing the merits of this societal investment isn’t my purpose here. My purpose is to make sure that individuals, organizations, and society get more value from the investments that are made.

I have workable tools and tips to make sure all of this data yields some return. Paradoxically, I won’t spend much time writing about measures. As I’ve said, it’s not as much about the measures as how they are used. I hope you will find the posts in the following weeks useful.