Archive for big data

“Human + Machine: Reimagining Work in the Age of AI” – book by Paul Daugherty and H. James Wilson (book review)

Machine learning and artificial intelligence are becoming really hot topics. Take Europe companies, for example – it is enough to through in the discussion the “digitalization” and the “machine learning” topics and you will get the CEO’s attention. But as in most mini-industrial revolutions, the devil hides into the details. And the very same companies that push for transforming themselves are having a difficult time to perform the transformation itself. This is because of multiple reasons – not the smallest being a certain lack of knowledge ramp up during the current revolution.
“Human + Machine: Reimagining Work in the Age of AI” takes an interesting middle ground approach to the topic. On one hand, it lays down quickly the rules governing this brave new volatile world – data agility. Continue reading ““Human + Machine: Reimagining Work in the Age of AI” – book by Paul Daugherty and H. James Wilson (book review)” »

Book Review – “Prediction Machines – The Simple Economics of Artificial Intelligence”

Artificial intelligence came (no doubt)3 years ago to stay. It started as a corporate craze for big data and last year moved to machine learning. These 2018 days AI goes in production via prediction (my 20 copyright cents please). Ajay Agraval, Joshua Gans and Avi Goldfarb, Toronto based professors, are well placed to write about this, as most of the tech companies spearheading the AI commercialization (led by the Big Tech 4 – Amazon, Google, Microsoft and IBM) are NAM based.
So what is “Prediction Machines” about? Without spoiling the book’s well documented contact, we can simply just underline that AI becomes a long powerful digitalization tool, but not any longer as a facilitator (as was until last year). Agraval forecasts that machines will eliminate uncertainty, thus reducing the market friction forces, thus reducing production and transaction costs. Continue reading “Book Review – “Prediction Machines – The Simple Economics of Artificial Intelligence”” »

Multinational Glitches in the Implementation of GDPR

Admit it, you have done this at least once: you were lured by the special offer / freebie / like into signing up for a newsletter… then you forgot about it. Or you installed an IOS/Android/Windows app which asked you for permissions to share with the Martians your accurate location, food prefferences or place of birth.
The personal data requests have become so ubiquitous, that now it is very hard to trace where it lands. It is then perfectly understandable why EU is seeking to make the various companies responsible for minimal safeguards of such personal data.
If you read the law (or the highlights), you would realize most of the requestgs are quite reasonable – and still theoretical. Fortunately for the consumer (and burdening for the companies), the burden of safeguarding and cleaning up the unnecessary data falls on the collectors. Continue reading “Multinational Glitches in the Implementation of GDPR” »

First Task to Accomplish on Monday Morning

Imagine it is 09:00 AM on a Monday morning. You just landed in your office, supercharged by the Bucks or Republic or another brand coffee. You already had two phone calls with a customer (internal or external). Your task waiting list is longer than the Bible’s first chapter and you do not (want to) know where to begin. So what do you do?
It might look as a joke, but you might start by reading a chapter of a professional book. Research shows that you become 1-2% smarter with every book you read. Your synapses get fired up, your brain starts working and warming up, you get smarter. So why not?

Big Data and Hadoop

Accidentally I came over recently an Apache server technology for big data centralization and analysis. Hadoop is a mix and match technology, open source, which allows companies to write their own big data analytical tools in a quick and efficient manner.

What puzzled me mostly was the adoption of this type of open-source solution. You would think that the serious developers (such as SAS, IBM or Pentaho) would be reluctant to adopt open-source solutions. After all, look at what happened with the Goldman Sachs flash trading code (if you have not read the book “Flashboys”, maybe it is time to give it a try :)). But no, they are embracing it! I do not know if this is because they want a continuously developeable solution, a community pooling benefit or just something off-the-shelves, but here we go. Big data tools are not only very lean, but also based on a global creativity streak. Isn’t this interesting?

Book Review – “Big Data @ Work” by Thomas Davenport

It is quite rare that somebody admits they were wrong about a major trend in IT which was overseen in the past. Quite rare. Fortunately, Thomas Davenport is not that kind of person – on the contrary. In the preface of his new book (“Big Data At Work) published by Harvard Business Review Press, he actually admits that he initially dismissed the concept as being just another technology hype. And you can hardly blame him – there are many gurus or specialists or journalists who still think that the “big data” concept represents another form of selling clound and analytical services. Promoted, of course, by the big IT companies who happen to endorse the concept quite actively.

From this perspective, Harvard Business Review Press has done some justice to the hype surrounding the concept. “Big Data at Work” was in a sense a long waited for book – people were maybe familliar with the concepts, but wanted maybe to know more about:

– how big data is implemented and used by various companies (the famous “case study” approach patented by the Harvard Business Review (one of the biggest business case studies publishers in the world by the way); Continue reading “Book Review – “Big Data @ Work” by Thomas Davenport” »

Is Big Data Just Noise?

Judging by the amount of information available on big data, I would say that paradoxically that the concept is poorly supported. More of a giant with feet of clay. Let us do for example a Google keyword analysis: “big data” search reveals 829,000,000 results in 0.44 secs. (which means that Google has spend some time about it). Apparently a lot.
The problem reveals after you start browsing the pages. The first 10 pages of results show in all cases either definitions and white papers about big data (very vague and fluffy), either selling links. Virtually there was no value added info on the concept itself (except maybe for the Wikipedia article, which is showing some well structured info at the introduction level).
And then we go. All resources which are published up to page 100 are consisting in what I call “meta-information”, information about information which reduces the meaning below a value-adding level. Actually, the more you read about big data over the web, the less you are likely to be convinced about the topic. I know this sound very semiotically or Foucault like, but my perception became a bit one of a frustrated librarian who cannot put the finger on the concept. Continue reading “Is Big Data Just Noise?” »