Sir Andrew Likierman

An unusually practical article appears in the October issue of Harvard Business Review on the topic of performance measurement. I regret that I can’t share a link with you, because HBR content is not available online, except to subscribers of the magazine (perhaps the folks at Harvard haven’t yet read about the idea of Free). No matter. Though I can’t share the article itself with you, at least I can summarize it here.

Entitled The Five Traps of Performance Measurement, Andrew Likierman’s article is concise and valuable. Sir Andrew Likierman is no less than the Dean of the London Business School, a non-executive director of Barclay’s Bank, and Chairman of the UK’s National Audit Office. He knows of what he writes.

As strategists and change agents, we’re all part of the process of performance measurement and management in our organizations. The task of measurement is neither easy, nor especially satisfying, at least in the short run. And those charged with measurement easily fall prey to pitfalls and traps. Sir Andrew highlights five that are most common.

1. Measuring Against Yourself. While it is important to understand change in performance measures from one period to the next, it is vital to understand how the organization is doing relative to its competitors. While competitive benchmarks may be difficult and potentially costly to obtain, there are ways to understand how one is doing relative to the competition. Likierman offers the example of Enterprise Rent-A-Car’s Quality Index, which captures customers’ plans for future rentals. From a random telephone survey of recent customers, Enterprise is able to project future increases or decreases in market share.  
2. Looking Backward. Comparisons with last year’s numbers aren’t useful if you’re not also looking at leading indicators that help make better decisions now to predict future performance. Likierman moves from this assertion to a discussion of the quality of management decisions as a leading indicator of success. He cites one European investment bank that tracks the eventual outcome of deals the bank chose not to do; if they turn out to be bad deals, the no-go decision is rated as a success. It is as important to focus on what the organization chooses not to do as it is to track what is done.
3. Putting Your Faith in Numbers. Likierman asserts that numbers-driven managers tend to produce high volumes of low-quality data. There is a tendency to use popular measures (ones that may be fashionable in an industry), rather than choosing the right ones for the firm’s unique strategy. An example given is that of the Net Promoter Score, the likelihood that a customer will recommend a product or service to others. But the NPS is useful only if customers are likely to make purchase decisions on the basis of a recommendation. Another symptom of this trap is trying to fashion links to financial performance when no tangible links exist; the apparent ROI of such service functions as HR or especially IT is far more circumstantial than real. Most of the numbers that I’ve seen used in IT ROI calculations are somewhere between rough estimates and wishful thinking – certainly not reliable enough for making decisions.
4. Gaming Your Metrics. It comes as no surprise that people whose performance is being measured will attempt to influence those numbers. This happens at an institutional level, not just with individuals. As Likierman reports, Royal Dutch Shell has paid $470 million since 2004 to settle lawsuits contending that Shell overstated its reserves. Morgan Stanley was reported to have deliberately lost a €20 million deal in order to improve its position in a global ranking. And who hasn’t seen sales behavior change at the end of the fiscal year, either trying to book business in the current year, or delay booking until the following year? All of these are examples of deliberate decisions to manipulate measurements. Likierman accepts the reality that gaming will happen, but prescribes a diversity of indicators that would be harder to manipulate; law firm Cliffor Chance changed from simply measuring billable hours to a portfolio of seven criteria on which to base bonuses; including measures of work quality and integrity.
5. Sticking to Your Numbers Too Long. Once instituted, measurement systems become part of enterprise culture, and evolve more slowly than the organization itself. By deliberately stating the purpose of a measure (and connecting to a strategic intent, as in the balanced scorecard process), leaders are more likely to question an indicator that has outlived its usefulness. To me, the sign of a healthy strategic management process is one in which measures are easily and frequently challenged, and the portfolio of measures is frequently revised.

Likierman concludes by reminding us that most business managers are not experts in performance measurement, and that line managers suffer from hopeless conflicts of interest in the measures design process. Measures design must include checks and balances, and (I believe) is best enabled by an outside, objective facilitator.

Are these pitfalls present in your organization? What are the other traps you’ve seen? Please comment below.

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The ‘balance’ in balanced scorecard refers to the ideal of providing leaders with a balanced portfolio of lagging and leading performance indicators. Leading indicators are valuable because they help managers form an expectation of what will happen, and enable testing of the cause-and-effect hypotheses that are at the core of the strategic planning process. But identifying candidate leading indicators and selecting from among them requires careful consideration and a healthy skepticism of apparently easy answers.

But this careful consideration requires familiarity with the concept of causation. Having just pulled down my trusty copy of The Practice of Social Research – a textbook from an undergrad class I took too many years ago, I was daunted by the prospect of condensing a lengthy entire chapter on the nature of causation to offer a brief understanding here. Wikipedia’s definition is far more concise:

Causation: The belief that events occur in predictable ways and that one event leads to another. If the relationship between the variables is non-spurious (there is not a third variable causing the effect), the temporal order is in line (cause before effect), and the study is longitudinal, it may be deduced that it is a causal relationship.

When working with executives, I find real-world examples to be especially useful. One of my favorites offers a measurement of housing starts (a statistic produced monthly by the U.S. Census Bureau) as a leading indicator of the sales of so-called “white goods” (a wonderful old term meaning major appliances, like refrigerators, washing machines, stoves, etc.). An uptick in housing starts is an extremely reliable leading indicator of a nearly identical uptick in sales of white goods several months later. As those new housing units near completion they are furnished with new appliances. So if you’re in the business of making refrigerators, you’re going to be very interested in tracking housing starts.

Unfortunately, causation isn’t usually that obvious. One client of mine (some details have been changed) operated an inbound call center for taking resort hotel reservations. As part of a broad strategy, this firm undertook an initiative to increase revenue by cross-selling reservations for nearby dining and attractions, along with the core hotel reservations. Call-center agents whose performance had previously been measured as calls handled per hour were now expected to engage callers in more personal conversations about their vacation plans, in order to find opportunities to cross-sell the associated reservations. The new performance measures became average call duration (the hypothetical leading indicator that was expected to increase) and average revenue per call (the lagging indicator). The strategic hypothesis: keep customers on the phone longer, and they’ll buy (reserve) more.

It didn’t work out that way. Even though the call-center agents had been trained extensively in cross-selling, the majority of customers calling in were not easily engaged in the longer, more personal conversations. These customers called expecting a brief transaction (the hotel reservation) and were impatient with the questions. But a subset of customers calling in were quite happy to chat with the friendly reservations agents. Unfortunately, these chatty customers turned out to be especially unlikely to make dining and attraction reservations, and ultimately reduced the productivity of the agents.

A continuing theme in my posts has been that of strategy as a set of hypotheses to be tested. A tested hypothesis proven to be false is just as valuable as one proven true. Measure such as those in the example above enabled managers to quickly identify flaws in the hypothesis, and revise the strategy. The value of a measure is in its ability to enable valuable decisions.

Do you have a favorite example of a pair of leading / lagging indicators that you can share? Please comment below.

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A classic example of the Flaw of Averages involves the Statistician who drowned crossing a river that was on average 3 ft. deep.

Desperately casting around for a topic to write about today, I was grateful to see a link to an interview in the San Jose Mercury News with Stanford professor Sam L. Savage about his book, The Flaw of Averages (great title!). I’ve not read the book yet, but the review has certainly piqued my interest:

How does General Motors, Sam L. Savage wonders, explain the pathetic performance of its crystal ball? When Americans started driving hybrids, GM was still pushing Hummers. Executives at the giant carmaker — fully aware of union contracts, presumably prepared for rising gasoline prices and economic uncertainty — drove straight into the ditch of bankruptcy.

“Probability management” is often mismanaged by business leaders, says Savage, a consulting professor of management science and engineering at Stanford University and a fellow at the Judge Business School at the University of Cambridge. Savage, who has performed probability studies for Royal Dutch Shell, set out to right statistical wrongs in his book “The Flaw of Averages.”

The Information Age has transformed statistics into a vital field of study, yet Savage says many habits and practices have been slow to change from the “steam era statistics” of the Industrial Age.

Written for a business audience, “The Flaw of Averages” leavens the math with levity, even the occasional cartoon.

Well alright then. Working with business executives and their measures for so many years, I continue to be amazed at how easily decisions are made on the basis of numbers with little consideration for the risks and consequences of those decisions. I’ve been meaning to write at some length about the need for the discipline of risk management in change programs, but before doing so, we all need to take a deep breath and consider the magnitude of our collective innumeracy.

The topic has been covered before. I just pulled Innumeracy: Mathematical Illiteracy and its Consequencesby John Allen Paulos from my bookshelf, and thumbing through it, I remember how much I appreciated the book, but that is wasn’t the easiest read. From the back cover description:

Why do even well-educated people understand so little about mathematics? And what are the costs of our innumeracy? John Allen Paulos, in his celebrated bestseller first published in 1988, argues that our inability to deal rationally with very large numbers and the probabilities associated with them results in misinformed governmental policies, confused personal decisions, and an increased susceptibility to pseudoscience of all kinds. Innumeracy lets us know what we’re missing, and how we can do something about it.

Sprinkling his discussion of numbers and probabilities with quirky stories and anecdotes, Paulos ranges freely over many aspects of modern life, from contested elections to sports stats, from stock scams and newspaper psychics to diet and medical claims, sex discrimination, insurance, lotteries, and drug testing. Readers of Innumeracy will be rewarded with scores of astonishing facts, a fistful of powerful ideas, and, most important, a clearer, more quantitative way of looking at their world.

SinceI found that Innumeracy was not especially accessible, I haven’t yet found occasion to use examples from it. Perhaps The Flaw of Averages will be better. It looks promising. From the interview with Savage:

Q. What are the most common ways people foolishly apply the law of averages? Is it the faith placed in “average returns” on retirement portfolio?

A. Plenty of people have been caught off base by the Flaw of Averages in investing, but here is an example that is closer to home. Imagine that both you and your wife are right on time for appointments, on average.

When you go somewhere together, however, you will be late, on average. Why? If we model being early or late for each of you by flipping a coin (heads is early, tails is late), then the only way you will not be late as a couple, is if neither of you is late. This is like flipping two heads in a row, or one chance in four. Now expand this to a big industrial project with thousands of tasks, and you can imagine the implications.

We don’t have to imagine the implications – we live with them every day. More to come (soon, I hope), on the topics of innumeracy and strategic risk management.

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To my chagrin, the term ‘scorecard’ is widely used in both the disciplines of performance management and strategy execution, and without further qualification, has an imprecise variety of meanings. To some, it may mean a large collection of indicators of operational performance. To others, it is an ambiguous shorthand for ‘balanced scorecard,’ which is a well-developed set of related ideas and practices around strategy management and execution. Ambiguity comes from the fact that to some, the term ‘balanced scorecard’ means simply a collection of measures that has been balanced according to some real or imagine scheme. On far too many occasions, I’ve been approached by a conference attendee with a request to review and comment on his so-called ‘balanced scorecard,’ only to find that the proud offering is a only collection of operational measures with no connection to strategy. This is the basis of my Scorecard Blues. So let me be blunt: if a set of measures has been selected without the prior development of a strategy map, it cannot be properly called a balanced scorecard.

Even without the qualifier of ‘balanced,’ a ‘scorecard’ is seen as a group of measures, and / or the visual representation of those measures, and / or the tool for managing measurement data. Many software tools called ‘scorecards’ have been developed to facilitate the collection, analysis, and presentation of scorecards, both for operational and strategic use. Because the term ‘scorecard’ has so many diverse meanings and uses, it simply cannot be used alone without further explanation. But there is one trait that attaches to nearly every individual’s own definition of the term ‘scorecard.’

The lowest common denominator of nearly all ‘scorecards’ is the ubiquitous red – yellow (amber in Europe) – green summary indicator scheme (hence RYG). I am not aware of any research into the origin of the RYG scheme for summarizing measure data. Of course, the scheme is universally used in traffic control, but the meanings are simple: red simply means stop, and green simply means go. I suspect that the first use of colors to summarize data was in aviation and later in aerospace, where green means that an indicator is within its normal operational range, with yellow and red varying degrees of abnormal results. This origin suggests the basis for a similarly ambiguous term, ‘dashboard.’

It is easy to see the appeal of using this scheme to summarize a large portfolio of operational measures. At a glance, one can observe the RYG summaries of dozens or even hundreds of indicators and quickly know which ones are in need of immediate attention. The ability to translate and display a measure as an RYG indicator is a basic capability in every scorecard software tool I’ve seen.

‘Scorecards’ predate ‘balanced scorecard,’ and as the BSC discipline has evolved and matured, so has the sophistication of BSC reporting. Adaptation of ‘scorecard’ tools to BSC reporting has been rampant. But the inevitable application of RYG to BSC reporting has been problematic. Most organizations that I’ve worked with have struggled with RYG indicators.

The BSC development process identifies strategic objectives as the basis for subsequent selection of measures. During the first several months of the life of a BSC, an organization may have difficulty in selecting effective measures and obtaining measure data; strategic measures are often very different than operational measures, and require that new information sources be developed. New measures do not always lend themselves as easily to target setting; measuring an objective to ‘Manage turnover of key employees’ not only requires data on turnover, but definitions of ‘key employees,’ and what ‘management’ actually means. Setting a target for the concept of turnover is far from intuitive.

Even when a target can be set for a particular measure, the target is probably is only useful to delineate between the green and yellow indicators. I’ve had to preside over many spirited (e.g., angry) and wasteful discussions of the right distinction between yellow and red for a particular measure. One of my colleagues has even advocated using only red and green indicators to his clients, but with limited success.

Because effective BSC programs require transparent and frequent communication of strategic performance among selected stakeholders (e.g. employees), members of leadership teams can become quite defensive about the colors. In the language of scorecard color, red means ‘bad’ for operational indicators, but not necessarily so for strategic objectives, where ‘red’ may be interpreted as ‘objective needing attention.’ Cultural attachments to the meaning of ‘red’ often causes contortions to avoid putting a red indicator on a strategy map.

In practice, I’ve arrived at a couple of conclusions and approached to using the RYG scheme on BSCs:

1. Leadership teams insist on having RYG indicators, but need to be guided to a shared set of definitions of their meanings.
2. Strict numerical targets don’t work very well for setting the colors.
3. Without guidance, a leadership team will become obsessed by color setting and distracted from productive strategic management efforts.
4. A healthy set of strategic objectives will have a good mixture of the colors. A strategy map with all green objectives isn’t believable. A strategy map with all red objectives is too demoralizing. Either extreme is dysfunctional.
5. Because there is subjectivity in target setting, it is better to have leadership teams come to a subjective agreement on the RYG setting for each objective on a strategy map, each time measures are updated. The discussion that leads to a consensus decision often helps the leaders uncover and understand root causes of deficient performance far better than any automatically set indicator will.
6. Over time, the RYG indicators of strategic performance become far less important than the rich discussions of strategic objectives, cause and effect, and initiatives in the strategic management process.

Is your organization using RYG indicators on its scorecards? Does everyone agree on the meanings of each color? Do RYG indicators help or hinder strategic management? Please comment below.

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A measurement concept is a mental image that describes an area of interest, such as speed, warmth, or comfort. The key to thinking about a concept is that it springs from an idea; an impression or perception that cannot be directly measured. To conceptualize an idea is to specify what we mean when we use that mental image.

Imagine that you’re an airline executive interested in improving passenger comfort. To say that I was ‘comfortable’ on a recent flight is to share my perception, but your understanding of my experience is not quantifiable. When you ask me what I mean by ‘comfort,’ I might say that I had a good seat, and that the flight attendant was helpful. In this example, ‘good seat’ is one of my concepts of comfort. But the concept of a ‘good seat’ has different meanings to different people. I describe a ‘good seat’ as an aisle seat with ample legroom and width, which are some of my dimensions of the concept (another has to do with being next to an empty seat).

In the course of speaking to many other passengers, and your understanding of possible approaches to improving passenger comfort, you conclude that the best leading indicator of the dimension of comfort is whether the passenger is in his or her preferred seat type (since some prefer window or even middle seats). So you choose ‘share of selected passengers in their preferred seat type’ as your indicator of the dimension of passenger comfort. Of course, you could have chosen other dimensions, and other indicators, but you know that after a point, the measurement process is not improved by more measures.

As part of the airline’s initiative to improve passenger comfort, you identify a target group of passengers (perhaps those at or above a loyalty program tier), and capture their preferred seat type in their online profile. You then modify seat assignment policies and processes (both automated and manual) to try to increase the number of times that those selected passengers are seated according to their preference. Only now have we arrived at a quantifiable, leading indicator of passenger comfort. The hypothesis implied by this choice is that getting passengers in their preferred seats will cause them to experience comfortable flights more often. An appropriate lagging indicator would be a survey of those selected passengers to capture their actual experience of comfort, and to then correlate that data with their seat assignments.

Of course, this example does not show how measures are usually selected. Measurement is often difficult, and every measurement choice implies a hypothesis. Leadership teams tend to choose those measures that are easily captured, not those that are reasoned choices as shown in the example. The choice of measurement may be delegated to subordinates whose understanding of the strategy may be incomplete, or have a vested interest in presenting a measure whose outcomes can easily be, ahem, ‘managed.’

A shared understanding of the vocabulary of measurement will enable leaders to more effectively consider and chose measures that will motive desired change behaviors and effective describe the performance and impact of strategic initiatives.

Please share below some of the best and worst measures in your change program.

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“Figures often beguile me, particularly when I have the arranging of them myself; in which case the remark attributed to Disraeli would often apply with justice and force: ‘There are three kinds of lies: lies, damned lies, and statistics.‘”

While there is certainly deliberate distortion of statistics every day in the political sphere, we hope that the measures that become part of a strategy execution program are effective in both motivating behavior and helping people make good decisions. There are many blog posts yet to be written about checks and balances in the design of measurement processes. But it’s a good idea to begin with a shared vocabulary of measurement. Two fundamental terms in the measurement discipline are accuracy and precision. 

A bag full of marbles may be said to contain ‘about 100 marbles,’ but it is accurate to actually count the marbles and be able to say that the bag has ‘exactly 103 marbles.’ With the marbles, we reach absolute accuracy by direct observation. But most measures can’t be directly observed, and determining their accuracy is less straightforward. When a web site says that it is 91 degrees where I live, but my outdoor thermometer says that it is 86 digress, it isn’t clear which measurement is more accurate, especially since temperature isn’t observable. It is the measurement process that determines accuracy.

Observability doesn’t guarantee accuracy. A call center manager may measure call volume per employee during a one-hour period by watching his employees and making tick marks on a piece of paper, but that direct observation is subject to error. Statistics from the telephone equipment in the call center should be more accurate (unless there is a flaw in the equipment’s software).

We’re surrounded by measurements that we can’t independently confirm, and we take the accuracy of most measurements for granted. Our inclination to trust a measure has as much to do with the source of the information (the web site vs. my wall thermometer) as the measurement process itself. It’s also important to remember in the business setting that those performing a measurement process may have an interest in the interpretation of that measurement.

Precision is a simple attribute of measurement. It is simply the fineness of distinctions made when expressing a variable. When I tell you that the bag of marbles contains ‘about 100 marbles,’ I am fairly representing an absence of precision in the measurement. But if I tell you that the bag contains ’108 marbles,’ I am expressing a degree of precision (down to the single marble) that implies accuracy, that is independent of how I actually arrived at the number. If the call center manager’s tick marks result in a calculation of 3.42 calls per employee per hour, we trust his measure simply because of that degree of precision.

It is simple human nature to confuse precision with accuracy. My outdoor thermometer is digital, and is actually telling me that it is 86.9 degrees outside. The web site says 91 degrees. If I hadn’t taken the time to think about it, I might be more inclined to believe the outdoor thermometer, just because of that decimal point, but my healthy skepticism says that I shouldn’t. All I know for sure is that it’s hot.

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Carefully Balance Autonomy and Mandates to Answer the ‘What’ Question

The ‘what’ question entails the actual content of strategy maps and measures. Unsurprisingly, there have been a variety of approaches used, with mixed results.

One such approach is to simply cascade the enterprise-level strategy map intact to subordinate units, and requiring them to select their own measures, which may be unique, subordinate to, or identical to enterprise-level measures. Sometimes there are no strategy maps at all at subordinate levels. But business and support units each have their own internal value proposition, and the top-level strategy map is rarely a good fit at subordinate levels. The motivation for this approach may not be benign; it has been employed to both eliminate the effort of creating subordinate strategy maps, and to offload the effort of collection measurement data to the subordinates. This approach is likely to create resentment at the subordinate level, and rarely results in a sustainable change program. Avoid this approach if at all possible. 

A more balanced approach is to require that subordinates follow the established vertical and horizontal structure of the parent strategy, but give them the freedom to establish their own objectives and measures. The reward from this approach is much higher engagement and buy-in for subordinate leadership teams, but the risk is fragmented or unaligned strategic messages. It is best to ensure that the same facilitator(s) who guided development of the higher-level strategy also facilitate development of the subordinates’ strategies – the facilitator can guide leadership teams to a better-aligned change agenda. It is important that subordinate and parent strategy maps resemble one another (I describe it as a ‘family resemblance’) and that there are no obviously conflicting strategic objectives. An excess of autonomy at subordinate levels will fragment and confuse the strategic intent.

The endless additional variations on approaches to managing cascaded content simply cannot be captured properly here. Please submit comments with your organization’s approach, and how well it is working.

The ‘Who’ Question Helps to Answer the ‘How’ Question, and Informs the ‘When’ Question

The ‘Who’ question is far easier to answer, as in, ‘Who facilitates the cascade?’ As the organization cascades its strategy deeper and deeper, the number of parallel efforts quickly multiplies. To save time and money, the pragmatic approach has been to enable third- and fourth- tier groups to self-facilitate their strategy development. Don’t do this. An expert, objective facilitator, (whether an outside consultant or from the enterprise’s own strategy office) is absolutely essential to the success of each step in the cascading. Self-facilitated meetings are likely to create poor quality BSCs, or none at all. Another unwise approach is to have the strategy create the strategy map and measures for the subordinate organization, and simply provide it to them as a mandate. This easily alienates the subordinate organization to the entire process, and loses any possibility of true engagement with the change agenda.

The ‘How’ question is answered by the choice of facilitator. An expert facilitator makes good decisions about the process of creating the BSC on the basis of his or her past experience, and tailor the approach to the unique characteristics of each subordinate organization. There is no one right way to make or cascade strategy in an organization; sadly, there are many ways the process can go awry. Expert facilitation is insurance against bad outcomes.

We now return to the ‘When’ question. Leaders want to sustain the momentum of enterprise-level strategy development, and aggressively pursue rapid cascading. But the enterprise probably doesn’t have the facilitation capacity for too many such efforts simultaneously, and this is where dangerous shortcuts are likely to be considered. It is far better to develop a high-quality cascade over time than one that is rushed and poorly-executed. A carefully planned and executed cascade is an essential ingredient to an effective and sustainable strategic management effort.

Agree with these ideas? Disagree? Seek clarification? Please comment below with your own cascading experience and questions. I hope we’ll have a robust dialog here.

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Look Beyond the Organization Chart to Answer the ‘Where’ Question

The easy and obvious approach to deciding where to cascade in the organization entails looking at the organization chart, and deciding which business and support units should get their own BSCs. So simple and obvious that leaders miss the fact that there are other approaches that may be more effective.

When an organization’s structure changes slowly over time, it is usually due to the political pressure to preserve leadership roles for certain individuals, as well as the natural resistance to change. These are the organizations whose members see themselves as working in ‘silos.’ The effect of a static organization structure may be to sub-optimize the organization’s effectiveness. The organization’s value proposition may need to evolve to meet external opportunities and challenges, but that evolution is constrained by an assumption that the existing structure is sacred. When decisions about the strategy itself are made in the context of the assumed permanence of the organization structure, it can be said that ‘Strategy Follows Structure.’ This is an unnecessary and prospectively costly constraint on the change program.

But some organizations restructure too frequently. There, the absence of certainty creates an underlying level of anxiety for staff members and managers, and valuable energy is devoted to personal political posturing (for the next re-org) instead of value creation. These organizations may be better poised to anticipate and respond to external opportunities and challenges, but I am not convinced. In one such client enterprise, a paralysis in accomplishing change resulted from the relative impermanence of people and groups.

And some organizations use matrix structures. While not inherently dysfunctional (some matrixes actually do work!), this approach requires staff members and managers to hone their political skills to navigate the challenges of conflicting leadership messages. There is no doubt that matrix organizations are less efficient, due to the overhead cost of overlapping (and sometimes competing) management structures.  

The controlling principle for answering the ‘Where’ question should be ‘Structure Follows Strategy.’ Look at the newly-hatched strategy at the enterprise level. Will success require different parts of the organization to work closely together across boundaries? Will existing political rivalries inhibit execution? If you could start from scratch, would you draw the organization chart the same way it is now? If you are answering yes to any of these questions, it may be best to use the cascading structure to blur or erase the existing boundaries, and create ad hoc overlay structures that align with strategic themes and objectives. Such ‘theme teams’ have proven to be very effective in tearing down walls, creating better cooperation and partnerships across the enterprise. While it may not be possible to erase and redraw the incumbent organization structure, these cross-functional teams may become the primary leadership structure for accomplishing change, and can render the old silos irrelevant.

Sound scary? You bet. But the default approach of aligning strategy to the existing structure will serve to reinforce the existing structure, no matter how sub-optimal it may be. Cascading strategy can be a powerful tool for effecting (or inhibiting) positive change in an organization’s structure.

Another aspect of the ‘where’ question is about depth. Of course, the size and diversity of the organization should be a key determinate, but as one goes deeper, the size of each group with its own BSC shrinks. It is impractical to have a BSC for a very small number of people, when the overhead of sustaining that BSC represents a significant fraction of the labor pool in the group. It is better to invest energy in integrating the strategy management system with individual employee performance management processes, in partnership with front-line managers and the HR organization. But that is a topic for another post. The best guidance on depth is to go only as deep through layers of the organization as absolutely necessary to ensure alignment and staff engagement. As the number of BSCs multiplies, so does the overhead cost of the strategic management system.

A final note about organizational geography: Some organization have such highly-entrenched functional and business units and hands-off management style that strategy at the enterprise level is too generic to offer meaningful guidance to subordinate units. An alternative approach to top-down cascading that has worked in these cases was dubbed “middle-up-down” by one of my colleagues. This approach entails enabling the subordinate units to create provisional strategy maps and measures and feed them up to enterprise-level leadership for a process of review and reconciliation, followed by a downward cascade that is informed by the reconciliation. Please comment below if you’d like to learn more about this approach in a future posting.

Next: The Final Three Questions

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Many cascades follow a basic pattern: a BSC (consisting of a strategy map and associated measures) is developed for the organization as a whole, then subordinate maps and / or measures are developed for each subordinate business unit and support organization (such as Information Technology and Human Resources). But the predictable pattern quickly disappears. Subordinate organizations may be required or allowed to choose to replicate or adapt the higher level strategy map and measures, or given the freedom to create an entirely new BSC. Unfortunately, there is often too little time and energy invested in understanding and making good decisions about cascading, and there are no well-established rules. The objective of this post is to outline the key questions that should be considered when planning to cascade BSCs in any organization.

Ask the ‘When’ Question First

Before considering any of the other questions, leaders should consider the timing of cascading. In their enthusiasm to leverage a newly-hatched strategy map, they may mandate that business unit cascades be completed quickly; unsurprisingly, rapid cascading is the default recommendation of outside consultants (mea culpa). But rapid cascading has its drawbacks. The enterprise-level strategy map may lose relevance in the shadow of those created for business units. That outcome is probably okay if the enterprise is operating as a holding company, but more often than not, the enterprise seeks to leverage strategic objectives across business and operating units. When this is the case, there is a large benefit in broadly socializing the enterprise-level change agenda across the entire organization before enabling the subordinate units to participate in the cascade. Not only does this enhance the credibility of the change program, but it avoids the risk of forcing a subordinate organization to cascade before its leaders are ready.

But the best reason to manage the timing of the cascade is to enable leaders to understand and carefully consider their approach to cascading, and make informed decisions. But it is easy for leaders to assume they instinctively know the best way to cascade, and to want to sustain the momentum of their initial work.

Of course, the vary real risk in not rapidly cascading is to lose executive level buy-in for the change program. Leaders should be encouraged to publicly commit to a cascading process, but avoid releasing details of the cascading approach. Public commitment reduces (but does not eliminate) the risk of losing support, and buys the critical time and flexibility necessary for leaders to make those informed decisions.

Reach Agreement on the ‘Why’ Question

Leaders who have agreed to undertake a change program using the BSC will easily agree that cascading is a good idea. But challenging them to describe their expectations of cascading will probably yield different answers and valuable insights into each leader’s management style. Why cascade? The easy answer comes from Kaplan and Norton’s Strategy-Focused Organization: to align the organization to the strategy. But what does alignment mean? To some, it means instituting a control and feedback regime that entails populating the measures on the enterprise scorecard. To others, it means enabling and empowering subordinate units to escape from bland and imprecise objectives at the enterprise level, and to “get it right” for the subordinate organization. Others may only see the cascaded BSC as an emblem of organizational status, and are most likely to volunteer their subordinate organization to ‘go first’ in leading the cascading effort. Some may confuse or conflate the optional cascading process with the necessary change communication program, as mentioned above.

While there is some merit to each of these expectations, it is vital that leaders express and agree to their expectations for the cascading process. A shared answer to the ‘Why’ question informs the remaining decisions on cascading, and enables executives’ expectations to be realized.

Next: The ‘Where’ Question

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A brief detour: when thinking about behavioral change in the workplace, you may be reminded of the Hawthorne Effect, a term referring to a famous experiment in the 1920s where factory workers productivity was found to improve when the lighting level in the factory was increased. Paradoxically, productivity also improved when the lighting level was subsequently decreased. The summary explanation for the effect suggested that the productivity gain among the factory workers was simply due to the motivational effect of the interest being shown in them. Well, there is a new development: according to an article in this week’s Economist, “idiosyncrasies in the way the experiments were conducted may have led to misleading interpretations of what happened.” In other words, the Hawthorne effect may not be true.

Notwithstanding new doubt about the Hawthorne effect, a compelling example of the power of measurement to change behavior comes from the world of medicine and public health. As detailed in an extensive 2006 New Yorker article by Atul Gawande, an unlikely revolutionary named Virginia Apgar transformed obstetrics and the nature of childbirth by devising a simple standard for scoring the health of a newborn baby at one, five, and sometimes ten minutes after birth. According to the New Yorker article (emphasis added):

The score was published in 1953, and it transformed child delivery. It turned an intangible and impressionistic clinical concept-the condition of a newly born baby-into a number that people could collect and compare. Using it required observation and documentation of the true condition of every baby. Moreover, even if only because doctors are competitive, it drove them to want to produce better scores-and therefore better outcomes-for the newborns they delivered.

Before Apgar scoring, babies delivered prematurely and with critical neonatal problems were often assumed to be not viable, and simply left to perish. The Apgar score, and the competition it produced, motivated doctors to take already understood steps to aid low-scoring newborns. The impact on neonatal mortality and survival has been astonishing. Again from the article:

In the United States today, a full-term baby dies in just one out of five hundred childbirths, and a mother dies in one in ten thousand. If the statistics of 1940 had persisted, fifteen thousand mothers would have died last year (instead of fewer than five hundred)-and a hundred and twenty thousand newborns (instead of one-sixth that number).

A simple measurement changed the behavior of medical professionals, and by extension, the hospitals in which they worked. The outcome of the behavior change is the survival of millions of babies. Carefully chosen and communicated measures have the power to change behavior and outcomes. This is the foundation for the design of every change program driven by the balanced scorecard.

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