Einstein said, “Everything should be made as simple as possible, but not simpler”.

A lot of consultants would do well to reflect on this when it comes to analysis and modelling.  Yes, it is true that in a world of “Big Data”, sophisticated analysis can be a competitive edge (or even essential), but sometimes, or even often, complex models only lead to overwhelming confusion.

I don’t want to sell you a complex model; I want you to understand the problem you’re trying to solve and have confidence in the best decisions to make.

So I always try to start with the most simplest of calculations, charts etc. that can provide the insight needed on what are the most important or dominant factors in the problem at hand, and then only increase the sophistication of the analysis as my (and your) understanding and need increases.  If required, I can then apply my various analytical skills, as summarised below, or bring in others with specialist expertise when needed.

I highlight in particular my experience in statistics, risk simulation and “real options” for decision-making under uncertainty, or as I like to say, "using the FORCE":

- as illustrated by the diagram below for a transformational 10-year budgeting framework and in my analysis of a potential Sydney-Wollongong high-speed train service.

These skills bring to problems not just a technical capability, but a mindset that recognises there is no point wasting time, resources and anxiety over single-point estimates throughout a single-scenario model if actually there is inherently large uncertainty in the parameters that dominate optimal decisions, or conversely, if the uncertainty in other parameters doesn’t matter because it makes no difference to decision-making.  

Sometimes this sort of analysis may reveal that there is little value in learning more information (so you’re just going to have to take a risk, one way or the other), and sometimes it will show that the best decision is to wait and see.  When was the last time you decisively deferred a decision?

Here are some examples of how I’ve applied different kinds of analysis in previous work:

NB. Highlighted words previously linked to the following pages on this web site (until Google messed it up):

Decision-tree showing future flexibility ('real options') for responding to revenue fluctuations
- enabling more stable funding guidance to service-delivery agencies, using a 10-year budgeting framework: