A few previous tweets:
>> Today's theme: financialisation. Not really a word, made it up to describe the trend of running businesses as if they were hedge funds.
>> Financialisation: #1 benchmark industry for advanced analytics is (still) the financial industry & financial markets. Is this a good sign?
>> Financialisation: For me Enron is the base case of financialising the energy business. There was deceit but at core it was models run amok.
>> Financialisation: CEP, Real-time BI, etc: Can you filter signal from noise in real time? Processing delay may prevent overreactions.
Financialisation; a term that I invented (AFAIK) to describe running 'normal' businesses like they're hedge funds. E.g. the use of statistics / 'quantitative models' to wring every bit of excess / waste / inefficiency from a business. Traders on the financial markets attempt to profit from small price movements by developing complex predictive models and the ability to move on changes very, very quickly. The problem is that financial markets are not like normal businesses. They are probably more like a casino than a business that provides tangible goods or services (e.g. dentists, dry cleaners, demolition, design, etc.).
Many business executives seem envious of this apparent ability to turn thin air into money using leverage and very fast moving transactions. One suspects they would love to turn their own business so quickly and, perhaps, avoid messy interactions with opinionated customers. Business Intelligence and Analytic Database companies haven't failed to notice this desire and heavily market their reference customers from Finance in other sectors.
My thoughts on this are influenced by Nicholas Nassim Taleb's books "Fooled by Randomness" and "The Black Swan". His premise is that the world is much more random and much less predictable than it appears to human observers and events often come out of left field to completely upset our ideas (hence the black swan). Taleb never mentions Business Intelligence or Analytics, but I'm struck by the relevance of his ideas to our industry.
On the other side of the fence; Thomas H. Davenport's "Competing on Analytics" is the standard bearer for financialisation and a favourite handout of BI vendors (e.g. Oracle, Microsoft). The choice quote: "Employees hired for their expertise with numbers …are armed with the best evidence… As a result, they make the best decisions." Really? Simply applying the power of numbers to a business, using very clever people of course, is a sure fire way to success? Does that mesh with your experience?
Consider the case of Enron. They were principally involved in energy supply, which has a very real need to analyse and forecast future demand. Enron got into trouble by using models (and modellers) to make highly leveraged plays on the energy futures market. Ultimately their crimes were about deceit (they used shell companies to inflate profits and conceal losses); however, it is my understanding that their losses stemmed from deals based on very sophisticated models that did not turn out as predicted.
A counter example very relevant to Business Intelligence is disaster recovery. It's common practice in IT to run a disaster recovery copy of important systems. We keep an exact duplicate of the system in another data centre far from the primary system so that, if the worst happens, business can carry on by switching to the DR instance. This is inherently excess capacity that bears significant costs and yet we hope it will never be used. We carry the cost of all this "excess" equipment for a very good reason; the cost of not having it is potentially much, much higher.
This sums up the risk of fincialisation: Can you be certain that the what looks like excess (on the cost side) is not actually very important? Can you be sure that what looks like new profit (on the opportunity side) is not exposing you to a large unexpected loss?
{This is where I was going to go over some common types of analysis and discuss whether they are more or less likely financialised. But this has been in draft for long enough so that will have to wait. TTFN}
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