The Backstory

As is often the case with people who are “between jobs”, I was whiling away time on Twitter the other day. A glass of some excellent Pinot Grigio later (OK, it may have been 3 glasses), I arrived at the inescapable conclusion that my Twitter timeline was filled with crap. And not just any kind of crap, but the kind that was so terrible it wasn’t just a time-sink, but also actively made you stupider by engaging with it in any manner.

And so I decided to embark on pruning my lists down to a manageable number, and removing any mostly useless follows. The big realisation here was that very few “famous” people have anything of worth to say, so pretty much all the sportspeople and actors were unfollowed as part of the spring cleaning.

Another conscious decision was to follow a bunch of people somewhat related to my professional interests (data science, programming, analytics, data visualisation, etc.) and see if I could engage in some of the conversations that were happening. The unfortunate outcome here has been that of severe disillusionment with the field, owing to a pretty solid validation of Sturgeon’s Law. A really great proportion of “thought leadership” that is out there in the BigCo business intelligence world is self-serving at best, and actively counterproductive at worst.

Despite this, the absolute self-confidence in all the bloviating that went on was a thing of beauty to behold. Everyone was coming up with solutions to everything.

This is when I had my business brainwave that will revolutionise the data warehousing/business intelligence world. It was so simple I’m a little surprised that it appears to be a novel idea: Analytics Syndication Services.

The Paradigm at Point A

The world is clearly enamoured of data driving everything: you have data driven decision making, testing, thinking, journalism, documents, and even, um, analytics.

But a few crucial questions remain: where is all this data coming from? What are people doing with it? And how does it learn to drive?

The core competencies

If a lot of large companies are are good at something, it is primarily in:

1. Learning from their past mistakes (i.e. how to avoid getting caught the next time)
2. Trying to align things (mission, vision, strategy, roadmap, and core values)
3. A thought process of “Something must be done, this is something, therefore this must be done.”
4. Attempting to cut costs by having one person do the job of four

So how can YOU, as a Big Data-age™ organisation, appear to be progressive, analytical, action-oriented and proactive all while increasing shareholder value?

Simply, by producing copious amounts of output that can effectively blend clever contortions of language and semantics with the authority of a collection of nicely coloured charts and a link to a CSV that says “[Dataset]”.

Shifting the paradigm from Point A to Point B

This clever amalgam of verbiage and numberwang is not everyone’s cup of tea. Since companies should obviously focus only on their core competencies, they would be well served by outsourcing this function to a specialist in Analytics Syndication Services.

This way, they can preserve valuable intellectual horsepower by only allowing the true experts to make statements like:

The best insights from Big Data are visualized through analytics. Only metrics do not mean anything without context - business owns this one.

or

A/B tests have shown that a statistically significant number of visitors to the home page prefer the mauve button to the heliotrope one, with a p-value of < 0.05.

or

Our HANA setup beats an HBase/Hadoop deployment by leveraging real-time predictive analytics to increase profitability by 23.5%.

This ensures that both the purely business types and the techno-functional types have their buttons sufficiently pushed, and can confidently carry out actions that are tenuously based on above pronunciations.

Another beneficial side-effect is that companies are now free to do pretty much anything, secure in the knowledge that professionals are now at hand to skilfully weave numbers and words into a compelling narrative that threads its way back and forth between fiction and non-fiction.

Non-believers may question the validity of the data, or the enhanced interrogation techniques employed on it, but they will quickly be silenced by an even greater flood of “statistics” and “intelligence” from the Analytics Syndication Service. And gradually, it will be clear to all who care to observe – crunching statements and data pulled from the A.S.S is the next billion-dollar idea.