The first 2013 edition of
The New York Times carried two articles that dealt with a key aspect of agile | adaptive strategies: how to incorporate what you know or don’t know about the future into your business strategy.
The first article titled “Outmaneuvered at Their Own Game, Antivirus Makers Struggle to Adapt” wrote about the fact that antivirus software is often not very good at stopping virus and “these programs rarely, if ever, block freshly minted computer viruses”.
Adapting to their proven inability to detect future viruses, the term antivirus is being dropped from product names and some players in the industry are moving from trying to defeat future virus to identifying and preventing unusual software behaviour (often through the use of whitelisting or isolating suspicious activity) or cleaning up breached software systems.
Acknowledging their inability to predict the future, the industry’s answer has been to adapt to reality and “turn the whole notion of security on its head.”
The second piece that caught my attention was a column by Steve Lohr titled “Sure,
Big Data Is Great. But So Is Intuition”.
Essentially, Lohr argued that whilst many technologists, businessmen and forecasters are getting excited by the potential of Big Data to predict useful, and therefore profitable, trends, the algorithms identifying the trends are too simple-minded.
“The problem is that a math model, like a metaphor, is a simplification. This type of modelling came out of the sciences, where the behaviour of particles in a fluid, for example, is predictable according to the laws of physics.
“In so many Big Data applications, a math model attaches a crisp number to human behaviour, interests and preferences…” This focused simplification, as readers of
Nassim Nicholas Taleb’s book
The Black Swan will know, effectively shields us from considering the complexity and unknown from where the most far-reaching changes emerge.
Lohr doesn’t argue that Big Data should be ignored, and neither do I. Quoting leaders in the field of data analysis he writes that the asking the right questions that the data must answer and comparing the answers to reality is important.
He adds “Listening to the data is important...but so is experience and intuition. After all, what is intuition at its best but large amounts of data filtered through a human brain rather than a math model?”
The first article dealt the adaption of an industry to the realization that it was increasingly bad at predicting future events, the second warns against focused simplification of predicting : both are key to agile | adaptive strategies.