, ,

Pigs_eating_pumpkins.jpgMartyn Richard Jones

Dublin 10th May 2017

Throw AI, Big Data and Data Science into a pot, and what have you got?

Yes. A pig’s breakfast, not fit for a pig.

Let me explain

Inevitably, some people are admitting the obvious. Big Data is failing.

Failing to live up to expectations.

Failing to deliver new insights that drive business actions.

Failing to deliver unique and tangible value.

So, does that mean the death of Big Data?

Well, no. Far too much has been invested in the wall-to-wall astroturfing of hype that has come to represent much of the Big Data circus.

According to some, Artificial intelligence is the new saviour of Big Data, and Big Data is the new young-blood of Artificial Intelligence. Both will walk down the yellow brick road of happiness, hand in hand with data science. Thus reaching the continuously optimising virtuous circle of ultimate data-driven magnificence that some have dreamt about.

The amateur hour of recriminations

Let’s face it. Problems, there are lots of them. But, the problem is never the technology, techniques or technicians, or the management of any of these. The problem is always the companies. Companies fail to turn data into insights. Companies fail to turn insights into action. Companies fail to execute. It’s all the fault of business. Big Data and AI, they are the smart guys. The dummies are the execs.

Big Data failures, like Artificial Intelligence failures, or other IT centric failures, have never had anything to do with the end-result of pushing Big Data and AI down the gullible throats of business executives. Like as if it were some type of must-have universal panacea, that if you don’t take soon and frequently, will result in the inevitable decline and demise of your business and the public humiliation of you, your friends and your family.

It matters not one jot that no one asked the question ‘and to what ends?’ The dummies are always the business folk.

Both AI and Big Data are buoyed up by a surfeit of technical prevaricators, semiotic charlatans and amateur plumbers, all incapable of delivering small advances, incremental improvements or tangible business value.

So, we have AI people complaining that their apps don’t work properly because they aren’t getting the right data or the business requirements weren’t correct or it just needs a paradigm shift. We have Big Data people complaining their ‘apps’ don’t work because the data cleansing tools are not up to the job, the maturity and capability of the organisation is all wrong, or the investment in hardware and software does not match expectations. We have data analytics people complaining about the wrong data, difficult equations and the density and opaqueness of machine learning. We have one group vying against another group – separately useless and collectively pernicious. All with the idea that if they all did the right thing, a miracle would be performed and the world would be saved.

Which, as we all should know, is a load of old nonsense.

Why do they do it

It’s quite simple.

Unqualified people need jobs too.

If big data practitioners had a solid grounding in data management, they would not come up with anything like the humongous amounts of absolutely bizarre, unscientific and baseless claims about big data. This is not a criticism but an observation, but most big data practitioners I bump into seem to be about as qualified in data management as the PG Tips chimps were qualified to carry out plumbing.

If data scientists were anything more than just glorified data lab assistants, they would stop complaining about the tools and the data and get on with the job. They would make it clear what was required in terms of data, pass that requirement on to the data management professionals, and get down to the task of applying their core statistical and analytics knowledge to their ‘tasks at hand’. So, learn the lesson. A data scientist who isn’t formally well-versed in statistics, is worse than useless. Learn from the Simpsons. Get a pro, not a ‘doh!’

There’s an old Mel Brooks sketch that reminds me of the current world of Big Data, AI and Data Science. Carl Reiner (a journalist in the sketch) is interviewing Mel Brooks, who although he is on foot patrol of a military facility, claims to be an astronaut. When Reiner comments that Brooks seems ill-equipped to be an astronaut, Brooks’ indignation is palpable “What do you mean ill-equipped? I have the gloves and the hat and everything!”

That’s it folks

So, bottom line time.

AI is not going to save Big Data.

Big Data is not going to save AI.

Big Data is a small area of interest and a vast ocean of bullshit.

AI is a vast area of interest, with some potential.

However, AI is in the process of being totally discredited with stupid marketing pitches and even stupider claims from ‘industry evangelists’. There are small, discrete and well-bound business problems that AI does well at, but all of this machine learning flim-flam really needs to stop.

AI technology is still very immature, even the least immature aspects of it. So, let’s send it back to the universities and colleges of the world, where it still belongs, for another couple of decades, at least – and fund AI research well.

That stated. If we don’t get a grip on the Big Data and AI hysteria we are going to end up with a massive collection of incomprehensible silos that will make the Tower of Babel seem like the pinnacle of fluid intercommunication and insight.

Just saying.

Many thanks for reading.


As always, please share your questions, views and criticisms on this piece using the comment box below. I frequently write about strategy, organisational, leadership and information technology topics, trends and tendencies. You are more than welcome to keep up with my posts by clicking the ‘Follow’ link and perhaps even send me a LinkedIn invite. Also, feel free to connect via Twitter and Facebook .

For more on this and other topics, check out my really old posts:

© 2017 Martyn Richard Jones