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“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.”

Hypatia

On the 1st of July, I decided to set up a professional group on LinkedIn in order to create a hype free Agora for Big Data dialogue. I called the group The Big Data Contrarians and although it is a closed group, all those with interest in an open, informed and honest exchange of ideas on data, from whatever angle they are coming from, are very welcome to join in. (URL:  http://www.linkedin.com/grp/home?gid=8338976)

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 Google’s 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 a number of 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 and Big Data
  5. To weigh up the pros and cons of data technologies and techniques applied to solving 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 be true sometime in the future, and vice versa. Nevertheless, that encapsulates the general spirit.

As with other groups, we can all contribute discussions, comments, links to other material advertise jobs and promote what our businesses, and what we ourselves 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 to talk about in anything but the vaguest or the most venal, pompous or preposterous of terms. I want simple, clear and coherent, and I’m sure I’m not alone in this desire. I want to hear:

  1. We looked at this data. Yes, provide details of the data that you 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 exactly what is going on.
  2. These are our pain points and business questions. There’s no need to be coy, we can just spell it out.
  3. These are the business questions converted to Big Data analytics. 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. What outcomes from Big Data analytics did we get, if any, actually directly influenced business decisions, and in what way.
  5. The truth about ROI. How much did you spend? How much did you gain? What benefits accrued? What disruption ensued?
  6. Did your Big Data initiative go live?
  7. Is your Big Data success story still in production?

I like these seven bullet-points, because they are essentially the types of things I can talk about when it comes to 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.

Because that’s how we all progress, right?

Many thanks for reading, and don’t forget, please join The Big Data Contrarians.

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