Human Infrastructure & Analyst First

Recently I was watching a vendor vs vendor punchup on LinkedIn – various salespeople, vested interest consultants and fanboys all trying to declare their database was clearly far better than the other. To me it looked suspiciously like a bunch of car salesmen desperately trying to convince someone their vehicle was the superior one because of x,y or z feature.

The Stig

Rather driven by having recently attended an early meeting of Analyst First, I was somehat bemused at the complete sidelining of the Human component. Hands down, I will agree a Porsche 911 is technically faster than a Subaru Impreza. However, stick them both on the same track, put me in the Porsche and the Stig in the Impreza – and I wouldn’t put great odds on me crossing the finishing line first (or, to be honest – at all – i’m no race driver and would propbably end up in a ditch with spinning wheels).

Wreck

In any tooling choice, it is smarter to pick a toolset with which you can comfortably match people’s skills and experience. I will build you a great Microsoft BI solution, because I know the toolset intimately and will squeeze every possible drop of value out of it. I will make a middling Cognos solution because I roughly know what it does and should do in theory (I will also complain vociferously about anything MS BI can do better that it can’t). I will build you a terrible Jaspersoft solution because I don’t even know how to turn it on.

The impact of a few shortfalls here and there in capabilities of a toolset your team are familiar with will be minor compared to the impact of them blindly feeling their way through a new toolset with a set of preconceptions based on how their previous one worked.

Analyst First

Which is kind of where Analyst First comes in. They represent a component of the Analyst community here in Australia with a very focused aim: to equip the man, not man the equipment. What does that mean in practice? It means not spending the big bucks on analytics software and expecting the analytical manna to start falling from heaven, but instead spending it on the people who know the raindance, so to speak. Their proposition is simple and quite reasonable: a good analyst first and foremost needs skills – not tools – to do their jobs well.

Rolling back to the car analogy, there is no no point buying a learner driver a Porsche – spend the money on driving lessons first. The learner will benefit more from it, and also not suffer from the false sense of security that a powerful car can give you. I’m fast! I’m safe! I’m wrapped around a lampost! Oops. Analytics is a tricky occupation – it’s very easy for powerful tools to give you an answer, and for the inexperienced analyst to believe it must be right because the expensive tool made the answer (and made it look pretty to boot).

I’ve done just enough Data Mining to know that the wrong answers can leap off the page and look very convincing until you look under the hood as to why you get that answer. One example was that I had a strongly predictive indicator come out of my data. It predicted with about 95% accuracy that if factor Y was present, the customer fell into category X. Convincing stuff. Until I got under the hood and discovered that factor Y was only ever entered into the system for customers in category X. It went from being 95% predictive to 0%.

Human Infrastructure

Tying these two related topics together is the concept of Human Infrastructure, one that is often neglected in project plans and budgets. BI, and its cleverer – if scruffier and more academically inclined – relative, Analytics – is not just another system which needs a mundane user guide which states “To get outcome A, press button B”. To get value out of data you don’t just need to know how to use the tool – you also need to understand how to analyse data. This is a mishmash of competencies around maths, stats and logic to name a few, none of which are able to be bypassed through use of a tool.

I often hear that users don’t want to know about the details of calculations and aggregations and that BI should just serve it up on a plate. This worries me as if your end users aren’t motivated enough (or as few people will dare say out loud, smart enough) to understand how an outcome arose, but are prepared to make decisions on it, then they will make bad decisions. Witness the sub Prime crisis driven by people selling stuff devised by clever quants regardless of their own ability to understand it.

The bottom line: Worry less about tools, and more about the people that are going to use them.

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