So, how do we get our hands around all that data? And once we have a conclusion, how do we present it effectively?
Slice up the data. Then start over from the raw data and dice it an entirely different way. Repeat.
The point is to look at the data from several angles to gain comprehension. Let's take the product comparison as an angle. It was a pretty straightforward project: We need a CMS. Which one should we use? There are literally hundreds of CMSs in this world, and a whole lot of information available about most of them. Let's commence data analysis:
- Slice. Make a feature comparison table, showing which ones have the features we're interested in.
- Dice. Map out the release frequency of modules, showing how quickly these introduce new features (as a proxy for future feature development pace).
- Slice. Figure out the annual cost of each CMS.
- Dice. Describe the community around each CMS, including number of job postings/candidates, presence and activity in forums or other documentation, and breakdown of current users in our industry.
Etc. etc. etc. You get the point. The idea is to take the data available and look at it from several different angles. Each angle will point up some CMS that is more appropriate than others. By starting over each time, you avoid bias based on your earlier analysis. Collectively, the slicing and dicing produces a small list of really good candidates, and provides comprehensive grounds for argument.
So often we deal with large amounts of data, and it's easy to get overwhelmed and make a choice based on just a few data points and a rough guess. Take the time to cut through the data in a few ways, though, and your choice will not only be more obvious, it'll also be a better decision.