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Why is Modeling Foundational to Performance Management?

by Gary Cokins, Consultant, www.sas.comWednesday, March 04, 2009

The confusion, ambiguity and lack of consensus about what enterprise performance management is will continue for a long time. Fortunately, many are realizing that performance management is much broader than how it’s often narrowly perceived: as just a CFO initiative with a bunch of dashboard dials and better financial reporting. However, since it is so broad, then what is enterprise performance management?

I have frequently described this much broader view of the performance management framework. Read my article, “Why the High Interest in Performance Management Now?”, if you’d like to learn more about that topic. In this article, I’m going to discuss an essential capability of enterprise performance management: modeling.

Managing performance requires deep understanding of causality. Craig Schiff, a prominent IT analyst and CEO of BPM Partners, has written an article entitled “Why Performance Management?”   In it, he describes four interconnected segments. In my articles and books, I use an analogy of performance management components as meshed gears in a machine with a global positioning system (GPS) for strategy execution navigation.

I like Craig’s fourth segment: operational optimization. I think of this as enterprise optimization. I really like the term “optimization” even though it can be dismissed by some managers as theoretical or impractical to achieve. Enterprise optimization can be described as the pursuit and realization of an organization’s strategic objectives with the least amount of total resources in an ever-changing environment. This pursuit maximizes long-term shareholder wealth creation through a deep understanding of customers.

But this description of optimization is only shallow rhetoric unless we dig deeper. What is the role of business analytics and modeling for enterprise optimization?

Modeling is essential to improving decisions. A model is a representation of physical activities and their outcomes. For some models, such as weather forecasts, the complex interdependencies of all the variables make the accuracy quickly decline. Hence, frequent re-calculating of the model is needed. For example, reliable weather forecasts, at best, project a week or two into the future. However, at its core, a model is based on understanding cause-and-effect relationships – and typically multiple and simultaneous ones. The better the relationships are understood, then the model's projections will be more reliable and longer lasting.

Where does understanding the input-output relationships for a model come from? The answer is analytics. Specifically, understanding the behavior of how anything works is based on analytics of all flavors, including segmentation and statistical correlation analysis.  I am familiar with this topic because my employer, SAS, provides software for business analytics and performance management solutions.

Modeling is prominent in fields such as skyscraper building construction and oil and gas exploration. Biologists model cell behavior; geneticists model DNA to understand diseases. Baseball executives model batter and pitcher outcomes to determine salaries or trades. It is not a big leap to apply the analytical methods and skills of engineers and scientists to managing and transforming an organization.

At the heart of modeling is decision making. And in any organization, decisions abound. For example, marketing analysts must decide which types of customers to retain, grow, win back or acquire (and which types not to). More deeply, what is the optimal spending amount on deals, discounts and offers to optimize future customer net revenues (profits)? How should an organization balance its risk appetite against exposure? How should the CFO report reliable rolling financial forecasts (since the budget is so quickly obsolete due to unexpected changes)? How should a personnel department identify the next employees who are likely to voluntarily quit or who to hire next? These questions can all be answered using business analytics.

A strategy map and its companion, the balanced scorecard, are popular for aligning the behavior of managers and employee teams with measurable, strategic objectives for which they can be held accountable. When you think of it, a strategy map is a model of an organization. What are your most vital key performance indicators (KPIs)? Use business analytics to test their correlations.

Optimization is about determining the best level of resources (e.g., human capital or equipment) to produce the highest yield and desired outcomes. Optimization includes managing that same “best level” of resources and aligning their behavior and priorities with the strategic objectives of the executive team. Optimization cannot be realized without business analytics. Modeling is foundational to achieving effective enterprise performance management and business analytics is at the heart of modeling.

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