When is going onPosted: February 25, 2015
February 25, 2015
When doing your CI research, and the follow-on analysis, you are trying to determine “What is/was going on?” That means assembling, physically or mentally, the data by topic or target, and then reviewing it to determine what it all means. And that is fine. In fact, there are a wide variety of analytical tools available for that (for books that can help, visit my page “What you should read to learn more about competitive intelligence”).
However, sometimes you have to approach it differently. In fact, I advocate always considering doing that. By this, I mean generating a sense of time as well as of topic. You can do that in at least three different ways:
- Arrange your data by the date it happened. That is, for example, place data about the building of a factory at the time it was done, not at the time it was reported or otherwise disclosed. Now you can read backward and forward (I recommend doing it both ways) to see what developed, how quickly and (perhaps) why.
- If you have a complex piece of analysis, consider constructing a separate time grid, noting highlights of your research (considering hyperlinking to source materials), even dividing it into parallel categories such as “executive changes”, “acquisitions and divestitures”, and “capital changes”. Now you can refer to it and see quickly connect a change in a building program to management changes as well as spot what is missing – for example, when was that second factory actually refurbished?
- Arrange your data by the date that it first became public (or otherwise available). Here you can, metaphorically speaking, understand how the data came to be available. Now ask, why did some data come out later than did other data? It may also enable you to spot sources for missing data.
What you are doing is shifting from seeking to understand merely “what” is going on to “when” it was going on, by resorting and re-reviewing the data; you are also able to extract more insights and more quickly identify missing data points by looking at your data from several different perspectives.