Strategy Survival Guide

Strategy Survival Guide Version 2.1
Prime Minister’s Strategy Unit
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Strategy Skills > Building an Evidence Base
Analysing data - Modelling
> in practice
Modelling is a very useful analytical tool that aims to establish formal mathematical relationships between
variables. Models can take a variety of forms, and it is important to select the right model for the
circumstances:
• In some situations, the variables and issues of interest can be narrowly and tightly defined, in which
case the model should in turn be narrow in its coverage, but detailed within its boundaries.
• In other circumstances, variables and issues of interest may go much wider (e.g. impact on the
whole economy), in which case the model will inevitably be less detailed, but with much wider
coverage.
Another choice to be made will be with regard to the degree of quantification. Is it necessary to determine the
amount of an impact, and can the data tell us this information? Or is a qualitative indication of impact (i.e.
direction of effect) sufficient?
Once the right type and level of model has been selected, the key is then to understand the model’s
structure:
• If the modelling work is going to be carried out in-house, an appropriate functional form will need to
be decided and the necessary data collected.
• Models will often be "bought in" from outside, rather than developed in-house. But this should never
be an excuse for simply treating them as a "black box", without understanding what makes them tick.
It is vital to understand why/how models produce the results they do, always ask: Which variables in
the model are driving the results obtained?
In either case, it will be important to get a good feel for the key determinants of the model’s results, so that
they can be used appropriately and intelligently. For example, is the model based on relationships estimated
on historic data? Or does it use survey data? To what extent does it incorporate behavioural change?
Modelling Tips
• Modelling is not just data mining, it needs to be based on theoretical foundations.
• Sensitivity analysis (i.e. assessing the impact of varying assumptions or variables) is useful in
understanding what drives a model's results.
• Need clarity about what is endogenous and what is exogenous to the model.
• A rich data set is needed to construct a robust model.
• Modelling can be very time and resource intensive - hence the likelihood of choosing to buy-in
existing models.
Excel Modelling
Modelling in Excel is like any other piece of analysis - you require a clear understanding of the questions at
hand, a vision of the output, a good plan to get there, time to work through the plan to completion and the
ability to package the analysis for review. Failure to do so will almost certainly result in the need for rework,
lost time and frustration.
There are a number of steps, which if followed, will assist in creating a successful Excel model.
Strategy Survival Guide – Strategy Skills
Page 129