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Business Modeling Needs More than Spreadsheet Dinosaurs

Sean Salleh 07 Jun 2013 Modeling methods

Huge body, tiny brain. Those were prominent characteristics of dinosaurs and part of their DNA. Those that were unable to cope with changes died out, although they had a good run – about 180 million years, all told. Information technology has its pre-history as well; some of the business modeling software is still with us with the same computing DNA from way back when.

dinosaur skeleton head Photo courtesy of Pixabay.com

Darwin Would Have Had Doubts

Spreadsheet programs for PCs are an example. While vendors have introduced new functionality since the days when VisiCalc roamed the earth, the roots of Excel and its competitors are still in a grid address based, rows and columns paradigm – more than just a few evolutionary cycles away from the needs of business modeling for meaningful representations of concepts, relationships and possible predictions.

Popularity is (Almost) Everything

But isn’t Excel still one of the most popular software applications out there, especially in the business world? Yes, indeed. Excel and other spreadsheets offer individual users efficient ways of storing, ordering and (numerically) manipulating data. And there is still an effect of magic at work when columns and rows of numbers are instantly recalculated by changing one or more of the base data, such as manufacturing cost, product retail prices or employer contributions to health insurance.

Dangers of Computing Democracy

Excel cryptic formulas
Playing the Guessing Game With Spreadsheet Formulas

True, information entered is still mostly referenced by its cell number, a row and column combination like B6 or R34. There are possibilities to use meaningful names (like ‘retail_price’) for cells, although there may be practical limitations on the numbers of such names you can use. But the power is with the people, and those people use Excel to build spreadsheets of ever increasingly complexity; including ones that are destined for enterprise business modeling. After all, Excel can do ‘what if’ scenarios, right? So is there anything wrong with using Excel to help predict a company’s future?

What’s Wrong with Excel

It turns out that there is in fact rather a lot wrong with trying to use spreadsheets to model businesses of any reasonable size. The key deficiencies of spreadsheets include not only the meaningless cell references mentioned above, but also dependencies that may be invisible to anyone other than the original creator of the spreadsheet, indecipherable complexity, no protection against tampering, and little or no support for handling uncertainties or sensitivity analysis.

Moving Up the Evolutionary Curve

By comparison, purpose-built decision support software such as Analytica deals with these issues by designing in from the start functionality to make business modeling a simpler and surer process for interested parties. Key aspects of Analytica include built-in functionality for intuitive influence diagrams, smart automated updates to entire tables and arrays, built-in treatment of uncertainty (using Monte Carlo simulation, for example), and not only sensitivity but also importance analysis. It’s a solution that also comes a lot closer to meeting the recommendations of bodies like the BPM Institute concerning business process management modeling for example.

Analytica influence diagram Beyond Spreadsheets… An Analytica ‘Influence Diagram’
for Meaningful Business Models

Never Say Die?

Meanwhile, the spreadsheet lives on. Gigantic spreadsheet files continue to engender no less gigantic errors. Notable disasters attributed to Excel include faulty national economy models by Harvard professors, multi-billion dollar losses by global banks and investment companies, unpleasant surprises in corporate acquisitions, and state-level education budget bungles. All of which brings us back to where we came in. Huge body, tiny brain…

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Sean Salleh

Sean Salleh is a data scientist with experience in guiding marketing strategy from building marketing mix models, forecasting models, scenario planning models, and algorithms. He is passionate about consumer technologies and resource management. He has master's degrees in Operations Research from University of California Irvine and Mathematics from Northeastern University.

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