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vocationIf you enjoy this piece or find it useful then please consider joining The Big Data Contrarians:

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To the layperson anxious for answers to complicated questions, the very idea of bringing together sets of disparate data and turning it into precious insights may seem like magic, a modern day alchemy, a goal placed well beyond the grasp of mere mortals. Fortunately, this is no longer the case, thanks in part to bagatelle-proportioned advances in Big Data and Big Data analytics and massive advances in imagination; we are able to look into the past, the present and the future, with absolute certainty.

I know some people will question my judgement in claiming that we have passed a data-driven inflection point in insight, truth and understanding, but believe me it’s true and I can back up my claims with plenty of evidence, which is to be found in many articles on LinkedIn Pulse, Forbes and elsewhere.

Nevertheless, I hear you ask, how does this work in the real world?

It’s really quite simple. All that is required is lots and lots of Big Data, an adequate sixth sense addition to predictive analytics and the simple computing power of transubstantiation. For good measure, you may want to add a smattering of over-structured data from your tired, old and abject legacy systems, but that is entirely up to you.

Now, this will work for any type of business, but it is apparently very much to the fore in the world of banking, telecoms and retail, which is what I will try to focus on.

Did you know for example that Big Data, ESP and transubstantiation can help banks to “identify, win, serve and retain customers more efficiently”? Neither did I, but apparently it is true.

Banking Example

Bessy Bighead, quite possibly the most well-known member of the aristocracy of Dylan’s Laugharne, is a wealthy person. However, once upon a time  the financial institution she banks with assumed she was simply rich, that is, until they correlated the data they had for her in their operational systems, retail banking, family banking, investment banking and asset management, together with additional Big Data that they pulled in from Facebook, Twitter and YouTube.

Armed with Social Media, Big Data and Big Data analytics Bessy’s bank were able to put two and two together and make a billion. Now everyone is happy.

Telco Example

Brenda Windsor, an assiduous user of all things internet, tried to keep her identity private on the social media, the regular media and even elsewhere. Mostly in order to stop people begging her for medals and appointments and tips on how to raise corgis. Fortunately for the survival of capitalist humanitarianism, diligent data scientists armed with little more than Hadoop technology, a hunger for Big Data correlations and with an overwhelming desire for dosh, sorted her out.

Brenda, who went by the monikers of @WindsorHRH on Twitter and MadgeII on Facebook, had been rumbled. As a result, the Sex Pistols were able to re-release their smash-hit Anarchy in the UK in the knowledge that it had the blessing of the highest power in the land. We are told that Brenda, whilst not amused, did have a little chuckle. Haha!

Retail Example

Bobby von Drei Streifen, a little known sports apparel aficionado who spent fortunes on well brilliant adidas bling, and who also had his name changed by deed poll to show his allegiance to the brand, was a relatively unknown fellow. That is, so to

speak, until he took to social media, big time. He used the monikers of Three Stripes and Out, The Adithree am Free and Mein Cockney Scamp is a Champ, but nobody worked out that these three personas were the same guy. However, in using the power of social media, Juice Hana and adding-up numbers, the biggest opposing brands of sports clothes were able to tag Bobby with an RDIF, trace him in every mall, detect him every time he entered one of their stores and track his every action. Which in the end allowed then to promptly kick him out of, on his arse, each and every time he invaded their business space.

Media Example

Using Big Data, ESP and transubstantiation data scientists were able to identify online articles and blog pieces containing toxic volumes of misleading boloney. However, because of their sworn oath of misalliance, arrogance and ignorance, nothing more was heard of the story. So all I can add is, thank god or whatever for the freedom and liberty of the western presses.

Kiss and Tell

So, you may ask, how was it humanly possible to put together all of this disparate data, with little or no idea of who was who on each and every one of the social media sites? After all, the only thing that was vaguely reliable was in the legacy systems and general ledger, right?

So, how did they make the connection? How did they manage to collect, correlate and integrate the data? After all, how can you match Brenda to Elizabeth Windsor unless you do something illegal, indecent or dishonest? Or to put it more bluntly, how does one make the connection between telephone subscriber Brenda Windsor and comments by @HerMadge on Twitter, CorgiFan on Pinterest or BuckHouse on Facebook?

Well, they couldn’t do any of this legally, could they? So, giving people the benefit of the doubt, which I really want to do, I call bullshit on these stories. That is, unless people want to pony up and admit that illegal activities involving identifying data have been going on. In that case, may the perpetrators find themselves in a grey bar Hilton in Germany. They are criminals, and I have no time or sympathy for them.

That’s all folks

Nothing much to add on this one. Apart from a big thank you for reading.

So at the end of the day, it’s not about ESP or transubstantiation, or getting gold from lead or blood from stones, but about boloney or something not entirely legal. At least, not legal in any decent society.

Many thanks for reading.

In subsequent blog pieces I will be sharing my views on the evolution of information management in general, and the incorporation novel and innovative techniques, technologies and methods into well architected mainstream information supply frameworks, for primarily strategic and tactical objectives.

As always, please reach out and 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 you will even consider sending me a LinkedIn invite if you feel our data interests coincide. Also feel free to connect via TwitterFacebook and the Cambriano Energy website.

For more on this and other topics, check out some of my other posts:

Stuff a great data architect should knowhttps://goodstrat.com/2015/08/16/stuff-a-great-data-architect-should-know-how-to-be-a-professional-expert

Big Data, the promised land where ‘smart’ is the new doh!https://www.linkedin.com/pulse/big-data-promised-land-where-smart-new-doh-martyn-jones?trk=prof-post

Absolutely Fabulous Big Data Roleshttps://www.linkedin.com/pulse/absolutely-fabulous-big-data-roles-martyn-jones?trk=prof-post

Not banking on Big Data?https://www.linkedin.com/pulse/banking-big-data-martyn-jones?trk=prof-post

10 amazing reasons to join The Big Data Contrarianshttps://www.linkedin.com/pulse/10-amazing-reasons-join-big-data-contrarians-martyn-jones?trk=prof-post

Amazing Data Warehousing with Hadoop and Big Datahttps://www.linkedin.com/pulse/cloudera-kimball-dw-building-disinformation-factory-martyn-jones?trk=prof-post

The Big Data Contrarians: The Agora for Big Data dialoguehttps://www.linkedin.com/pulse/big-data-contrarians-agora-dialogue-martyn-jones?trk=mp-reader-card

The Big Data Shell Gamehttps://www.linkedin.com/pulse/big-data-shell-game-martyn-jones?trk=mp-reader-card

Aligning Data Warehousing and Big Datahttps://www.linkedin.com/pulse/aligning-data-warehousing-big-martyn-jones?trk=mp-reader-card

Big Data Ludditeshttps://www.linkedin.com/pulse/big-data-luddites-martyn-jones?trk=mp-reader-card

Data Warehousing Explained to Big Data Friendshttps://www.linkedin.com/pulse/data-warehousing-explained-big-friends-martyn-jones?trk=mp-reader-card

Big Data, a promised land where the Big Bucks growhttps://www.linkedin.com/pulse/big-data-promised-land-where-bucks-grow-martyn-jones-6023459994031177728?trk=mp-reader-card

The Big Data Contrarianshttps://www.linkedin.com/pulse/big-data-contrarians-martyn-jones?trk=mp-reader-card

Is big data really for you? Things to consider before diving inhttps://www.linkedin.com/pulse/big-data-really-you-things-consider-before-diving-martyn-jones?trk=mp-reader-card

Big Data Explained to My Grandchildrenhttps://www.linkedin.com/pulse/big-data-explained-my-grandchildren-martyn-jones?trk=mp-reader-card

If you enjoy this piece or find it useful then please consider joining The Big Data Contrarians:

Join The Big Data Contrarians here: https://www.linkedin.com/grp/home?gid=8338976

Many thanks.

A note from the Prime Minister:

Data is only as good as its time and place utility. If it has none, it has no present value, unless of course, someone wants to pay something for nothing, but that is constructing a con not an economy, an aberration destined to be hated and then forgotten. Don’t only think about how to use the data you have, but also about what data should be captured and how it should be used. By the way, join the Big Data contrarians here on LinkedIn: https://www.linkedin.com/grp/home?gid=8338976

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