Analytics, whatever that means, has emerged as the hot topic all over our industry... According to the conventional wisdom, very special experts, quants we'll call them, are needed because mere mortals can't handle this stuff... But I don't buy this.
Analytics, whatever that means, has emerged as the hot topic all over our industry. Gartner seems to have bolted from Business Intelligence and placed "Advanced Analytics" in the firmament of must-have technologies for 2010 (I guess everyone followed their lead and implemented BI last year so there is nothing else to talk about). The problem with analytics is, who can do it? Numerate people in organizations are as scarce as hen's teeth. According to the conventional wisdom, very special experts, quants we'll call them, are needed because mere mortals can't handle this stuff. But you can't buy quants like muskmelons on the road to Bettendorf in July, and even if you could, a muskmelon would be less troublesome.One of the often-cited problems with quants is that they tend to be condescending towards those who lack quantitative skills. This is the reason Tom Davenport often uses the term "PhDs with personalities," a phrase I find quite distasteful. However, most people's experience with quants leaves them yearning for someone who speaks in a common language and doesn't get impatient when asked stupid questions.
But I don't buy this. Quants don't act any differently than other highly trained professionals who have to repeatedly explain themselves to those who have no background in the subject and show no initiative in acquiring it. IT people for instance. Sound familiar?
In addition, why all this talk about PhDs anyway? There is nothing so special about most "advanced analytics" that someone with adequate training could not do. Actuaries are a good example. Most actuaries can (and do) apply very complicated quantitative methods to their work, yet very few have a PhD. A bachelors or masters degree in math, physics, biology -- anything that took them through, say, four semesters of calculus, plus linear algebra, differential equations and real analysis. These subjects are table stakes for almost any career in math, engineering or the sciences. I didn't mention probability and statistics, because I was an actuary and did not take those courses in college. I, like many actuaries, learned those subjects and passed the exams through self-study over a number of years. Insurance companies give actuaries study time (one to two hours/day) for the three to five years it takes to pass all the exams.
Why not do the same for quants? The actuarial exams are pretty brutal, but there is no reason for a quantitative expert course of study to emulate that. I'd urge companies to set up such a plan with an eye to ripening experts over a two- to three-year period with time at work to study. Hire promising people out of college with the right background, and allow them to grow into the role of a rocket scientist super quant.
One more thing: When you get a PhD in math or statistics, you get a research degree, not a clinical practice degree. That means, in order to get it, you have to spend some years on original research and defend it. The keyword is original. By the time someone gets a PhD, their field of interest is so narrow, they probably have no better grasp of the types of analytics that a commercial company needs than a BA/BS math major with adequate training. The real mental challenge is in developing new algorithms and models, which is where PhDs belong. Building a pricing model using multiple regression or Bayesian analysis is not. This PhD stumbling block is a result of collective fear of math -- it's hard so let's get the smartest people on it. But the smartest people may be standing right in front you.Analytics, whatever that means, has emerged as the hot topic all over our industry... According to the conventional wisdom, very special experts, quants we'll call them, are needed because mere mortals can't handle this stuff... But I don't buy this.
The Agile ArchiveWhen it comes to managing data, donít look at backup and archiving systems as burdens and cost centers. A well-designed archive can enhance data protection and restores, ease search and e-discovery efforts, and save money by intelligently moving data from expensive primary storage systems.
2014 Analytics, BI, and Information Management SurveyITís tried for years to simplify data analytics and business intelligence efforts. Have visual analysis tools and Hadoop and NoSQL databases helped? Respondents to our 2014 InformationWeek Analytics, Business Intelligence, and Information Management Survey have a mixed outlook.
InformationWeek Tech Digest, Nov. 10, 2014Just 30% of respondents to our new survey say their companies are very or extremely effective at identifying critical data and analyzing it to make decisions, down from 42% in 2013. What gives?