Last weekend I refereed three youth soccer matches, and had a great time. I can no longer play soccer as much as I would like (broken ankle from playing men’s indoor soccer about 10 years ago) but I can referee youth soccer which is as close to playing as you can get – literally. But the last game of the weekend reminded me of the process of producing a magic quadrant so much so that I had to write about!
A magic quadrant, despite its name, is a whole less magic than its name suggests. A lot of work goes into producing an MQ. I just published on, an update to the Magic Quadrant for Master Data Management of Product Data, and my colleague John Radcliffe just published his update to the Magic Quadrant for Master Data Management of Customer Data. Here is a quick summary of what I did:
- Initial review of the market, its current dynamics, trends – that lead to an hypothesis, based on interactions with users over the preceding 12 months. This view or hypothesis is to be tested with the rest of the research process, and would be the basis for the Magic Quadrant note
- Vendor briefings with vendors in the MQ, and a whole lot of other vendors, that are not in the MQ, in order to get a wide view of what is happening from the vendor perspective
- 170 individual interactions with end users, via reference calls or survey feedback
- Pretty straight forward statistical analysis of the survey feedback; with some subjective analysis of additional data provided by users
- Building of the Magic Quadrant model, which is spreadsheet based, for each vendor individually, then for vendors in relation to each other, then at the market level
- Initial “sniff test” and then detailed consistency review of the analysis
- Revision to hypothesis, and revisions to the market overview
- Detailed write up for those vendors in the Magic Quadrant
- Peer review at Gartner spanning direct feedback in research community presentations, as well as detailed line by line and dot by dot feedback in our editing software
- Final “sniff test”
- Vendor “factual” review – to make sure no erroneous data or comments are included
- Editing, publication
So it is not surprising that analysts look rather tired and a little older once an MQ is published. This takes me back to my soccer refereeing last weekend.
Before a soccer match starts, both sets of parents are neutral – even positive toward the referees. In fact everyone is very polite period. Both sets of parents go out of their way to be nice to everyone. But when that whistle blows, all bets are off. As the first half of the game ends, it is very likely that one set of parents “love you” and the parents of the other team “hate you”. This is a reflection in how the decisions went during the game. Some decisions will be seen to favor one team, and negatively hurt the other. During the second half the referees have every chance to now upset the previously happy parents with more decisions that less understood by the parents. So by the end of the game everyone is upset with the referees. This cycle repeats itself over and over, around the country (around the world!) every day, every weekend. And its just like an MQ.
In this case, the sets of parents that represent the youth teams are vendors. When the MQ process starts they are neutral – and they are very helpful. As the MQ process concludes, it’s likely every vendor is upset at something. Each vendor sees the market through their own, private lens – much as parents see the soccer match. As with MQs, most vendors only have one or two lenses at most, referees and analysts get to see a whole lot more of the game and market – so are more experienced at looking at the whole.
The end of the game however is different. At the soccer match you go home without any new friends. With an MD, at least the end users, the primary consumer of the Magic Quadrant research, are very thankful for the work we do. I just have to wonder what we do, as analysts, with the yellow and red cards we are so familiar with in soccer refereeing!