“When you are IT systems managers in a dangerous time…”
Kiril’s kickoff speech this morning started out restating the drivers. Without good IT systems, process and people management, you cannot effectively:
Make use of your business flexibility to meet changing market conditions
Not to mention that being in constant fire-fighting mode and spending >70% of your time as an IT Pro on maintenance is a recipe for staff burnout.
MOM can help IT Pros reduce this time spent I maintenance/reactive mode. They’ve just release a bunch of new MPs. Interestingly, one of them is for mission critical desktops. There is also a new MP that lets you build synthetic transactions you’re your websites to tell you what kind of user experience your visitors are getting.
On the plane down to MMS I read some interesting stuff from Tim Wallace:
“Science is finding that mimicking living systems to produce robots is about understanding biology, not physics. There are lessons here for the way we run our corporations.
FAST, CHEAP and out of control is not the way most of us would conceive the model organisation. Fast and cheap maybe, but out of control – definitely not.
That, surely, would be to reject the very idea of management as a discipline, what Peter Drucker called the “great liberating, pioneering insight” that human work can be studied systematically, analysed and improved through control of its parts. Captains of industry are not, after all, paid salaries hundreds of times that of the average worker to steer rudderless ships.
The phrase “fast, cheap and out of control” was coined by Australian-born scientist Rodney Brooks and a colleague for an article published in 1989 advocating the use of robots in space exploration. AI challenges us to rethink OI (organisational intelligence) and to smash the machine, rebuilding it from the bottom up – fast, cheap and out of control.
What is intelligence? The standard dictionary defines it as the ability to acquire and apply knowledge and skill. We have developed rough measures to evaluate it in creatures, such as humans and dogs, but pinning it down in creations, such as machines or organisations, is harder. An intelligent organisation is clearly something other than the sum of its members.
Enron prided itself on recruiting the brightest and best from America’s top universities but you’d be hard pressed to find a dumber corporate culture, despite it having been generated by a gang of MBAs.
Defining intelligence in machines is even harder.
How the living system computes has been a driving metaphor in scientific research, Brooks writes. “I am reminded that, early on, the nervous system was thought to be a hydraulic system, and later a steam engine. When I was a child I had a book that told me the brain was a telephone-switching network. By the 1960s children’s books were saying that the brain was like a digital computer, and then it became a massively parallel-distributed computer.
I have not seen one, but I would not be surprised to see a children’s book today that said the brain was like the World Wide Web with all its cross-references and correlations. It seems unlikely that we have gotten the metaphor right yet.”
Machine metaphors that are not quite right are all around. Mainstream economics, for example, is based on classic Newtonian physics; the universe works in predictable clockwork fashion: turn one cog in the machine and consequences occur, all others things being equal – which, of course, they never are.
In management, the combined legacy of Frederick Winslow Taylor and Henry Ford gave rise to an entire philosophy of business based on the metaphor of the machine: labour rationalised through mechanisation; work divided and specialised; brainwork centralised at the top; tiers of management controlling production by process, method and textbook.
But advances in human understanding in many areas are showing the deficiencies of thinking about living systems in terms of machines. New schools of thought rooted in evolutionary biology are emerging to rattle the shaky assumptions of the industrial age, seeking to discern the real lessons of living systems rather than being led astray by the metaphors we are inclined to impose upon them.
Consider the beehive. To most of us it is a model of efficient hierarchical enterprise – of drones, workers and, above them all, the queen. The hive teaches us an important but subtle lesson in corporate governance: it is autocratic and democratic. Relocation of the hive, for example, is a bottom-up decision.
When bees prepare to swarm to a new location, they send out scouts, then take an “electronic” vote (there’s another machine metaphor) about which scout to follow. The queen is ostensibly in charge but is by no means a micro-manager, with many big decisions made by consensus.”