What is a model?
George and I build models. It says so on our new business cards. So what is a model?

BTW, we are not the only ones who build models. We all build models, you probably build really good models and more often than you think. They may not be in Excel and may not be presentable on a PowerPoint slide... but you have built and used models... sometimes to save your own life.

Let me take driving as an example to illustrate two very important aspects of a good model.

1. A model is NOT reality, but a representation of reality.
2. A model is "good" or "bad" based on the purpose of the model.

You adhere to these key rules of good model building when you drive. We discus the first aspect in this entry.

A model is only a representation of reality. There is an apocryphal story about a king who wanted to create the best map of his kingdom. He commissioned a task force that was mandated to create the most accurate map of his kingdom. It was to show everything so there would be no mistake and confusion as to what was in his kingdom.

With this mandate, the task force went to work. After a few weeks, the king demanded to see the work in progress. They brought him their "latest version", rolling it in on a cart. As they started to unfold the huge map, the king realized that the map was "actual size". It started with his court, and showed where the throne was, depicted in the same size as the throne. It showed what the floor looked like, and every location of every wall and column.

This was not a good map. This is not a good model. It may be accurate, but it's impractical (among other things).

When you drive, you are constantly building, using and rebuilding models in your mind to run "projections" and ready yourself for or to directly take actions.. Will that car move over to my lane? Do I need to cover my brake? You use a "map" in your head to plot out where you need to go. You incorporate this info with your knowledge of physics, how a car operates, your state (are you in a rush or are you enjoying a leisurely ride?) and many more elements to help you drive.

Whew! To illustrate the first aspect of modeling... have you realized that your model is not reality? It's only in your mind. It's also probably filled with incorrect and incomplete information. And yet you rely on this as you make your way down the streets and highways. This incomplete, sometimes inaccurate model, is used by you and the other drivers as each of you go about your driving day.

Good thing, too. You have learned to only include things in your model that is needed, to abstract and simplify things. In my next post, I will continue by talking about the second aspect of "what is a model".
- posted by hcpark @ 10:22 AM 1 comments

 
Business Analytics, Defined (Sort Of)
You’ve probably heard a lot about Business Analytics lately – it is a term that is thrown around with other vague notions like added value, ecosystem, and integration. If we keep it vague, firms won’t be able to embrace it, won’t recognize when they’ve got it, and can’t compare it to “not having it”. Yet Business Analytics is one of those things that defy strict definition, even to those of us who build Business Analytics every day.

Business Analytics “feels” different from simple graphs derived from a spreadsheet, even different from a finely tuned executive dashboard using the best available Balanced Scorecard practices. It is different from Six Sigma measures of process performance. But if these aren’t it, what is it?

For the moment I’ll sidestep the challenge of strict definition and try to lend some identifying characteristics to Business Analytics. Think of these as signature features that if present, probably mean that you are somewhere in that space, somewhere near the arena of good practice – a place we like to call a Next Generation Enterprise.

Business Analytics has the feel of search. BA is not static, not a bunch of numbers in a pre-ordered format…good BA starts with nothing but a notion of “I know what I want when I see it”. Therefore you might start with typing a phrase such as “how many blue widgets with the optional thingy did we produce last quarter?” Hmmm…lower than I thought…was it a seasonal thing? I’ll now compare that to different quarters across the years…hmmm…yep…it does appear to be seasonal. I wonder if the red widgets are similarly effected…? If such free-flowing navigation is not on par with your favorite search engine, you probably don’t possess Business Analytics.

Business Analytics allows for user-driven, rule-based automation. If people are thinking about their jobs, and thinking about their firms, they also should be thinking about what vital information might trigger an important action. Let’s say that you’ve noticed that whenever a competitor adds a new product line, unit prices follow a distinctive curve over two quarters. Any BA system worth its salt should let you take that idea, describe it in a non-programmatic way, and have the system automatically support or refute that hypothesis over time.

Business Analytics measures everything. I know a certain software CEO who has measured every hit to the company’s website since 1994. They’ve committed every email archive to a freeform searchable repository. Each year when the Nobel Prizes are awarded, they know within minutes whether the winner owns their software, uses it regularly, and how many interactions they’ve shared. Now this is a bit radical, I know…but the overarching point here is that storing data comes at a near-zero cost, and data is the basic fuel of good analytics. You can’t hope to know in advance what data someone might need to do some innovative study -- why not err on the side of too much data than the more frequent stance of collecting just enough data to get by?

So your homework is this: think of one basic, fundamental question that you could ask about your company – a question that anyone outside the firm might want to know. Then see how much energy, time, and consternation this question generates.

If you are in a car company, you might ask: How many white XLC pickups did we sell in Nebraska in 1987?

If you are in a drug company, you might ask: how many labor-hours went into the development of our latest cholesterol drug?

If you are in a retailer, you might ask: which store has the best ratio of sales to floor space?

These are fairly simple, straightforward questions, wouldn’t you agree? This is data the company should be studying regularly, and therefore should be within someone’s grasp at a moment’s notice. I suspect that you will find that more often than not, this data will be surprisingly difficult and expensive to acquire.

Firms talk a big game these days about innovation – unleashing the intellectual power of its talent to solve tough problems. But if we haven’t given our talent access to the most basic ingredient of innovation, such talk is exactly that.

George Danner
- posted by Industrial Science @ 5:20 PM 1 comments