According to recent research from Accenture, nearly half (40 percent) of major corporate decisions are based on the good 'ole gut.Interesting. But why?
61 percent said it was because good data was not available, and just over half (55 percent) said their decisions relied on qualitative and subjective factors.More interesting. Of course it could easily be argued that even bad data is better than nothing, especially if you can make some assessment of how bad it might be or at least understand its limitations. And the second one should worry CEOs and boards across the country - "qualitative and subjective factors". These are often illegal - think insurance or banking where decisions about pricing or risk may not be based on these kind of factors - and always influenced by the underlying biases of the decision maker.
But then we get to what I makes this interesting to me (as a writer on decision management):
Other reasons related to workforce challenges: 23 percent of respondents said "insufficient quantitative skills in employees" were a main impediment at their company, and 36 percent said their company "faces a shortage of analytical talent."These two are, frankly, only a problem if you think data is for helping an individual make a decision (decision support) and nothing else. If, as I do, you believe that data can also be used to automate and manage decisions (decision management) then these problems fade into the background.
If the system tells the user what decision to make or even what 2 or 3 choices are valid, appropriate, legal and potentially profitable then the user does not need quantitative skills. The user just needs to be able to read and then use information. Call center representative should not be required to have quantitative skills to use customer data to make better retention decisions - they should be required to have people skills to make the customer feel good about the targeted retention offer the system suggests (that is based on policies, regulations and analytics).
Using data to build decision management systems means that the users don't need to be quants. You just need some folks with quant skills to put the right models into your operational systems. This means that the analytical talent you do have is immediately multiplied. Your analytic team build a predictive analytic model, to predict customer churn for example, and that model gets embedded in a decision service that delivers customer retention offers. All your call center representatives now act based on an analytically-enhanced decision without having to have any analytical skills themselves.
It was reassuring that
Two-thirds surveyed recognize their decision-making failings and want to fix themThough it was a pity that they thought more of the same would do so:
nearly three-quarters (72 percent) of the Accenture survey respondents say they are striving to increase their organization's business analytics and BI use.Someone once said that the definition of insanity was to do the same thing the same way and expect a different result. All these companies have spent a ton of money on BI without changing their decision making. Perhaps they should try something new....
Posted January 31, 2009 5:32 PM
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