Tuesday, 11 December 2012

Analyse the business and its environment

The world is full of strategy analysis tools, models and frameworks, many of which  are very useful for getting to grips with  the strategic challenges the organisation faces. See 7 essential strategy analysis tools for examples of some of the most popular and useful ones.

However, it is important to remember that all of these models are a means to an end, and not an end in themselves. The 'end' is to generate strategic insight which is useful in generating successful strategy. The strategy analysis frameworks and models may help you to do this, but strategic insights generated without the aid of such frameworks and models can be just as good as those generated with them.

Analysis generally requires data as an input. In strategy, data usually arrives from one of four sources:
  1. data which is generated within your business, such as operational performance data.
  2. data which is generated in the interaction of your business with the outside world, such as from customer or supplier transactions.
  3. data which is generated by primary research, such as customer surveys and focus groups you conduct.
  4. data originating from secondary research, such as industry wide reports.

Whatever the source of the data, the basic process of analysis is the same.
  1. First the data is studied to see if there are any trends: for example 10 daily sales volumes, each higher than the preceding one, shows an increasing trend in sales data
  2. Then the trends are studied to see if they yield any patterns: for example, sales always increase when the weather gets warmer.
  3. Then we attempt to deduce the structures supporting those patterns: customers buy more ice-cream when it is warmer.
  4. And finally we attempt to derive theories explaining what we see: customer buy ice-cream when they are hot because it cools them down.
It is those theories that drive insight. (For example, if we can sell both ice-cream and hot chocolate, our stores will be busy whether the weather is hot or cold. Or, ice-creams compete with soft drinks, not just with other ice-creams.)

As humans we are conditioned to this process and do it so naturally that we don't always notice we're doing it. Unfortunately, we are also fallible and can jump to conclusions without properly considering all of the data. The 'scientific method' is useful in this context. By actively seeking out to disprove our theories, we increase our confidence that the ones we can't disprove may actually be correct.

Analysis is an art as much as it is a science. Some people seem to have a knack for looking at data in different ways which more effectively unlock its secrets.

There are also people who think intuition trumps analysis. They argue that analysis is cold and clinical and doesn't always take into account the whole picture and the subtle clues. I am less convinced. I think that intuition is a form of analysis - its just analysis of the experience data that resides in our brains rather than coded on spreadsheets and in databases.

Big data is having a profound effect on the art of strategic analysis. With more and more data about wider and wider ranges and types of human behaviours increasingly available in computer systems, our ability to derive strategic insight from coded data is better than ever before. See, for example, More data usually beats better algorythms. Of course, the availability of this data increases rather than reduces the burden of analysing it for insight.