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Big Data, Machine Learning and Innovation

New terminology doesn't imply new technology

Big Data and its most prominent technical ingredient, Machine Learning, are all the rage these days, as IT industry is trying to convince companies technology revolution is underway. ( "If you are not doing it, your competitors sure are, and by the time you realize it, it will be too late" ). Data fracking, i.e. Big Data, is 21 century new oil of that will power and grease stalled industries and reignite growth, or so the story goes.

While advanced analytics (it comes under various names - predictive analytics, data mining, and data science, more recently) is great and in use for decades now, we are well reminded that it is just - analytics. As such, it is a natural next step beyond straightforward SQL queries, but falls short of being anything near revolutionary. We are well advised to listen to the words of Larry Ellison (on IT industry in general and cloud in particular, but applicable to many new IT initiatives):

Our industry (IT - RM) is so bizarre.They change the term and they think they've invented technology.
Let's call that cloud, it sure beats innovation
. (For example) Chanel - last year  was  Fuchsia, this year it is Complice.
(Ellison draws parallel between IT and fashion industries - RM )

In Machine Learning, for example,  linear/nonlinear data fitting regression using gradient descent method  (which is hyped and sold  as a new, powerful method ) is just decades old iterative method, often implemented in Fortran in 80's. All that machine learning does ( in data processing field ) is to find curve that best fits available data ( or find data patterns ).  While understanding and using these methods assumes good knowledge of mathematics and computers, it is nowhere near rocket science level that it is hyped to be. Since it is presumably impossible to find this superhuman data scientist, we hear suggestions that companies should create FrankenTeams comprised of computer programmers, statisticians, machine learning experts, database administrators, Hadoop evangelists etc., that are all together supposed to approximate a single unicorn "data scientist".

Consequently we are witnessing companies trying to dig out middlebrow, stuttering,  psychology, sociology PhDs  from their academic caves . They come out to daylight, blinking, attracted by this new gold rush, having no clue what business data processing and corporate environment is. Companies are supposed to turn them into "data scientists" or "business scientists," only because they have knowledge of advanced statistical methods. That approach will not only not work, it is also incredibly and unnecessarily wasteful.

It should also be understood that each model has inherent limitations as it is approximation of reality and essentially an optimization, which by definition never perfectly describes reality. Past history doesn't necessarily project into future, since model sometimes fails to catch black swan events or outliers of disproportional weight.

That said, we are all in for bringing advanced analytics into mainstream, but no unrealistic expectations, please.

More Stories By Ranko Mosic

Ranko Mosic, BScEng, is specializing in Big Data/Data Architecture consulting services ( database/data architecture, machine learning ). His clients are in finance, retail, telecommunications industries. Ranko is welcoming inquiries about his availability for consulting engagements and can be reached at 408-757-0053 or [email protected]

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