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Wednesday, December 08, 2004Tim Bray has come with an
interesting methodology for identifying technology winneres.. Excerpts( with edits and my views added::
The success matrix for candidate Technology Success Predictors.
Management support Some technologies arrive to the sound of loud management cheers; others in the geeks’ hip pockets and are actually in production before the boss has noticed what’s going on. Does arrival through the front vs. the back door make a big difference?
Investor support: There are a lot of people who make their living by investing in technology companies, most visibly the Venture Capital community, and the aggregate numbers are big. Is a heavy flow of investment dollars an important predictive success factor?
Standardization process : Some technologies are invented and controlled by a single company or even a single person; others are the fruit of leisurely, transparent, consensus-driven, standards processes. Is standardization (or the lack of it) a useful tool in judging the new?
Technical elegance: Some technology we use every day is a tissue of kludge upon kludge upon kludge. Others are built around work which won the Turing prize and is, in an engineering sense, beautiful. Should elegance factor into the predictive process?
Apparent ROI: In the sales trenches—particularly in these tough times—nothing succeeds unless you can paint a compelling Return On Investment picture. I.e., deploying this technology will increase revenue, or cut costs, or increase customer satisfaction, or prevent Eliot Spitzer from suing your ass. So when you’re pondering new tech arrivals, is the potential ROI a key factor?
80/20 point :Some technologies try really hard to solve the whole problem; others do the bare minimum possible to achieve some useful effect. This is related to, but not the same as, the ongoing debate about “Worse is Better.” So is hitting the 80/20 point (eighty percent of the benefit for twenty percent of the effort) a useful success predictor?
Compelling idea:The neologism “meme” is useful in this context; some ideas are so powerful and eye-opening that when you first get them, they infect you and you want to tell everyone. Others take a lot of work first to understand at all, and then to understand why anyone would care. Is this distinction useful in the prediction proces?
Happy programmers : Programmers are eager to use some technologies, and others have to be forced down their throats. But in the business big picture, programmers aren’t, after all, very important people. So should a technology prognosticator take the trouble to find out what the programmers like?
Good implementations : Some technologies arrive as pure idea-ware, others as running code that you can buy or download and be using by this time tomorrow; and when there’s running code, sometimes it’s good and sometimes it’s well, we’ve all been there. Does this availability and quality really matter that much in predicting a new technology’s adoption? With so many technologies and enterprises getting started with every passing day, analysis like this is a very handy aid indeed. |
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