Tag Archives: Big Data

Data Warehousing Explained to Big Data Friends

20 Mon Jul 2015

Posted by in Big Data, Big Data Analytics, Consider this, Data Warehousing, good start, Good Strat, goodstart, goodstrat

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Okay, before we get started I have to declare the real intent for posting this piece. It is to get you to join The Big Data Contrarians professional group here on LinkedIn.

To apply to join the best Big Data community on the web simply navigate to this address http://www.linkedin.com/grp/home?gid=8338976 (or paste it into your browser) and request membership, the process is quick and painless and well worth the effort.

Now for the rest of the news…

There are many common misconceptions amongst the Big Data collective about Data Warehousing. There are common fallacies that need clearing up in order avoid unnecessary confusion, avoidable risks and the damaging perpetuation of disinformation.

Big Picture

In the dim and distant past of business IT, the best information that senior executives could expect from their computer systems were operational reports typically indicating what went right or wrong or somewhere in between.  Applied statistical brilliance made up for what data processing lacked in processing power, up to a point, because even heavy lifting statistics requires computing horsepower, which in those days was really a question of serious capital expenditure, which not all companies were willing to commit to.

Then, and curiously coincidentally, people around the world started to posit the need for using data and information to address significant business challenges, to act as input into the processes of strategy formulation, choice and execution. Reports would no longer just be for the Financial Directors or the paper collectors, but would support serious business decision making.

Many initiatives sprang up to meet the top-level decision-making data requirements; they were invariably expensive attempts, with variable outcomes. Some approaches were quite successful, but far too many failed, until the advent of Data Warehousing.

Back then, most of the data that could potentially aid decision-making was in operational systems. Both an advantage and a problem. Data in operational systems was like having data in gaol. Getting data into operational systems was relatively easy, getting it out and moving it around was a nightmare. However, one of the advantages of operational data is that it was generally stored in a structured format, even if data quality was frequently of a dubious nature, and ideas such as subject orientation and integration were far from being widespread.

Of course, data also came in from external sources, but usually via operational databases as well. An example of such data is instrument pricing in financial services.

Therefore, briefly, a lot of Data Warehousing started as a means to provide data to support strategic decision-making. Data Warehousing ways not about counting cakes, widgets or people, which was the purview of operational reporting, or to measure sentiment, likes or mouse behaviour, but to assist senior executives, address the significant business challenges of the day.

Who’s your Daddy?

Bill Inmon, the father of Data Warehousing, defines it as being “a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management’s decision making process.”

Subject Oriented: The data in the Data Warehouse is organised conceptually (the big canvas), logically (detailing the big picture and) and physically (detailing how it is implemented) by subjects of interest to the business, such as customer and product.

The thing to remember about subject areas is that they are not created ad-hoc by IT according to the sentiments of the time, e.g. during requirements gathering, but through a deeper understanding of the business, its processes and its pertinent business subject areas.

Integrated: All data entering the data warehouse is subject to normalisation and integration rules and constraints to ensure that the data stored is consistently and contextually unambiguous.

Time Variant:  Time variance gives us the ability to view and contrast data from multiple viewpoints over time. It is an essential element in the organisation of data within the data warehouse and dependent data marts.

Non-Volatile:  The data warehouse represents structured and consistent snapshots of business data over time. Once a data snapshot is established, it is rarely if ever modified.

Management Decision Making: This is the principal focus of Data Warehousing, although Data Warehouses have secondary uses, such as complementing operational reporting and analysis.

In plain language, if what your business has or is planning to have does not fully satisfy the Inmon criteria then it probably is not a Data Warehouse, but another form of data-store.

The thing to remember about informed management decision making is that it needs to be as good as required but it does not need to achieve technical perfection. This observation underlies the fact that Data Warehouse is a business process, and not an obsessive search for zero defects or the application of so called ‘leading edge’ technologies – faddish, appropriate or not.

JOIN THE BIG DATA CONTRARIANS: http://www.linkedin.com/grp/home?gid=8338976

Some Basic Terms

Before we delve into the meaning of Data Warehousing, there are a couple of terms that need to be understood first, so, by way of illustration:

Let’s follow the numbers in the simplification of the process.

  1. We gather specific and well-bound data requirements from a specific business area. These are requirements by talking to business people and in understanding their requirements from a business as well as a data sourcing and data logistics perspective. Here we must remember at all times not to over-promise or to set expectations too high. Be modest.
  2. These business requirements are typically captured in a dimensional data model and supporting documentation. Remember that all requirements are subject to revision at a later data, usually in a subsequent iteration of a requirements gathering to implementation cycle.
  3. We identify the best source(s) for the required data and we record basic technical, management and quality details. We ensure that we can provide data to the quality required. Note that data quality does not mean perfection but data to the required quality tolerance levels.
  4. Data Warehouse data models modified as required to accommodate any new data at the atomic level.
  5. We define, document and produce the means (ETL) for getting data from the source and into the target Data Warehouse. Here we also pay especial attention to the four characteristics of Data Warehousing. ETL is an acronym for Extract (the data from source / staging), Transform (the data, making it subject oriented, integrated, and time-variant) and Load (the data into the Data Warehouse and Data Mart).
  6. We define, document and produce the means for getting data from the Data Warehouse into the Data Mart. In short, a bit more ETL.
  7. User acceptance testing. NB Users must ideally be involved in all parts of the end-to-end process that involves business requirements, participation and validation.

This is a very simplified view, but it serves to convey the fundamental chain of events. The most important aspect being that we start (1) and end (7) with the user, and we fully involve them in the non-technical aspects of the process.

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Business, Enterprise and Technology

Essentially, a Data Warehouse is a business driven, enterprise centric and technology based solution for continual quality improvement in the sourcing, integration, packaging and delivery of data for strategic, tactical and operational modelling, reporting, visualisation and decision-making.

Business Driven

A data warehouse is business centric and nothing happens unless there is a business imperative for doing so. This means that there is no second-guessing the data requirements of the business users, and every piece of data in the data warehouse should be traceable to a tangible business requirement. This tangible business requirement is usually a departmental or process specific dimensional data model produced together in requirements workshops with the business. We build the Data Warehouse over time in iterative steps, based on the criteria that the requirements should be small enough to be delivered in a short timeframe and large enough to be significant.

Typically, a Data Warehouse iteration results in a new Data Mart or the revision of an existing Data Mart.

Enterprise Centric

As we build up the collection of Data Marts, we are also building up the central logical store of data known as the Enterprise Data Warehouse that serves as a structured, coherent and cohesive central clearing area for data that supports enterprise decision making. Therefore, whilst we are addressing specific departmental and process requirements through Data Marts we are also building up an overall view of the enterprise data.

Technology Based

By technology, I mean technology in the broadest sense of techniques, methods, processes and tools, and not just a question of products, brands or badges.

Unfortunately, there is a popular misconception that Data Warehousing is primarily about competing popular and commercial available technology products. It isn’t, but they do play an important role.

Architecture

The following is an example of a very high-level Data Warehouse architecture diagram.

Methodologies

Various methodologies support the building, expansion and maintenance of a Data Warehouse. Here is one example of a professional data integration methodology, produced, maintained and used by Cambriano Energy.

And here is an information value-chain map as used by Cambriano Energy as part of its Iter8 process management. There are alternatives, many of which do a satisfactory job.

Last but not least, this was (from memory) the way that Bill Inmon’s Prism Solutions ETL company used to view the iterative EDW building process.

JOIN THE BIG DATA CONTRARIANS: http://www.linkedin.com/grp/home?gid=8338976

Keeping it Shortish

At this point, I decided to cut short further explanations on aspects on Data Warehousing. However, if you have any question then please address them to me and I will do my best (or something close) to answer them.

That’s all folks

Hold this thought for another time: If you think you can replace a Data Warehouse, that is not a Data Warehouse, with another approach to ‘Data Warehousing’ that doesn’t produce a Data Warehouse, for as fast and cheap as one can do it, then you still don’t have a Data Warehouse to show for all of your efforts. That is not a great place to be.

Therefore, you see, Data Warehousing was never about a haphazard approach to providing random structured, semi-structured and unstructured data of various qualities, provenance, volumes, varieties and velocities, to whomever was of a mind to want it.

Many thanks for reading.

 If you want to connect then please send a request. I you have any questions or comments then fire them off below. Cheers :-)

Oh… and one last thing before I go… DON’T FORGET TO JOIN THE BIG DATA CONTRARIANS: http://www.linkedin.com/grp/home?gid=8338976

 

The Big Data Contrarians – New Big Data Community

03 Fri Jul 2015

Posted by in Big Data, community, Good Strat, goodstart, goodstrat

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Friends, peers and colleagues, lend me your bandwidth and 10 minutes of your time.  Gather around and let me tell you about the greatest, most interesting and fantastically diverse Big Data and Data community right here in our very midst on this amazing LinkedIn community.

We have a new Big Data/Data group, and the group is aptly named The Big Data Contrarians, and yet it is neither a ‘me too’ group, of which there are too many to mention, or a ‘belief circle’, of which the less said, the better. Not, The Big Data Contrarians group is a place for cool opinion pieces, creative abrasion, practical insight and (within the realms of the possible) BS free comment.

However, before going into more detail about the group, I would like to digress for a moment.

Like many people, I take a lot of inspiration from outside my own professional spheres of practice, principles and technologies, and this is no less true when it comes to advertising.

Two of my real influencers – the real kind not the LinkedIn kind – are advertising legends Dave Trott (also author of Predatory Thinking) and Bob Hoffman (the Ad Contrarian), who are exceptionally experienced, talented and creative people, of the NoBS (no flim-flam) kind. Indeed, it was after reading some of Bob’s and Dave’s recent articles that I decided to get this group registered on LinkedIn, which, love it or loath it, is where many of us connect.

So, I hear you ask “What’s The Big Data Contrarians, Mart?”

Okay, to be fair, The Big Data Contrarians group is about far more than just being contrarian and a legitimate means of inciting discussion, for as reasonable as that is. It’s also about arguing against or openly rejecting mistakenly cherished and contrived Big Data beliefs and ‘institutions’ and established Big Data hype, speculation and opinion. It’s about separating Big Data fads, fantasises and folk-tales from Big Data reality.

What we seek to understand and convey is where, when, how and for what ends data (including Big Data) can be used to derive legitimate benefits. Moreover, stated from a position of reason and facts, and not simply projected as an issue of Big Data faith, speculation and clairvoyance.

On the other side, we can call out the Big Data hype for what it is, and just as Bob Hoffman calls out the social media and advertising BS babblers in his trade, this too lends a platform for people to do the same with the disreputable and dubious practices of Data gurus, courtesans and ‘influencers’.

“So, Mart, is being a Big Data Contrarian a bit like being a Big Data Luddite?”

Well, not really, but the problem with having so many people who are new to IT is that the past is a mystery top them, so anything that is new to them is actually taken as new, whether it is new or not.

Those who know will know that technologies of distributed file stores and search over unstructured data has been around for quite some time, and some of the “new” technologies that we big-up today, are actually simple developments of data technologies that go back to the seventies and eighties, or maybe even before.

However, this is not essentially about being anti-technology or even in advances in the application of technology, but of understanding that it isn’t helpful for the media, the big industry players and their indentured acolytes, to railroad, cajole and bully businesses into buying Big Data technology they don’t need, to solve Big Data problems and opportunities they don’t have.

That said, it’s up to the members of The Big Data Contrarians to decide on what shape the community should take, and as it is an open forum in democratic terms, the members have equal rights in presenting their own opinions, lessons learned and other insights.

So, if you haven’t yet drunk the Big Data kool-aid, come on down to The Big Data Contrarians, the place for everyone interested in Big Data/Data and its many potential uses.

Many thanks for reading.

Of course, this piece will also not feature on LinkedIn’s Big Data channel, because apparently that channel editor (naming no names) doesn’t like anyone raining on their particular Big Data flim-flam parade.

#BigData #BigDataChannel

Raise the Big Data Flim-Flam High

30 Tue Jun 2015

Posted by in Big Data, Consider this

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If there were ever a more apt rallying slogan for the Big Data BS babblers it would be “We BS about Big Data so that you don’t need to think”… and you know what? That’s how it is working.

The trouble with the hype is that almost everyone and their dog is in on it. From the freelance or indentured Big Data gurus to the Gartners, IBMs and HPs of this world. Everyone who is anyone is trying to jump on the Big Data bandwagon, whether it makes sense or not. Hell, if I could become ludicrously rich and infamous on the back of Big Data, I would jump the Big Data shark as well.

The other trouble is that the Big Data hype is very inconsistent in almost all areas, apart from the general unstated agreement there seems to be that Big Data will bring riches beyond the dreams of avarice, for everyone who wants it.

So, let’s assuming that one wants to cash in on Big Data, what’s the first thing that we need to understand?

Big Data comes in big data volumes, it has many data varieties, meaning it has a number of distinct formats, and it comes at us with increasing velocity, which most of the time we simply do not notice.

So what does that tell us? Right, Big Data is data; just more of it, more flavours of it, generated and transmitted at faster and faster rates.  To simplify, data is like water (Oh, no not another analogy) and whereas Data Warehousing is the Rhine or the Mississippi Delta, Big Data is the Ma and Pa of the Iguazu, Victoria and Niagara Falls.

So, what were the next questions I asked myself on the way to the land of Big Data health, wealth and happiness?

I asked, “if you have a Big Data success story then let’s hear the skinny”, such as:

  • Please detail data that has used to create new insight and understanding?
  • How was this data sourced, treated and stored?
  • How was the resulting data queried? Let’s see the queries, the code, the pseudo-code and the code narrative.
  • What were the results of the queries? In technical and business terms, please.
  • What normalisation of the results took place?
  • How did those results drive insight? In business terms, please.

Perfectly straightforward, right? These are the sorts of questions that one should be able to ask of a Data Warehouse user and then reasonable expect to get a coherent set of answer back in return.

Well, seems like when it comes to Big Data this sort of line of questioning and reasoning is somewhat problematic.

Which is a problem, because I am seeing fantastic claims made for Big Data, which is great, and I wish we all could become more prosperous from Big Data, but I can’t seem to get a handle on quite how one goes about it. It’s as if ‘tangible’ was anathema when it comes to practical and detailed examples of Big Data in action. You know, driving tangible business benefits through an understandable value chain of processes, ingredients and outcomes.

So, come on Big Data guys, gals and gurus, it’s time now to pony up, put up or shut up.

The Amazing ROI of Big Data

30 Tue Jun 2015

Posted by in Big Data, Consider this, Strategy

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For every professional bubble-head and bozo ‘bigging up’ Big Data there are at least ten intangible, unintelligible and phantom Big Data success stories.

Why do I write that? Simples! Because that is what we have.

From the perspective of non-IT business users, what does a real IT based success story look like?

Here’s some examples:

  • We ran a Big Data project and the end-result was increased sales and margins, which added $21M to the bottom line. The overall project cost, including cost of business disruption, was $7M.
  • We deployed Hadoop technology to identify potential influencers and purchasers on Twitter. As a result of the campaign we increased sales of the Widgets by 8% (adding $9M is revenues and $3M in profits on an investment of $1M).
  • Big Data helped us to identify and exclude significant errors of judgement introduced into our new corporate strategy. As a result we averted possible losses of more than $10M. Total cost of aversion exercise was $5M. $5M up and no egg on our faces.

These are fictitious examples of tangible benefits that might be accruable to Big Data. But, they are not factual, they are made up.

Remember this. They are not real-life stories.

Now, for some real-lifelike examples of benefits accrued from Big Data.

  • Big Data vendor strikes gold! The Big Data technology vendor GREPACLE today signed an enterprise wide licensing arrangement with the Fed for an estimated initial $750M, covering the years 2010 to 2017. The deal includes all industry-ready Hadoop “free ‘n’ open-source software” developed by GREPACLE. AWKACLE, who brokered the deal, expect to clear a $33M net profit from the arrangement.
  • OLLY-HARDY, the west coast hardware giant, has signed up WALLYCO who have handed over $60M as the first instalment for the provision of a cheap and cheerful battle-hardened commodity-hardware infrastructure that will replace the existing legacy infrastructure currently based on OLLY-HARDY MPP and SMP hardware and Oracle and Teradata software. A second contract billed to be worth in excess of $100M is in the pipe-line and is expected to be signed during the next quarter.
  • The profits of information theology research and technology advisory firm Gardening Leave jumped a clear 25% over the last three quarters due solely to sales of it’s reports and services in the Big Data domain.

The names changed, and the project details finessed, to protect the guilty, but they are three simple, clear and fabulous examples of how gold is obtained from Big Data.

However, there are many other Big Data success stories to consider, including:

  • The indentured Big Data pundits. Who wouldn’t lie for a slice of the pie? Right! But not everyone has scruples, values or even ethics when it comes to the filthy lucre.
  • The pro-Big Data press and their Big Data advertisers and ‘infomercialisers’. There still is money in getting people to advertise, big time.
  • The external service provider. The hardware may be commodity. The disk storage may be ample, cheap and cheerful. The unit cost of staff may be lower. But, you will be paying 10 times over the odds to your favourite outsourcer/offshoring business just for the privilege of having them screw up your Big Data project… 18 months down the line. You will even pick up the tab for breaches of data privacy and data protection. But don’t worry, paying someone else to make mistakes and learn on your money and time is the highest form of corporate altruism.

Well, that should give one a flavour of the direction of Big Data, of the benefits accruable and to whom the benefits really accrue.

Now here’s a thought:

Most of the success stories seem to have the sale of a Big Data project, Hadoop’s ‘grep awk ecosystem’ and ‘development’ services as its central tangible success criteria.

At best, these are dubious Big Data tech and service vendor success stories.

What tangible Big Data client benefits are there on view in the public domain? How about non-IT business Big Data ROI? Same for Hadoop ROI? Same for Big Data and Tech Stack service ROI?

What about… Who? What? Where? When? How? Why?

Oh, there aren’t any success stories like that or they are so secret that one cannot but allude to them in generic BS terms.

But, seriously? Do people still swallow that type of mendacious flim flam?

Many thanks for reading.

Big Data Explained to My Grandchildren

29 Mon Jun 2015

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Ban pan doeth peir

ogyrwen awen teir

The Book of Taliesin 

Once upon a time, a hobgoblin of digital moonshine stalked the land. Its name was Shirley Temple (but, it was better known as Big Data), and it had many followers.

Few really knew where Big Data had come from, because it just appeared overnight. Like owls, snow, rumour and astroturfing flim-flam merchants.

Some say the Gardner brought it in on the bottom of their wellies after a particularly tough night on the lemonade.

One night, a man with a black dog told me that it was really all a load of old nonsense, dreamed up by Redwood Shore Larry, to shake things up a bit.

Others, of the more superstitious bent, claimed that the giant who lived in the Big Blue mansion on the hill, had concocted it, from sugar and spice and all things dodgy and nice

The more cynical amongst the population just pointed at its high priests, acolytes and bicycle boys, and had a good old laugh.

Yet others claimed that it was a digital immaculate conception and a divine-revelation of mega-trend setting proportions that would change the face of the Lleyn peninsula, forever.

Elsewhere, some talked of dark deeds, of wickedness, or that it was a psycho-paranormal phenomenon closely associated with the cultish cult of the badly drawn Yellow Elephant. A wonderful wacky, off-the-wall and global orco-centric sect that sacrificed the processing-cycles of reason, strategy and coherence on the altar of half-baked pragmatism, bodgerism and winging-it.

Nonetheless, some of the global villagers did express opinion that this was no new phenomenon, and that they had seen such a sinister semblance before. Knowledge and experience had informed them. They seemed to intrinsically know that a timeless feature of data is its variable volumes, its variable velocities, its increasing varieties and, because we insisted on hoarding so much of it, its increasingly expansive footprint.

But, how did they know?

They only had the gardener, the butler and the cook to corroborate their suspicions.

We know what we know, and what we don’t know we know what we don’t know, now, that is, but don’t tell, unless we do, or don’t, or not. So I’m glad we cleared that one up.

Down the valleys, across the moors and over the waves. From Bangor to Abertawe via Machynlleth and Caerfilli. Big Data, it moved and expanded, and expanded and moved again. Dong! Dong! Dong! Ominous, humongous and smelling of sulphur and a badly spiced kebab.

The Big Data mini-meme spread like wildfire fuelled by petrol and crack. It was a force to be harnessed, a force for good and bad. Even though no one really knew what it was, and nobody knew how to do it, many claimed to have done it, and successfully so. It didn’t matter how, what where or when. Front interface, back-end processor, client-server, no one and nothing was free or safe.

The benevolent Big Data virus reached everyone. The rich, the poor, the shop on the corner, and the girl next door. Everyone knew its name and that it was new and mega and good and bad and all of that.

The thing is, Big Data wasn’t really anything new. As we now know.

But, at the time, for some people, especially the so called high-ups and professional people, it was a big deal, even in Pontypridd! Like a major inflection point in the evolution of the generation and use of what we now call data, or to use the vernacular, digital gold-gold.

You see, back then, people wanted to make another class of data and another class of gold, and another series of lovely, chubby little verbose categories to describe it. People needed another name, one that represented some data class values, spirituality and imprecision. It was a time of post-modernism, and we were always stoned, mazed or drunk.

Some of my peers at the time – not all of them, just the exceptionally plain Jane, weird and alternative ones – told me that Big Data was data that came in bigger volumes, at greater velocities and in greater varieties.

The first I heard that, I was gobsmacked… Honest to God!

I know probably you’ll laugh at that, now, and you may even tell your friends and butties just how superstitious, primitive and money-grubbing we were back then.

So whilst you are making fun of your old granddad, do not forget either, that is the way it was in those days, down the data mining towns and Big Data pits of South Wales.

I know it is hard to believe, but back in those days, people really believed in that nonsense. I didn’t have any time for it myself, but many did, and many people made a living of sorts just talking and writing about it.

It’s hard to believe now, but at one time we were really dumb, but as we didn’t want to stand out from the crowd by revealing that we didn’t know, we just stood back and let the buffoons, clowns and comics run the show. Whilst we exclaimed, “these guys are really good”, “I wonder if he can turn Big Data into wine” or “maybe he does requests and children’s parties”.

Now we look at data, all data, and we call it…. Yes… data.

How we have advanced. It’s amazing.

But then, when it all died down, as these things do, in their given time, we regained our senses, of sorts, we got back on our feet and we progressed in a sane, rational and humane way.

Now we can look back and see things for what they were. The Big Data hullaballoo, that you have never heard of, was the last gasp of some the biggest IT dinosaurs, who had tied their hopes and aspirations, ridiculously so in my view, to some toys created by the naïve for the gullible. It was always going to end in tears, and it did.

Now you know.

So, this ends the story of Big Data, also known as Shirley Temple.

Goodnight kids!

Many thanks for reading.

As always, please 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 even send me aLinkedIn invite. Also feel free to connect via Twitter, Facebook and the Cambriano Energy website.

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

#BigData #BigDataAnalytics #Decency #Ethics

Is big data really for you? Things to consider before diving in

13 Sat Jun 2015

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“For the likes of Google, Twitter and Facebook, Big Data is an intrinsic part of their business and plays a key role in their ability to survive and thrive. However, it is not for everyone. Here’s how to know for sure if Big Data is for you.”

The rest of the article appears on my IT Circus blog hosted by IT World (Copyright © 2015 IDG Enterprise)

Link: http://www.itworld.com/article/2934368/big-data/is-big-data-really-for-you-things-to-consider-before-diving-in.html

#BigData #DataAnalytics #Qualification

The Hadoop Honeymoon is Over

16 Sat May 2015

Posted by in Big Data, Consider this, Good Strat, Good Strategy, Martyn Richard Jones, Strategy

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Listen up Big Data playmates! The ubiquitous Big Data gurus, tied up in their regular chores of astroturfing mega-volumes, velocities and varieties of superficial flim flam, may not have noticed this, but, Hadoop is getting set up for one mighty fall – or a fast-tracked and vertiginous black run descent. Why do I say that? Well, let’s check the market. Continue reading →

Big Data Tales: Bernice and the Martians

12 Tue May 2015

Posted by in Big Data, Consider this, good start, goodstart, goostart, Martyn Jones, Stories

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Bernice and the Martians, BATM for short, were an incredibly popular progressive-rock band.

Their first big commercial success came with the release of their first album and their planned promotional tour, which took in all continents.

The manager of the band was none other than effable polymath, Renaissance man and good all-round rogue, Ricky Jonesy – an obsessive control freak, lover of fine wines and darling of predictive analytics. He really loved his numbers, his social media and his sentiment analysis.

In fact, much of the early success BATM came about due to Ricky’s unparalleled passion for the ‘Big Data’.

Ricky was the band’s architect. He had major input into their material: what they composed; how they composed; their stage sets and lighting; where they performed; the way they played; how they dressed; were photographed; spoke; walked; and, ate and drank. In short, he controlled the whole BATM enchilada. It was like being in data-driven heaven.

As I said, their first album, a progressive-rock masterpiece called ‘Your Hole’, achieved major critical acclaim even before it was bolting out of the stalls and across the interwebs. Overnight the band became big property, and their notional market value ran higher than Twitter on steroids.

The band members were really please. The presses interviewed Bernice right, left and centre and he made no bones about the fact that a major part of their success was due to Ricky and his Big Data mojo.

Articles about the phenomenon appeared in all the major social media sites. Facebook, LinkedIn and BubbaToons. Ricky was named Supreme Data Scientist of the year by the Gardener Group, hailed as a messiah by the Big Data Front and lauded by all and sundry.

Then the band went on tour. Blazing a trail of ones and zeros across the face of the planet.

They were 5 gigs into their tour and Ricky decided to call a band meeting.

“Hi, guys” said Ricky “I’ve been analysing the stats, and I see that those yokes Big Blokes in Tights are trending strongly on the social media, coinciding with the release of their new single Never Stick A Banger In Your Ear”.

“Oh, whoa” chimes in Bernice, “tell us what we gotta do then, Ricky”.

Back comes Ricky. “Well, this is what I thought we might do”

“We take the old Fester and Ailin song Tropical Diseases, we practice it as much as can, and then we play it at the next gig in Birmingham, this weekend”

“But, Ricky!” pipes up Marty Smarty, “it’s an Irish country and western song. It doesn’t fit in with what we do, does it? And, anyway, we only have three days to get it prepared.”

Ricky responds. “Ah, you don’t want to be worrying your little head over that. Trust me. Learn the song. It’ll be great. The public will love it.”

So, BATM learn the song. It’s perfect. At the Saturday gig, they play it as the encore. The fans love it to bits and there’s not a cold cigarette lighter in the place.

Then they fly off to Palma de Mallorca for a bit of a rest before their next gig in Madrid.

The guys and gals are lounging at the poolside at the legendary Don Pimpón Espinete Plaza complex. The weather is glorious, the food is glorious, the scenery is glorious, and even the orchestra is glorious.

Then along comes Ricky, calling yet another band meeting.

“Hi, guys” said Ricky “I’ve been analysing the stats again, and I see that those yokes Spanky’s Magic Piano are trending strongly on the social media with their cover version of Engel Humpadink’s The Monkey Song”

“Oh yeah, what’s that mean for us, Ricky” chimes in Halo Popette, the bands keyboardist.

Back comes Ricky. “Well, this is what I thought we might do”

“We take the old Fester and Ailin song There’s A Dead Man Up The Chimney, and we rewrite it in the style of Tom Jones when he made that album of his, Little Fockers, was it? Then we practice it as much as can, until it’s perfect, and then we play it at the next gig in Madrid, this weekend”

“But, Ricky!” pipes up Brian McGarsical, “It’s a bit of an odd one isn’t it? I mean to say, it doesn’t fit in with what we do, does it? And, anyway, we only have four days to get it prepared.”

Ricky responds as fast as a chalked-up cat going down a drainpipe. “Ah, you don’t want to be worrying your little head over that. Trust me. Learn the song. It’ll be great. The public will love it. And anyways, it will fit nicely on the playlist, up there with Tropical Diseases.”

The band rewrite the song, and practice the Bedejaysus out of it. Ricky likes it so much that he gets the stats to confirm that this has to be number one on the next gig playlist.

Come the day of the gig, and BATM kick off, not with a progressive-rock anthem, but with There’s A Dead Man Up The Chimney. A group of young people at the front clearly are loving this new sound, but quite a few people are starting at the stage in fright, and it’s not from skunk induced paranoia either.

Two guys are having a conversation at the back of the hall.

“Yo, lunchbox, hurry this gig up, I thought this band was all progressive-rock and stuff, not this wiener schnitzel stuff.”

“No comment.”

Having divided the crowd with their first song, they play songs from their album. Again, they encore with Tropical Diseases. The crowd at the front go wild. The progressive rockers look on, bemused.

“Well, that was a mixed bag” says Bernice.

“Take it from your man Ricky. It all went fine lads. Just needs some fine-tuning of the songs and the analytics need to be a bit more real time. Take me word for it.”

Back comes a unison of “Okay, Ricky. We believe yas!”

So, off they go to Bonn, to prepare for the following weeks gig at the Live Music Hall in Cologne.

The band goes out visiting the museums, they have lunch at Brauhaus Bonnsch, and after a leisurely walk along the banks of Rhine they are taking a beer or three in a lovely little beer garden close to the United Nations campus.

Then out of the blue, a familiar voice can be heard.

“Hi, guys! We’re all goin’ on a summer ‘oliday”. It’s the voice of Ricky. “Anyway, Good news guys. I’ve been analysing the amazin’ Big Data stats again, and I see that those mensch Die Zahnarzt are trending strongly on the social media, especially on Swotter and Titter, with their amazon’ cover version of Podge and Rodge’s chillout mix of Currywurst and Microchips.”

Silence. No one says a word for the best part of infinity.

Ricky continues… “As you’re not going to ask, lads, I’ll tell you. We take the old song A Great Day for the Washing, and we rewrite it in the style of techno-Buddah-bar-chill. Then we practice it as much as can, until it’s perfect, and then we bang it out at the next gig in Cologne, this Friday. Innit. Come on lads, it’s 20 minutes of stage magic, and it’s a breeze.”

Come the day of the gig, and the band arrive early at the hall. Ricky is already there. He’s changed the stage set completely and has a new wardrobe for the lads – Bavarian romantic. They’ll soon be all Princed and Smiley Virused up to the eyeballs, wrecking ball included.

and BATM kick off, not with a progressive-rock anthem or chill, but again with There’s A Dead Man Up The Chimney. Again, a group of young people at the front clearly are loving this new sound, but quite a few people are starting at the stage in drug induced awe. Then they follow that up with A Great Day for Washing. By the time they get to the encore of There’s A Dead Man Up The Chimney, boisterous arguments are breaking out everywhere and empty crisp packets and used sticks of chalk are being thrown at the stage. It’s a disaster.

Four guys are having a conversation at the back of the hall.

“I liked the first song”

“No! The first was terrible. Minging! I want my prog rock back.”

“It’s like the choice of leprosy or the plague.”

“Down with this sort of thing.”

Next day Bernice calls an urgent meeting of the band.

Ricky kicks off.

“Well, lads bit of mid-week game yesterday wasn’t it?”

Bernice comes back with a “You can say that again, Rick”

“Don’t worry, I have analysed the social-media Big Data from all of the concerts, and we’re doing good guys. It’s in the analytics”

“We have to go back to our roots and drop all the changes we made”

A stranger in the lounge where they are having the meeting walks up to them and in simple language explains to them what has happened.

“You created a great product, a great brand, with some interesting progressive music”

“Your music was acclaimed and your world tour was eagerly anticipated by all your fans”

“But then you went wrong”

“You became data driven, dopey and data driven”

“You chased fads, tendencies and styles, and it became a mish-mash”

“People don’t want mish-mashes. Not your base. They wanted good progressive music”

“You’ve lost all credibility. No, you’re just an eccentric band of brothers and sisters that no one will really want to see more than once, if at all”

“Your former fan base is acutely embarrassed by you. That’s your bread, butter, vodka and caviar… in your terms”

“Data drive, Big Data, Big Data analytics in real time?”

“You people have no idea the damage you can do, and so easily”

To be continued…

Many thanks for reading.

Sexing up Big Data’s Dodgy Dossier

20 Fri Mar 2015

Posted by in Big Data, Consider this, good start, Good Strat, goodstart, Martyn Jones

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Most of us would probably like to work in a profession recognised for its legality, decency and honesty. At least I hope so. In my line of work, what we have right now is palpable evidence that the IT industry lacks a moral compass.

Imagine this. A major sensationalist tabloid pulls together a team of diverse journalists who are set to work on a national campaign to promote very high usage of sunbeds as a cure for cancer. Why? The newspaper owner’s son owns the sunbed franchise.

The health experts criticise the publisher for being irresponsible, unprofessional and lacking in scruples.

The public is mainly undecided, but many take the story on face value and adopt the fad. The intensive use of sunbeds sharply increases. Elsewhere, in unrelated news, the cases of skin cancer show a marked increase. Some blame it on EU legislation for bangers and bananas.

In spite of protests, the press campaign continues over many months.

Eventually, and based on the evidence of recognised health experts and bodies, the press regulatory association tries to get the offending publisher to temper their claims, but without any success. It is only when the government’s lawyers step in and threaten the newspaper owners with legal proceedings, do they freeze their campaign. Much later, the editor resigns and the board of directors issue a short apology on the back pages of their much vaunted organ.

We have that in IT. Our current sunbed cure for cancer, if you believe those who are ‘bigging it up’, is undoubtedly Big Data.

I occasionally post content to Linkedin, some of it (maybe even this piece) gets promoted through the Pulse Big Data channel. There are some reasonable pieces pinned to that channel, but unfortunately, for much of the time what we get is total and moronic Big Data astroturfing. Tantamount to the equivalent of Big Data’s very own Big Lie campaign.

The Linkedin Big Data channel reflects life, and it is full of self-aggrandising and shameless marketing guff, shot-through with scandalously flimsy promotions of tendentious success stories, specious claims of value, half-truths about realisable benefits and embarrassing conjecture about the importance of social media and internet logs.

What I am referring to mainly are superficially neutral (yet virally toxic) pieces placed in the public domain in order to promote Big Data at any cost.

Now let’s step back a bit.

For over 125 years, the Financial Times (FT) has built up a solid professional reputation for accurate reporting, reliable journalism and informative editorials. The FT is a newspaper trusted by its discerning readership and admired everywhere. In fact, I could not imagine their journalists writing about markets, securities and financial houses the same way that pundits elsewhere write about Big Data, Dark Data and the Internet of Things. Because the FT knows, that maintaining the trust of their readership is far more important than winning the short-term favours of a few market players.

So consider this; if we in IT cannot bring our standards of communicating with the public up to the levels of the financial industry, at minimum, you know what that means don’t you?

Exactly. The IT industry will have a far worse public image problem than the bankers and the solicitors currently have, and we all understand the general public appreciation of those professions.

Now, call me old fashioned, but for me that possibility is worthy of serious consideration, and especially by those in IT who confuse no holds barred pimping of fads, trends and technology, in which truth, decency and honesty are optional, for ethical, candid and informative analysis and reporting of the industry.

How will the industry take these criticisms?

To go back to the sunbed analogy what we will most certainly get comments in this vein:

Whilst those who rail against ‘the cancer curing advantages of sunbed use’ may be right – or at least partially right – the sunbed revolution will continue, just as the IT revolution industry has done, and in spite of people saying that the age of computing would be a passing mania.

So, when someone tells you “intensive sunbed use is just a dangerous fad”, what they actually mean to say is that we don’t need the term any more, as intensive sunbed use is here to stay, as are those who are shrewd, unprincipled and cynical enough to cash-in on the public’s gullibility and wilful stupidity when it comes to fads.

Yes, it does get that bad.

We have people who seemingly spend all their waking lives working out not-so-original ways and means of riddling the IT industry with vacuous bullshit, and what Big Data promotion has shown us clearly is that what we have palpable and comprehensive evidence that the IT industry in general lacks a moral compass.

Is that a reflection of IT, of those who create and manipulate IT fads, or of society in general?

Many thanks for reading.

As always, please 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 even send me aLinkedIn invite. Also feel free to connect via Twitter, Facebook and the Cambriano Energy website.