Tag: Big Data

About big data, in some way of another.

  • Coco, el perro que superó a Wall Street

    Coco, el perro que superó a Wall Street

    Martyn Rhisiart Jones

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  • STRAIGHT TALK: Grok, What Do You Make of Martyn Rhisiart Jones’ Take on Big Data?

    STRAIGHT TALK: Grok, What Do You Make of Martyn Rhisiart Jones’ Take on Big Data?

    Me: What do you make of Martyn Jones of goodstrat.com’s views on big data, are they correct, close or not true?

    [Grok thought for a while]

    Grok: Martyn Jones (of goodstrat.com) is one of the sharpest, most entertaining data contrarians out there. He’s been calling “Big Data” mostly bullshit since around 2014–2015, long before it became fashionable to say so. His Core Views (in his own blunt style): The whole 2010s Big Data circus (Hadoop, the 3Vs turned into 12 Vs including Vagueness, Vendibility, Vulgarity, etc.) was 80–90% vendor hype, consultant grift, and marketing snake oil.
    Most companies didn’t (and still don’t) have Google/Facebook-scale problems. Chasing petabytes of unstructured social media noise often distracted companies. They were diverted from cleaning and using the high-value operational data they already had.

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  • DATA WORLD: Friends! Romans! Countrymen! Big Data is not Data Warehousing – 2026/01/29

    DATA WORLD: Friends! Romans! Countrymen! Big Data is not Data Warehousing – 2026/01/29

    29th January 2026

    Hold this thought: To paraphrase the great Bob Hoffman, just when you think that if the Big Data babblers were to generate one more ounce of bull**** the entire f****** solar system would explode, what do they do? Exceed expectations.

    I am a mild mannered person. However, one thing that irks me is hearing variations on certain themes. These themes include phrases like “Data Warehousing is Big Data.” Another is “Big data is in many ways an evolution of data warehousing.” Lastly, some say “with Big Data you no longer need a Data Warehouse.”

    Big Data is not Data Warehousing. It is not the evolution of Data Warehousing. It is also not a sensible and coherent alternative to Data Warehousing. No matter what certain vendors will put in their marketing brochures or stick up their noses.

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  • DATA WORLD: Big Data With Bigger Smiles – The Chef’s Choice – 2026/01/28

    DATA WORLD: Big Data With Bigger Smiles – The Chef’s Choice – 2026/01/28

    I would like to introduce you to a pragmatic approach to Big Data and Big Data Analytics. It is real-world focused and business-centric. This is the best approach to Big Data you are ever likely to find. Yet, I am still significantly understating the magnificent utility. It is also timely and has all the pertinent facets of the approach.

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  • CEO BRIEFING: What Every CEO Needs to Know About Big Data – Revisited – 2026/01/26

    CEO BRIEFING: What Every CEO Needs to Know About Big Data – Revisited – 2026/01/26

    Martyn Richard Jones

    New York City, 18th January 2017

    OLYMPUS DIGITAL CAMERA
    Blue sky for professional data architecture and management

    Scope

    I’ll make this short, sweetish and to the point.

    These are the arguments that CEOs need to know about Big Data.

    It was written for CEOs and those who provide independent advice to CEOs.

    If you are someone who wants to be prepared for CEOs who are armed with the realities of Big Data, then maybe this is for you too.

    Point One: Big Data is bullshit

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  • DATA WORLD: Big data’s unvirtuous circus and twelve v-words – Refresh – 2026/02/05

    DATA WORLD: Big data’s unvirtuous circus and twelve v-words – Refresh – 2026/02/05

    Martyn Rhisiart Jones

    Bonn, Germany, 2014

    Many people come up to me in the street and ask me what big-data is all about. I have experienced this numerous times before. I am sure it might just happen to you as well. I know sort of thing, I read the big-data tea leaves. Nothing gets past me.

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  • DATA WORLD: Big Data: The Art of Bullshit – 2026

    DATA WORLD: Big Data: The Art of Bullshit – 2026

    Bertxin Galestara and Sir Afilonius Rex,  based on an  opinion piece written by Marty Rhisiart Jones

    Mountain View, 19th January 2017 – Bandoxa Thursday 15th January, 2026

    “I’ve been accused of vulgarity. I say that’s bullshit.” – Mel Brooks

    If you enjoy this piece or find it useful, then please consider joining The Big Data Contrarians on LinkedIn. (more…)

  • AI Panic: What CEOs Must Know About the Tech Talent Gap

    AI Panic: What CEOs Must Know About the Tech Talent Gap

    Martyn Rhisiart Jones

    Oza-Cesuras, 4/12/2025

    CEOs are panicking. Again. They’re not worried about a recession. It’s not a hostile takeover. It’s not even the GDPR lurking in their inbox like a passive-aggressive ghost. They’re panicking because of AI. Yes, apparently there’s a talent gap. A yawning, gaping chasm of human inadequacy between what exists and what robots can do. Only the CEOs are brave. Some may even be desperate. They are the only ones who stare into it without blinking or vomiting on the nearest whiteboard.

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  • The Role of Statisticians in the Age of Data Science

    The Role of Statisticians in the Age of Data Science

    Did data and AI kill the statistician?

    Without a grounding in statistics, a Data Scientist is a Data Lab Assistant.

    Martyn Jones

    Hold this thought: There are big lies, damn big lies and data science with an AI chaser.

    Statistics is a science, and some would argue that it is one of the oldest sciences.

    Statistics can be traced back to the days of Augustus Caesar. He was a statesman, military leader, and the first emperor of the Roman Empire. Some set its provenance even earlier.

    Indeed, suppose we accept that censuses are a part of statistics. In that case, we can trace history back to the Chinese Han Dynasty (2 AD). We can also consider the Egyptians (2,500 BC) and the Babylonians (4,000 BC).

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  • Getting business value from data? Commercial analytics is where it’s at

    Getting business value from data? Commercial analytics is where it’s at

    Martyn Richard Jones

    Overview

    I first became involved in commercial analytics in the eighties. First, through my involvement in customer segmentation and data visualisation, principally in banking but also in energy, manufacturing and the chemical industry. It also emerged later, in conjunction with my activities at the Sperry European Centre for AI, and was centred on pricing and yield management applications developed using a combination of statistical techniques, expert system technology and data centre architectures all tightly integrated within a 4GL development and delivery environment. This provided a comprehensive and seamless scenario building, hypothesis testing and reporting capability. (more…)