14th Century Advice For 21st Century Leaders

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"One should not increase, beyond what is necessary, the number of entities required to explain anything."

-Occam's Razor

Occam's Razor is a guiding principle for building effective models. Devised in the 14th-century by William of Occam, an English logician and Franciscan friar, its vital core message is that the least complicated approach is usually the best.

The speed and power of the personal computer makes it too easy to add more and more complexity in the name of greater precision. This pursuit of precision often obscures the underlying purpose of modeling: We use models to find meaning, to teach and to explore potential.

Banking is an accounting profession charged with the responsibility to be precise in dealing with people's money. The very nature of accounting tends to reinforce our belief that pure precision is possible. Yet data must be organized and analyzed differently for modeling than for accounting: in accounting, we post facts; in modeling, we use relationships to generate theories.

When modeling, we may be tempted to use more variables, more assumptions and more complex methods in our desire to predict the future. The real trick, however, is to find the few critical variables, not a lot of lesser influencers. Multiple variables and complex calculations are more likely to obscure clarity than support it.

One of the most important models ever conceived-E=mc2-uses just one variable and one constant. Yet it can unlock the secrets of the universe. Do we need anything more complex?

Pitfalls Of Technology

Too often, information technology becomes bogged down in details without supporting the kind of understanding that can enable institutions to make high-level assessments of strategy, planning, budgeting, simulation and communication. This leaves executives, directors and other decision-makers without the strategic tools they need to lead.

Armed with a personal computer and a spreadsheet program, we can accomplish complex calculations easily and quickly. But what are the true benefits of numerical analysis? What is the underlying purpose of our calculations? To derive meaning. Then how do we derive meaning from calculations? Although we often present data as an end product and assume the recipient will grasp its meaning, raw data is only transformed into information and knowledge when it is organized clearly.

The Big Picture

Without history, we cannot understand direction or momentum, both vital to grasping true meaning. We need history's perspective of where we have been in order to find the meaning of our current circumstance or where we are going. For example, given only today's stock price for a company, without any historical perspective, we cannot grasp the relevance of the price.

Relevance takes knowledge to the next level. Each bit of knowledge combines with other knowledge to create a new reality or perspective. When knowledge can be used to anticipate and shape the future with confidence, we have the opportunity for wisdom.

The analyst's role is to bring order from chaos -knowledge from data-to support the decision process by presenting the results of analysis and projections with clarity. Decision-makers then can more easily combine it with their experience and form a perspective that brings confidence to decisions and actions.

This data hierarchy mirrors the way the human brain functions. In the brain, sensory data is received and classified into buckets. These buckets become learned sequences of recurring patterns which, in turn, are used to make predictions.

For example, if you hear a few notes of a song, your brain sifts through its known patterns or sequences of notes to recall the song. If it does not recall the song at first, it seeks additional sequences or more notes until the song is recognized. Once a familiar song is identified, you can predict upcoming sequences of notes.

In finance, the process begins when accounting data is collected. The data is organized into journals and sub-journals, then distilled into larger categories in the general ledger.

At the decision level, an analyst organizes the categories into recognizable patterns; for example, real estate loans may be growing. This fact can be combined with other data, such as rates, to help explain the growth in real estate loans.

Moving to the next level, the institution's leaders use their experience to project whether real estate loans might continue to grow. More information may be needed to support a prediction, perhaps even from another source (such as loan officers), in order to proceed.

Communication Is the Key

Calculations are important, but communication is vital. If the information represented by data is not effectively communicated, the true usefulness of that data is restricted.

So the final critical step for deriving understanding and meaning from data-and in turn being able to use the data effectively to direct and lead a financial institution-is to translate data into a form that can communicate quickly and easily, even intuitively.

How effectively do data tables alone communicate the meaning of that data? As you look at a financial table dense with data, allow your eyes to go out of focus. What do you see? Most likely, a block in shades of gray. Does it convey meaning? This simple exercise illustrates how such data tables communicate-in the absence of understanding- with the leaders of your institution.

Because the human brain works best when it perceives patterns, patterns help individuals understand what they see. Few of us can quickly identify patterns in numbers alone; most prefer to see concepts represented visually or graphically.

Gleaning From The data

Leaders must glean sufficient understanding from data to act on its meaning and implications; the best way to ensure this understanding is to provide a graphical display which clearly indicates history and direction (trends), strengths and vulnerabilities, and competitive comparisons. Tables and graphs can help transform a blur of numbers into a recognizable pattern almost instantly. Without them, our data is too often perceived as blocks of gray.

Thus we return to the practical value of a concise, uncomplicated approach. In modeling, clarity makes explanation easier to understand and work with.

In his world-so different from ours-800 years ago, Occam had it right, and the message still resonates: In the end, the most direct answer leads to the clearest understanding of reality.

Craig Hartman is CEO of Plansmith Corp., Schaumburg, Ill. He can be reached at 800- 323-3281 or www.plansmith.com.

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