You get the feeling that business analytics might really have gone mainstream when you’re reading Tom Davenport in an in-flight magazine over Ohio. And there he is, on page 15, in the seatback pocket of every chair at 28,000 feet on the way from Boston to Minneapolis.
Actually, Davenport (of Babson College and “Competing on Analytics” fame) has been doing some collaborative work lately with Harvard Business Review, which currently posts a regular feature in the U.S. Airways magazine. His current short article, “Helping Organizations Develop Better Judgment,” talks about organizational judgment being a different animal than the sum of the individuals who judge.
Davenport points up the shift of blame from leaders – generals, politicians, CEOs etc. – to whole organizations.
“No longer [is] one desk where the buck stops, it is now the entire organization. For the past several decades we have witnessed a slow but steady evolution in the very concept of leadership, from sole heroic visionary and master delegator to a team-based and even democratic organizational model. At the same time, we recognize how many inputs judgment can have – from data-based analytics to crowdsourced predictions to impressionistic insights."
While bucks do indeed continue to stop – managers and generals and CEOs still get fired after all – I get his point, or think I do. Data is a powerful and pervasive resource we are increasingly and aggressively assembling, organizing and evaluating en masse from different points of view.
There are side effects to this phenomenon. While analysts always say, ‘the more data, the better, the analysis,’ the volumes and sources available today make it more possible to pry any number of interpretations from the ripe corpse of data before us.
Another view is that we’re seeking validation (or recrimination) for behavior from all of our organizational inputs at once, as if they can be assembled like an organism to prod and research. Where decisions and outcomes are bad or good by degrees, we add ourselves as data points for judging paths taken, paths not taken, paths run or stumbled down. As the consumers and producers of this data, we’re by degrees empirical observers or opinionated pundits in a running series of micro-judgments.
I think I finally understand Twitter.
Where a buck does stop and a reward or penalty is paid, there only more data to devour and recycle. Maybe it helps explain why political opinions have grown so binary, or why we judge leaders on their personal as much as their professional lives.
Another article in today’s paper talked about record levels of media consumption and time lost for simple observation and interaction. We’ve already seen evidence that people on the whole talk faster and listen less to one another.
The two-sided nature of this, the ability to be a speck of groupthink and a granular analyst consumer all at the same time, feels new. I was at a dinner with some smart consultants and a bunch of CIOs last night and one of the consultants said he’d noticed in implementations that, “it’s becoming less about the technology and more about the consumption model.”
That really is something to think more deeply about while we slave over data integration or master data management, because all that hard labor could conceivably end up as just more backbone to pick over, validate or dismiss, correctly, incorrectly or without certainty.
I hope not but as “professional” information consumption goes mainstream, massive data visibility will bring its own dissonance. Things like data governance were never capable or designed to fix that.
At the risk of getting flamed (not for the first time over existential data dualism) it does make you wonder if we’re not already both the cause and the result of the information transformation taking place.
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