FP&A Professionals: Make Your Clients Intelligent Consumers

I am speaking next week at the annual national conference of the Association for Financial Professionals (AFP) in Washington, DC. Since financial planning & analysis (FP&A) professionals are a key AFP constituency, I thought now would be a good time for an open letter to the FP&A folks.

There’s no question that a, if not the, principal role of the FP&A function is to produce meaningful information enabling enterprise managers to make fast, intelligent decisions. The ability to design reports achieving that is rare and valuable. It requires great communication skills, knowledge of the systems tools, and a deep understanding of the underlying enterprise. But is that enough? I say no, emphatically. A critical player in the design of great management reports is the managers who need those reports to do their jobs effectively. So, if your job involves producing management information, ask yourself:

  • Do you devote sufficient time to teaching your user clients to read your reports? Remember that many of them are not financially skilled.
  • Do you actively, aggressively seek feedback from those user clients, about what’s meaningful, useful, and comprehensible, and what isn’t? This is one area where silence is not golden.
  • Do you actually make changes in response to that feedback about your reports?

In other words, are you making the effort to ensure that your clients are intelligent consumers of management information? Your career will thank you.

As an aside, I have developed a training course, “Reading the Management Income Statement: A Course for Non-Financial Managers,” available on Proformative, a popular site for senior finance professionals. It reflects the approach I describe in this post. If you or someone you know would like to enroll, use http://bit.ly/1wg2HvX and then click through to the course. I also offer three other courses on the Proformative site:  “The Evolution of a Great Spreadsheet Report” (use http://bit.ly/QXFcJt), “Excel-ing When Your PowerPoint Is Loaded with Numbers” (use http://bit.ly/1keqViv), and “Designing a Great Management P&L” (use http://bit.ly/1u23GAq). If you take any of these courses, many thanks, and I would really appreciate your feedback.

“Painting with Numbers” is my effort to get people to focus on making numbers understandable.  I welcome your feedback and your favorite examples.  Follow me on twitter at @RandallBolten.

 

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The World Is So-o-o Random!

Today’s USA Today story about last night’s World Series game (GO GIANTS!) includes an interesting graphic, showing the success of “wild card” teams since they were first included in Major League Baseball’s postseason playoffs in 1995. 2014 is the first year that two wild card teams have faced off in the World Series – yes, a major-league record!!! – so a wild card team is certain to win the World Series, the sixth time that has occurred.

Six times in 20 years.  Only one-fourth of the teams are wild cards, and yet wild cards win one-third of the time. Does this mean that wild card teams have a better chance of winning the World Series than division champions? Of course not. After a 162-game regular season, the playoffs are reduced to five- and seven-game series, where much can happen. And, as baseball fans say much too often, it’s a round ball and a round bat.

So much of life is random, and not just baseball. Or at least complicated beyond human comprehension, which is pretty much the same thing as random.  And yet, we humans believe there must be a reason for what has happened, and then explain that reason to others. Every day the stock market goes up (or down), and journalists and pundits attribute that to one or more of the gazillions of good (or bad) things that happened that day. Why don’t they just say, “Well, the stock market behaved randomly again today”?

Now, I ask you: Will the emergence of big data make our understanding of human behavior better or worse? Are you sure?

As an aside, baseball lends itself beautifully to numbers and just generally keeping track of stuff. For example, before he gave up a home run yesterday, Madison Bumgarner had pitched 32 2/3 consecutive scoreless postseason innings on the road, a major-league record. And game 3 of the NLDS series between the Giants and the Washington Nationals went 18 innings and mind-numbing 6 hours, 23 minutes, another – yes! – major-league postseason record. Oh, and Brandon Crawford’s grand slam homer in the Giants’ Wild Card Game against the Pittsburgh Pirates was the first by a shortstop in postseason play. Ever. In 111 years of postseason play. You’ve gotta love a sport like that. So does big data.

“Painting with Numbers” is my effort to get people to focus on making numbers understandable.  I welcome your feedback and your favorite examples.  Follow me on twitter at @RandallBolten.

 

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Math Is a LANGUAGE First, a Technical Skill Second

On this national holiday, let us ponder how we are educating our children, and ask ourselves: What is mathematics for?

Is Math Liberal?” – a recent review on the Mother Jones website of How Not to Be Wrong: The Power of Mathematical Thinking, a book by University of Wisconsin math professor Jordan Ellenberg – suggests that math is not just about computation, but also a framework for thinking through problems intelligently. Not surprisingly for a piece in Mother Jones, the article suggests that math used intelligently can provide insights in areas such as politics and religion.

Well, OK, but it’s even simpler than that. Even before viewing math as a computational skill – which it is – and as a thinking skill – which it also is – it must be seen as a language for describing complex situations and problems. When you are using numbers to describe a company’s financial results, or a sales commission plan, or a price list, or how well a baseball team is playing, or for that matter the fiscal state of the U.S. federal government, you’re not asking your audience to do any computation whatsoever, nor necessarily even any analytical thinking. You’re just describing.

Think back on all the times numbers were presented unclearly or downright incoherently. Were the concepts too difficult or complex to articulate, or for the audience to understand? Probably not.

It’s a shame that math isn’t taught in school as a communication skill, and not just as a computation skill. If it were, all those numbers we’re deluged with would be so much easier to understand, and so much more meaningful.

Let’s teach our children quantation! And have a great July 4th!

“Painting with Numbers” is my effort to get people to focus on making numbers understandable.  I welcome your feedback and your favorite examples.  Follow me on twitter at @RandallBolten.

 

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Quantation: Don’t Blame Your Audience

This week I had the privilege of presenting at the Institute of Management Accountants Annual Conference in Minneapolis. They are a great audience for my “Painting with Numbers” message and I thoroughly enjoyed interacting with the IMA members.

I also had the chance to attend a number of excellent presentations. My favorite was Gary Cokins’ on “Business Analytics for Decision Support and Value Creation.” Cokins is IMA’s Executive in Residence, and has decades of experience in the subject. Perhaps most notably, he was an early pioneer in the introduction of activity-based costing (ABC), a powerful method for properly allocating costs to products, activities, and services.

I had one quibble with Cokins’ presentation, though. Near the end of his presentation, he observed that ABC and other powerful analytical tools haven’t had the widespread adoption that such techniques justify. As explanation, he cited a number of technology and process factors, and described how they could be addressed.

He concluded by citing cultural/behavioral barriers, such as resistance to change, fear of being held accountable, and plain old laziness. But in essence, these arguments blame line managers – that is, the audience for analytical tools – by questioning their motives or their intelligence.  These may be valid points, but the audience – that is, the decision-makers – will almost certainly respond by arguing that:

  • Advocates of the analytical tools didn’t articulate their points clearly.
  • The advocates did articulate the points clearly, but the value didn’t justify the effort or the expense.
  • The advocates failed to include considerations that change the value proposition.

It’s comforting to blame the audience because “they just don’t get it.” I’ve seen that perspective over and over in my career – quantation is hard for many people to understand. But it’s not an excuse. When that happens, it’s time to go your reports and your presentations and your elevator pitch, and see what can be improved and made more persuasive.

So don’t blame your audience when they don’t understand your message.

Even when you’re right to feel it’s their fault.

“Painting with Numbers” is my effort to get people to focus on making numbers understandable.  I welcome your feedback and your favorite examples.  Follow me on twitter at @RandallBolten.

 

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Income Inequality, Political Polarization, and Big Data

A fascinating article in a recent Washington Post draws a striking comparison between income inequality and political polarization. It suggests a high correlation between greater inequality and more polarization. That’s all well and good, but it’s all meaningless if you don’t hypothesize a causal relationship between the two. And the emergence of big data enables us to go off on more and more efforts that are frequently wild-goose chases.

Here’s a graph from the story, plotting inequality on the vertical axis and polarization on the horizontal axis, for every year from 1917 through 2011:

The graph’s added wrinkle – drawing a line between each adjacent year, so you can see how the metric between the two relationships has evolved over the years – is worth remembering, as a clever way of presenting three dimensions of data on a two-dimensional plot. There is certainly visual evidence of a correlation between inequality and polarization, and the lines connecting the points show how the relationship between polarization and income disparity has shifted over time.

But there the value of this analysis, at least for me, ends. The accompanying article seems to be headed toward a number of unsupported, and dubious, conclusions, like the author’s assertion that “tax policy has almost certainly played a role in driving up inequality” in the last 35 years, which is arguable at best.  Most significant – though qualified by a buried, mild reminder that “all this tells us nothing about causality” – is the implication that polarization causes inequality.

The strongest visual clue of that editorial inclination is in the basic layout of the above graph: polarization is scaled along the horizontal axis (or “X-axis”) and income inequality along the vertical (or “Y-axis”). It is standard practice to graph the independent, or causal, variable along the X-axis and the dependent variable along the Y-axis.  (Or, as we learned in algebra I, y = ¦(x).)

There’s no question that income inequality is a serious problem and getting worse. But is it caused by polarized politics? If anything, my intuition favors the converse: that income inequality is exactly the kind of large, complex, emotional issue with debatable solutions that causes, rather than is caused by, political polarization.

What does all this have to do with big data? Well, big data software enables us only to identify correlations between variables.  It doesn’t tell us whether a causal relationship exists, let alone which direction the causal relationship runs. But we’ll certainly be able to find more of these correlations.

So whether we’re talking business, consumer behavior, public policy, or sports, be prepared for more of these partially-baked analyses.

“Painting with Numbers” is my effort to get people to focus on making numbers understandable.  I welcome your feedback and your favorite examples.  Follow me on twitter at @RandallBolten.

 

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VA Bonuses: This Week’s WORST Government Decision

During a week in which the most hotly debated news topic has been the quality of decision-making by senior federal government officials, my nominee for the worst decision is VA Secretary Eric Shinseki’s parting gesture not only to cancel performance bonuses for senior VA executives, but to ban patient wait time as a measure used in performance evaluations.

Once again, our senior government officials are focused on solving the wrong problem. Given the obvious importance of serving our veterans and the obvious magnitude of the waiting-time problem, now is the perfect time to reward administrators who successfully shorten waiting times. The bonuses should of course not be paid out until the waiting time numbers are validated, but isn’t that equally obvious?

Commentators and legislators on both sides of the aisle are decrying the crass lure of money and its corrupting influence. Yes, that’s one way to look at it. Here’s another way: embrace the powerful effect of compensation on motivation and performance. The best metrics for rewarding performance are measurable, easy to understand, and bear an explicit and clear relationship to the enterprise’s strategic goals – in those senses, VA patient waiting time is an ideal metric. And the metric should either be hard to fudge or measurement should be properly controlled and validated – this concept is deeply ingrained in well-run businesses, but less so where our tax dollars are concerned.

Properly rewarding the people who are part of the solution is the right thing to do, not the wrong thing.

[As a side note, I’m equally disappointed in the response of Republican members of Congress, who are using this fiasco to pile more mud on an administration of already questionable competence. As members of the party that is supposed to be in favor of a more businesslike government, these legislators could have taken a more constructive approach, rather than calling for measures that punish good administrators as well as bad ones.]

[On another side note, I ask you: How does this chain of events, including the administration’s response, affect your confidence in our government’s ability to administer a single-payer healthcare system?]

“Painting with Numbers” is my effort to get people to focus on making numbers understandable.  I welcome your feedback and your favorite examples.  Follow me on twitter at @RandallBolten.

 

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Realtor Commissions: A Lesson in Incentive Compensation

The standard realtor commission structure doesn’t motivate the realtor to do his or her best for you. As you read this post and my suggested improvement, think about how this situation relates to the incentive compensation plans in your own enterprise, and why incentive compensation is such a delicate art.

Here’s the problem: The standard realtor commission is a straight 6% of the sales price, paid by the seller. That 6% is divided evenly between the listing firm and the buyer’s firm, and each firm’s 3% is divided evenly between the firm and the individual realtor. So if you sell your property for, say, $500,000, your realtor pockets 1.5%, or $7,500.

Now, how hard will your realtor work to get you a price that’s, say, $20,000 higher, if that means an extra couple of weeks work? $20,000 is certainly material to you, but that only means an extra $300 in your realtor’s pocket – hardly enough to justify the distraction from working with other clients. [This incentive problem has long been pointed out, most notably in Freakonomics, the excellent book by Steven Levitt and Stephen Dubner.]

Instead, let me offer this modest proposal for revising the commission structure: Each realtor competing for the listing proposes an asking price for the property. The owner chooses one of the realtors. The “Base Commission” is 6% of the asking price proposed by the winning realtor. The actual commission depends on the sales price, and is computed as follows:

  • Exactly the asking price – the Base Commission (just like the old way)
  • Above the asking price – the Base Commission, plus 12% (not 6%) of the difference between the actual sales price and the asking price
  • Below the asking price – the Base Commission, minus 12% of the difference between the asking price and the actual sales price

The advantages of this approach are:

  • Sellers will still be inclined to choose the realtor proposing the highest asking price, because (a) that minimizes the commission at any given sales price and (b) a high suggested asking price may indicate an enthusiastic realtor.
  • Even so, realtors will think twice about proposing too high an asking price because their actual commission is affected by that asking price. The result is a disincentive to “highball” the asking price – a disincentive that certainly doesn’t exist under the current scheme.
  • Realtors will try harder to get the seller a better price, because the marginal commission rate of 12% on the additional dollars is double the Base Commission rate of 6%.

The most important lesson, which applies to all incentive compensation schemes, is this: The real art lies in paying the recipient fairly not when he/she exactly achieves expected performance – anyone can design a comp plan to do that – but when actual performance is significantly above or below expectations.

“Painting with Numbers” is my effort to get people to focus on making numbers understandable.  I welcome your feedback and your favorite examples.  Follow me on twitter at @RandallBolten.

 

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Most Metrics Are RELATIVE, Not Absolute

What are metrics for? When properly used, most are nothing more than a relative tool for comparing one time period – or company, or country, or person – to another, or actual results to planned or expected results. They are not an absolute score, meaningful on its own. It’s important to remember this, whether the metric involves business, government, sports, or national economic performance.

I saw yet another article about a change in the U.S. unemployment rate (downward this time) with the same tired old qualifying observation that the change doesn’t factor in people who have given up and chosen to stop looking for work, thereby understating the true unemployment rate by reducing the number of verifiably, measurably unemployed.

To this news, my reaction is, “So what?” Let’s call this phenomenon “the flaw,” and ask ourselves:

  • Is the “flaw” a truly material number? (A home exercise: compute how many unemployed have to leave the labor force to move the unemployment rate from 7% to 6%.)
  • Is the “flaw” any more or less pronounced than it was the last time the rate went up (or down) by a similar amount?
  • Are the causes of the “flaw” uniformly negative – i.e., people leaving the workforce specifically because they’ve given up – or are there also positive explanations? For example, has someone else in the household who was unemployed now found a job? Have some people found “off the books” employment?

And most important, is the “flaw” so severe that that you can’t really tell whether the problem of unemployment is getting better or worse? If so, then the metric should be thrown out and replaced with a better one. If not, the hand-wringing is pointless.

Many, many metrics have similar flaws, but that doesn’t mean that they aren’t immensely useful even so.

“Painting with Numbers” is my effort to get people to focus on making numbers understandable.  I welcome your feedback and your favorite examples.  Follow me on twitter at @RandallBolten.

 

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Why ARE Those College Wait Lists So Long?

In today’s Washington Post, Jay Mathews writes about the dramatic increase in the length of college acceptance wait lists. His focus is on the strategies wait-listed high school seniors might pursue, but let’s consider just why those wait lists are so long.

Mathews suggests that admissions departments (a) don’t want to hurt applicants’ feelings and (b) want a cushion against too many applicants turning them down. Reason (a) is neither valid nor plausible, but reason (b) is spot-on, in large part because of the strategies used by today’s high schoolers.

From a fairly large sample – family and friends, cocktail parties, inquiring of applicants I’ve interviewed for my alma mater – I cannot recall the last student who applied to fewer than ten colleges. Twenty or thirty years ago, four or five applications was the norm. What’s changed? Well, when it is a widely-held belief that the college(s) of your choice is(are) becoming much more selective, applying to lots of colleges is the rational thing to do.

Gone forever are the days when a few juniors or seniors would hop in a car, visit a few colleges, interview with the admissions departments, and get a sense of which college felt right. And because the applicant pool is so large, many admissions departments won’t interview applicants – it’s not only expensive, but to do so would favor students with the wherewithal to travel to visit colleges, or ones at high schools that the admissions department chose to visit. Also, the Common Application has made it easier and less expensive to load up on applications.

As a result, colleges may have a pretty good sense of whom to accept, but almost no sense at all of who will accept them.  In that environment, using a wait list protects the college from both admitting too many applicants and admitting too few.

Mathews also takes the colleges to task for extremely long wait lists, citing Stanford – 800 for an incoming class of 1,700 freshman – and MIT – 700, for a class of about 1,100. But if colleges have a poor sense of how many will accept them, they certainly won’t have a sense of which students will turn them down.  So the wait list needs to have the requisite violinists, physicists, and philosophers to fill the unexpected gaps.

Extremely long wait lists – and the enormous increase in college applications in general – is the result of rational behavior by both applicants and colleges. But the resulting outcome certainly isn’t desirable.

“Painting with Numbers” is my effort to get people to focus on making numbers understandable.  I welcome your feedback and your favorite examples.  Follow me on twitter at @RandallBolten.

 

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Stupid Metrics Drive Out No Metrics At All

Developing scorekeeping metrics is a critically important yet undervalued role of the chief financial officer. CFOs ignore this role at their and their organizations’ peril, because if they don’t set the scorekeeping metrics, others will, and will make a mess of it.

All organizations crave metrics. We’re human, and that means we need to compare how we’re doing against our competitors, our peers, our commitments, or simply what we were hoping for. There are lots of possible metrics – business examples include revenues, profits, and dollar compensation. Metrics based on ratios make scoring less dependent on size and more memorable to the audience – e.g., margins, EPS, growth rates, percentage variances, market share, percent of new business, percentage raise. The art of all this lies in coming up with a small but meaningful number of the metrics that make the most sense for each organization.

Two recent stories in The Washington Post bring all this to mind. One reports that JPMorgan Chase’s 2013 proxy statement shows that CEO Jamie Dimon got either a huge pay cut in 2013 or a huge raise, depending on whether you look at the SEC-mandated disclosures (pay cut – see p. 47 of the proxy) or JPMC’s voluntary detailed disclosure (raise – p. 34). The other reports that the American Statistical Association has harshly criticized the “value-added method” (VAM), a highly quantitative method for evaluating teachers that depends heavily on standardized test score results. While VAM is gaining increasing traction in the U.S., it is extremely controversial.

In my opinion, both the SEC-mandated compensation disclosures and the VAM methodology are not only deeply flawed, but so complex that it’s hard to understand either the presentation of the results or the underlying methodology. Now, it’s easy to blame the regulators and politicians for both messes, but they were simply filling a void.  That void was created by the decades-long reluctance – if not downright refusal – of U.S. public companies and the teaching profession, respectively, to comply with the public’s request for fuller self-evaluation. In other words, “If you won’t tell us how you’d like to be scored, we’ll decide for you.”

For any enterprise, scorekeeping takes on many dimensions – investor valuation, general performance assessment, sales compensation, employee performance reviews, just to name a few. But regardless of the reason for the scorekeeping, the CFO is the right person to take responsibility for the task, because he or she is the one most likely to:

  • Be a neutral third party to all of the line functions in the enterprise,
  • Know what data is available and how to retrieve it, and
  • Have the necessary mathematical and analytical skills.

And if the CFO doesn’t determine the scorekeeping practices, someone else will serve your enterprise a fried telephone book.

“Painting with Numbers” is my effort to get people to focus on making numbers understandable.  I welcome your feedback and your favorite examples.  Follow me on twitter at @RandallBolten.

 

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