What's Wrong with Spreadsheets?
The spreadsheet was the first “killer app,” the application which led to millions of people buying their first personal computers in the early 1980s. Spreadsheets were a brilliant innovation for replacing accountants’ paper spreadsheets. But, they are poorly suited for serious financial modeling. Empirical studies show that more than half of spreadsheets in regular operational use have serious errors. Thirty-five years after the release of VisiCalc, the first spreadsheet, it’s time for something better.
A modeling tool like Analytica that displays entities and lets you directly interact with them is much more intuitive to use. It reduces the need to mentally translate between “inner representations” and the one used by the software. Analytica makes it much easier to write, review, verify, explain, and extend models. It reduces the number of errors by preventing many kinds of errors from being made in the first place, and by making remaining errors easier to detect and fix.
The Top 10 problems with Excel and how Analytica offers solutions.
1. Meaningless names
2. No structured documentation
Benefit: You can easily include clear, complete, model documentation as you go.
3. Variables don't have defined types or roles
An Excel cell can contain a number, formula, text value, documentary text, or empty space. It can be an input, output, or intermediate calculation—a decision variable, a constant, the index of a table, or an objective to be optimized—among many other things.
Benefit: The modeler and software both understand the role of each variable – which prevents common conceptual errors.
4. Invisible model structure
Excel offers no easy way to visualize the overall structure of a model. Analytica has influence diagrams that provide an intuitive graphical view, depicting variables as nodes and dependencies as arrows.
Benefit: Modelers and decision makers communicate clearly with each other about key assumptions and model structure. It’s easy to navigate large models.
5. Little support for modularity
Benefit: Complex models become manageable. Each diagram shows key variables and relations, and hides irrelevant details in submodules.
6. Formulas refer to cells not tables
Benefit: The number of formulas to write, verify, and debug is often 100 to 1,000 times less, hugely reducing chances for error.
7. Editing or adding a dimension requires considerable effort
Benefit: Editing or adding a dimension automatically updates all affected tables and keeps formulas correct.
8. No treatment of uncertainty
9. Minimal support for sensitivity analysis
Benefit: Modelers without special training can easily do importance analyses and generate valuable insights.