Martyn Richard Jones

San Martiño de Bandoxa

24th April 2020

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The big-data contrarians

In fact, men will fight for a superstition quite as quickly as for a living truth – often more so, since a superstition is so intangible you cannot get at it to refute it, but truth is a point of view, and so is changeable.


Millions of people believed that the earth was flat and that the sun orbited the earth. That was “conventional wisdom.” Thinking otherwise would have made you a contrarian. A person holding a contrary position usually against a majority.

There aren’t so many examples of conventional wisdom being so wrong, but there are enough cases to justify healthy scepticism.

On the 1st of July 2015, I set up a professional group to create a hype-free Agora for big-data dialogue. I called the group The Big Data Contrarians, and I used the LinkedIn platform to set up a forum. And although it is still a closed group, all those with interest in an open, informed and honest exchange of ideas on data, their analysis and uses, from whatever angle they are coming from, are very welcome to join in.

So, why is the group called The Big Data Contrarians and not something more generic, such as The Data Contrarians?

It was a hard call, but in the end, I decided to go with the spirit of the times, for as overhyped and laden down with tripe as it might be, and the name stuck. This doesn’t mean that the group is limited to airing views, opinion and speculation just about big-data. There’s a wealth of related data out there, and it’s not all as big as Googles stash. Indeed, one of my pet projects for the world of big-data and the Internet of Things is in discovering, designing and developing ways to reduce the big-data footprint, early and often, without losing the essence of what the data might be able to tell us.

I believe that The Big Data Contrarians group fulfils several useful functions:

  1. To alert people to interesting but ultimately dubious, big-data claims.
  2. To share lessons learned, good sense and practical data and big-data principles.
  3. To connect professionals in overlapping disciplines, for example, in statistics, data architecture and data management, project management, solutions architecture, database administration, data science, risk management, technical, management and executive management roles, and a long list of etceteras.
  4. To educate, inform and entertain each other about the practical world of data, big-data, and how we integrate, enrich and interrogate that data.
  5. To weigh up the pros and cons of data technologies and techniques applied to solve business challenges.
  6. To minimise the hype surrounding big-data, covered in part by the first item, but more so.
  7. To engender critical thinking, healthy scepticism and reasoned contrarianism.

Of course, these are simply suggestions, and what may not be false today, may just be true sometime in the future. And vice versa. Nevertheless, that encapsulates the general spirit.

The group also is broad enough to include discussion and comments on data analytics, statistics, data science, data governance, data protection, data architecture and management, data warehousing, data lakes, data hubs, master data management, data at rest and data in movement, fast data and slow data, edge computing, Internet of Things, artificial intelligence and learning, and data humour.

As with other groups, we can all contribute discussions, comments, links to additional material advertise jobs and promote what our businesses, and what we are doing.

In particular, I would be interested in hearing the facts about real-life big-data success stories. I want to see things that have some value, not just ambiguous references to what might work or allusions to success stories that are just too successful in talking about in anything but the vaguest and the most venal, pompous or preposterous of terms. Me, I want simple, clear and coherent evidence, and I am sure that I am not alone in this.

I want to hear:

  1. About the data. And details of the data used, the data objects, attributes, data structures and business and technical meta-data. It doesn’t need to be exhaustive, it just needs to be sufficiently complete to understand precisely what is going on.
  2. About the pain points and business questions. There’s no need for big-data folks to be coy, they can just spell it out.
  3. About the questions asked of the data. What were the business questions converted to advanced big-data analytic solutions? Yes, let’s see details of the search, sum and compare parameters, etc. after all, this is about understanding and spreading the “word.” So let’s just make it happen.
  4. About the outcomes. What results from advanced big-data analytics did they get? Did these outcomes actually directly influenced business decisions, and in what way?
  5. About the money. What is the truth about the return on investment (ROI)? How much did the good-folk spend?
    • How much did they gain?
    • What benefits were accrued?

What disruption ensued?

  • About production acceptance. Did the big-data initiative go live?
  • About durability. Is the big-data success story still in production?

I like these seven bullet-points because they are necessarily the types of things I can talk about when it comes to data architecture and management; data warehousing; and, risk management and reporting. I also think that the time has come for us to move on to less prosaic and woolly allusions to big-data successes, and indeed share the skinny on both real big-data successes and real big-data failures – and the same applies to AI, deep learning and advanced analytics.

Because that’s how we all progress, right?

Many thanks for reading.


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