Wednesday, January 12, 2011

Excel Model Building - Experts vs. Non-Experts

Excel Model Building -

Experts vs. Non-experts



What makes a great Excel modeler? There actually are identifiable skills and problem-solving tendencies that expert Excel modelers seem to possess in greater abundance than the average Excel modeler.

A brief study was done by Dartmouth Professors Stephen Powell and Kenneth Baker, authors of the best book ever written about quantitative Excel modeling: Management Science – The Art of Modeling With Spreadsheets. (If you are interested, I've reviewed the book on this page. It is the 4th Internet marketing book review on the web page)

Professors Powell and Baker audiotaped 28 of their MBA students work through 4 ill-structured modeling problems. The focus of the test was not to observe the students create a spreadsheet for a well-defined problem but rather to craft the right Excel modeling approach to a problem that had not been well thought-out and structured in advance. This situation much more closely approximates the real life faced by a consultant or business decision-maker.

Major Differences Between Expert and Non-Expert Model Builders

Professors Powell and Baker listed the following conclusions of their study in their well-respected book:

1) The expert model builders would build the model first and then ask for data later. Non-experts would immediately focus on the data. Non-experts appeared to exhibit a much stronger reliance for the data at hand.

2) Experts would create and evaluate a number of solutions before narrowing their efforts on just one. Non-experts tended to select an overall direction after significantly less evaluation or creation of other alternatives. Non-experts sometimes had a tendency to try and avoid the model-building process altogether and propose solutions such as: “Let’s call an expert” or “Let’s do market research.”

3) Expert modelers as a whole are stronger in math than non-experts.

4) Expert modelers would think more in terms of variables and relationships in the early stages of problem solving. Non-experts would often use concrete numbers right away.

5) Experts tended to stop much more often during the model-building process and re-evaluate their current progress and direction.

6) Experts initially framed problems in ways that could be translated to a model quickly. Non-experts tended to be much more unstructured in their early attempts to create an overall plan. Their attempts at early organization seemed to resemble brainstorming sessions.

7) Expert model builders solved the problems by working backward from what the solution was supposed to provide. Non-experts did not use this approach as much.

Conclusions

Practical Excel model-building advice based upon the modeling approaches of experts would be the following:

1) Don’t expect data to answer the problem.

2) Don’t be in a rush to find the quick answer.

3) Try to see the forest through the trees. Always keep in focus what the overall goal of the model is.

4) Try to create and evaluate a number of different approaches before deciding on the overall direction that your model will take.

5) Regularly re-evaluate your progress toward your model’s completion and the direction that you are currently taking.


Here are some other blog entries related to this article:

Correctable Reasons Why Your Data Is Not Normally Distributed

22 Big Statistical Mistakes You Don't Want To Make

Logistic Regression in Excel To Predict If a Prospect Will Buy

How To Build a Better Split-Tester in Excel Than the Google Web Site Optimizer

Using the Normal Distribution in Excel To Find Your Daily Range of Sales

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