What is a “good model”?

Chances are, you have worked with models.  You may build and run complex models spanning many years or detailing lots of “steps” or “lines”.  You may have also used some simple models, such as a hand-drawn map that tells you how to get to the park.

A model is simply “some representation of reality”.  You have a real product line and a set of salespeople.  They take real orders from real customers.  If all goes well, you will receive real dollars (or Euros) for the sale.  So you have a speadsheet for projecting sales revenue associated with all this.  After a hard day at work, you may go running at a nearby park, and a simple hand-drawn map may suffice in getting you there.

Both the “sales projection spreadsheet” and the “hand drawn map to the park” are models.  There are two key features of a model, any model, that can be illustrated through the above examples.

1. A model must be constructed with more or less a specific question in mind.
2. A model is “good” or “bad” in light of this purpose.

So a hand-scribbled model, not drawn to scale, with some streets that are not labeled may indeed suffice for your manoeuvring through the neighborhood and finding the park.  But if you wanted to lay down utility lines and plan some street-ripping construction, you would want a different model, one that shows more specific dimensions and perhaps what kind of surface materials you are dealing with.

In many of the modeling and analytics work I do, I get asked, “how accurate is it?” or “how much data is in it?”.  I believe that the questions are valid, but the first questions to ask are: “what are we trying to solve?”.

Before you build a model, think about what you are trying to do.  Who is the audience?  How will the model (or the results) be used? What kind of questions will people ask?  Then we can go about discussing “level of detail” or “what kind of data”.

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  • Mike Munsil  On June 22, 2010 at 12:41 PM

    Good stuff, Howard.

    To me a good model is the one that I later find out lied to me the least. Of course, that implies I never really know how good the model is until AFTER I make decisions based upon it… just saying.

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