Once upon a time, information technology was strictly about — well, information. And by “information” what was meant was “data”.* An application boiled down to a database design, plus a straightforward user interface, in whatever the best UI technology of the day happened to be. Things rarely worked quite as smoothly as the design-database/press-button/generate-UI propaganda would have one believe, but database design was clearly at the center of application invention.
*Not coincidentally, two of the oldest names for “IT” were data processing and management information systems.
Eventually, there came to be three views of the essence of IT:
- Data – i.e., the traditional view, still exemplified by IBM and Oracle.
- People empowerment — i.e., Microsoft-style emphasis on UI friendliness and efficiency.
- Operational workflow — i.e., SAP-style emphasis on actual business processes.
Graphical user interfaces were a major enabling technology for that evolution. Equally important, relational databases made some difficult problems easy(ier), freeing application designers to pursue more advanced functionality.
Based on further technical evolution, specifically in analytic and consumer technologies, I think we should now take that list up to five. The new members I propose are:
- Investigative analytics.
- Emotional response.
I may want to rename that last one someday, but “emotional response” will serve well enough for now.
At first blush, it might seem that investigative analytics could be regarded as straightforward database processing, perhaps based on some analytics-friendly data structure; but that’s not true today, and I can’t pinpoint a past era when I think it was true either. Defining and structuring the data is just a starting point, and is not tantamount to saying what will be done with it.* For example, exactly the same data mart might be used for:
- Conventional business intelligence — reporting, query, charting and drill-down (“multi-dimensional” or otherwise).
- Similar activities, but with a QlikView or Endeca kind of spin.
- Statistics and machine learning.
- Graphical analysis.
Perhaps the best example of an investigative-analytics-oriented company is Google, where nothing is acknowledged as true until it’s been statistically verified, and whose core service is the world’s most flexible research tool.
*True, in some cases the introduction of derived/cooked data could lead the data structure to evolve in an application-specific way. But I don’t think that seriously undermines the basic point.
Meanwhile, as Ray Wang likes to point out, a large fraction of current IT innovation is consumer-centric. I’m not sure I’d grant all his business-to-business sub-points to that claim, but it surely is true that:
- Consumer-oriented technology is much more sophisticated than it used to be.
- A large fraction of what’s done in information technology is consumer- or at least customer-facing.
The key defining trait of consumer/customer (as opposed to employee) computer use is that it is optional; your customers don’t have to do business with you, or use the applications and interfaces you offer. Consequently, it’s your responsibility to make sure that what you ask them to do is convenient and engaging (or fun, or at least not-annoying). And so, much more than before, you need to be concerned about users’ emotional reactions to computing systems. Personalization fits in with both the investigative analytics and emotional-engagement themes. Investigative analytics is why you can personalize people’s experiences (web surfing, shopping, gaming, etc.); emotional response is why you must. Companies I’d put in the “emotional” camp include Facebook, whose central concept is “friend”; Zynga, whose customer-retention strategy seemingly boils down to addiction; and — back in its heyday — AOL.