Big Data: The Crawfish Approach to Meaningful Information
March 21, 2017
Have you ever watched a crawfish (sometimes called a crawdad or a crayfish) get away from trouble. The freshwater crustaceans can go backwards. Members of the members of the Astacidae can be found in parts of the south, so you will have to wander in a Georgia swamp to check out the creature’s behavior.
The point is that crawfish go backwards to protect themselves and achieve their tiny lobster like goals. Big time consultants also crawfish in order to sell more work and provide “enhanced” insight into a thorny business or technical problem other consultants have created.
To see this in action, navigate to “The Conundrum of Big Data.” A super consultant explains that Big Data is not exactly the home run, silver bullet, or magic potion some lesser consultants said Big Data would be. I learned:
Despite two decades of intensive IT investment in data [mining] applications, recent studies show that companies continue to have trouble identifying metrics that can predict and explain performance results and/or improve operations. Data mining, the process of identifying patterns and structures in the data, has clear potential to identify prescriptions for success but its wide implementation fails systematically. Companies tend to deploy ‘unsupervised-learning’ algorithms in pursuit of predictive metrics, but this automated [black box] approach results in linking multiple low-information metrics in theories that turn out to be improbably complex.
Big surprise. For folks who are not trained in the nuts and bolts of data analysis and semi fancy math, Big Data is a giant vacuum cleaner for money. The cash has to pay for “experts,” plumbing, software, and more humans. The outputs are often fuzzy wuzzy probabilities which more “wizards” interpret. Think of a Greek religious authority looking at the ancient equivalent of road kill.
The write up cites the fizzle that was Google Flu Trends. Cough. Cough. But even that sneeze could be fixed with artificial intelligence. Yep, when smart humans make mistakes, send in smart software. That will work.
In my opinion, the highlight of the write up was this passage:
When it comes to data, size isn’t everything because big data on their own cannot just solve the problem of ‘insight’ (i.e. inferring what is going on). The true enablers are the data-scientists and statisticians who have been obsessed for more than two centuries to understand the world through data and what traps lie in wait during this exercise. In the world of analytics (AaaS), it is agility (using science, investigative skills, appropriate technology), trust (to solve the client’s real business problems and build collateral), and ‘know-how’ (to extract intelligence hidden in the data) that are the prime ‘assets’ for competing, not the size of the data. Big data are certainly here but big insights have yet to arrive.
Yes. More consulting is needed to make those payoffs arrive. But first, hire more advisers. What could possibly go wrong? Cough. Sneeze. One goes forwards with Big Data by going backwards for more analysis.
Stephen E Arnold, March 21, 2017
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