## 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

**Analytica**, each variable is an object with fields for name, description, units, as well as definition (formula) and value.

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.

**Analytica**, each object has a class, such as decision, constant, chance variable, objective, or index, defining its role in the model.

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

**Analytica**, you organize a large model as a hierarchy of simple comprehensible modules.

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

**Analytica**understands the indexes that identify dimensions of a table or array.

Benefit: Editing or adding a dimension automatically updates all affected tables and keeps formulas correct.

## 8 No treatment of uncertainty

**Analytica**, the value of any variable can be a probability distribution. Efficient Monte Carlo simulation generates the corresponding distribution on results.

## 9 Minimal support for sensitivity analysis

Benefit: Modelers without special training can easily do importance analyses and generate valuable insights.

## 10 No separation of end-user interface from the model logic

**Analytica**, it is easy to create “dashboards” for users to access key inputs and outputs.