Analytica Provides Low-Cost, High-Quality Decision Models

My friends at DM News, which has published my Software Review column for the past fifteen years, unceremoniously informed me this week that they had decided to stop carrying all of their paid columnists, myself included. This caught me in the middle of preparing a review of Lumina Analytica, a pretty interesting piece of simulation modeling software. Lest my research go to waste, I’ll write about Analytica here.

Analytica falls into the general class of software used to build mathematical models of systems or processes, and to then predict the results of a particular set of inputs. Business people typically use such software to understand the expected results of projects such as a new product launch or a marketing campaign, or to forecast the performance of their business as a whole. They can also be used to model the lifecycle of a customer or to calculate the results of key performance indicators as linked on a strategy map, if the relationships among those indicators have been defined with sufficient rigor.

When the relationships between inputs and outputs are simple, such models can be built in a spreadsheet. But even moderately complex business problems are beyond what a spreadsheet can reasonably handle: they have too many inputs and outputs and the relationships among these are too complicated. Analytica makes it relatively easy to specify these relationships by drawing them on an “influence diagram” that looks like a typical flow chart. Objects within the chart, representing the different inputs and outputs, can then be opened up to specify the precise mathematical relationships among the elements.

Analytica can also build models that run over a number of time periods, using results from previous periods as inputs to later periods. You can do something like this in a spreadsheet, but it takes a great many hard-coded formulas which are easy to get wrong and hard to change. Analytica also offers a wealth of tools for dealing with uncertainties, such as many different types of probability distributions. These are virtually impossible to handle in a normal spreadsheet model.

Apart from spreadsheets, Analytica fits between several niches in the software world. Its influence diagrams resemble the pictures drawn by graphics software: but unlike simple drawing programs, Analytica has actual calculations beneath its flow charts. On the other hand, Analytica is less powerful than process modeling software used to simulate manufacturing systems, call center operations, or other business processes. That software has many very sophisticated features tailored to modeling such flows in detail: for example, ways to simulate random variations in the arrival rates of telephone calls or to incorporate the need for one process step to wait until several others have been completed. It may be possible to do much of this in Analytica, but it would probably be stretching the software beyond its natural limits.

What Analytica does well is model specific decisions or business results over time. The diagram-building approach to creating models is quite powerful and intuitive, particularly because users can build modules within their models, so a single object on a high-level diagram actually refers to a separate, detailed diagram of its own. Object attributes include inputs, outputs and formulas describing how the outputs are calculated. Objects can also contain arrays to handle different conditions: for example, a customer object might use arrays to define treatments for different customer segments. This is a very powerful feature, since its lets an apparently simple model capture a great deal of actual detail.

Setting up a model in Analytica isn’t exactly simple, although it may be about as easy as possible given the inherent complexity of the task. Basically, users place the objects on a palette, connect them with arrows, and then open them up to define the details. There are many options within these details, so it does take some effort to learn how to get what you want. The vendor provides a tutorial and detailed manual to help with the learning process, and offers a variety of training and consulting options. Although it is accessible to just about anyway, the system is definitely oriented towards sophisticated users, providing advanced statistical features and methods that no one else would understand.

The other intriguing feature of Analytica is its price. The basic product costs a delightfully reasonable $1,295. Other versions range up to $4,000 including ability to access ODBC data sources, handle very large arrays, and run automated optimization procedures. A server-based version costs $8,000, but only very large companies would need that one.

This pricing is quite impressive. Modeling systems can easily cost tens or hundreds of thousands of dollars, and it’s not clear they provide much more capability than Analytica. On the other hand, Analytica’s output presentation is rather limited—some basic tables and graphs, plus several statistical measures of uncertainty. There’s that statistical orientation again: as a non-statistician, I would have preferred better visualization of results.

In my own work, Analytica could definitely provide a tool for building models to simulate customers’ behaviors as they flow through an Experience Matrix. This is already more than a spreadsheet can handle, and although it could be done in QlikTech it would be a challenge. Similarly, Analytica could be used in business planning and simulation. It wouldn’t be as powerful as a true agent-based model, but could provide an alternative that costs less and is much easier to learn how to build. If you’re in the market for this sort of modeling—particularly if you want to model uncertainties and not just fixed inputs—Analytica is definitely worth a look.