One of the wonderful things about going to conferences is the ability to sit back and listen instead of leaning forward and solving problems – or equations – all day. When one is sitting back, listening, the mind gets to wander. Not far, but wide enough to hit some aha moments along the way.
These insights are not directly attributable to a given speaker, they are just part of the consilience that happens when a prepared mind has a moment to catch its breath.
Such was my experience at the eMetrics Summit in Toronto last week, only this time I can attribute it to Stéphane Hamel.
Stéphane gave a great presentation about how to make the most of big data and showed a wonderful picture of a tree with its roots exposed.
He explained that, while we’re all standing around and looking at the trunk of the tree, business intelligence (BI) concerns itself with the roots. BI is all about understanding the complexity of business processes and things that go on behind the scenes (underground).
However, digital analytics, says Stéphane, is about customers – all those green leaves, waving around in the air. Digital analytics is about the complexity of the public and their reactions to breezes and diseases and…well, you get the point.
It’s wonderful when an analogy just puts everything into perspective.
Throughout the day, I kept wondering about another analogy I had been working on about analytics, based on the ineradicable problems we all face with analytics.
It has to do with driving.
Data = Roads
It’s good to have roads. The more the merrier. The more roads you have, the more choices you have. That’s great. But when there are too many, when they are inconsistently paved, insufficiently identified, and inefficiently connected, you will get lost.
Good, clean, even roads really improve speed, mileage, and comfort but they’re not all like that all the way from point A to point B. Good, clean data would really improve insight, results, and real-time interactions. But data are messy and need to be corralled with tools.
Vehicles = Analytics Tools
There are all sorts of vehicles on the highway and there are many choices about color and style but the first question is function.
A motorcycle and an 18-wheeler have different purposes, and just because you got the most expensive or the fastest does not mean you have the best one for the job.
Just getting from point A to point B ignores the problem of what you are transporting. Some data analytics problems require more capacity.
Vehicles need mechanics; people who are professionally trained to maintain the equipment and keep the machine on the road. If something goes wrong, they can pull it apart and put it back together.
They can tag pages, grab social media data streams, feed unstructured data to a MapReduce cluster, extract, transform, and load the pre-processed results into a datamart specially designed for the purpose.
The mechanic cannot pave the roads or make the vehicle, but can certainly keep an eye on fixing it when it won’t start, stalls out every 500 miles, or starts making that funny noise. They are mechanics, but they don’t drive.
Driver = Analyst
Professional drivers on a closed track, wearing a seatbelt and a helmet have a fun job. They know the power of their gear and they know just how much risk they are willing to take.
Once they get into the race, they then have to deal with the competition. They have to gauge the other drivers and cars around them. It’s no longer man versus machine, but man versus man in machines. He can’t worry about how the engine is running. That’s the mechanic’s job.
The driver’s job is to keep the car on the road and not hit – or get hit by – the other drivers.
The expert analytics tool user must understand the data and the tools enough to get the best performance from an analytics tool and data set while solving live, business problems. But the race car driver has one advantage over the average driver on the freeway; he knows exactly where he’s going.
Navigator = Business Visionary
No, I don’t mean the founder of a new class of industry. By visionary, I mean the one who knows enough about all of the above to see the big picture; to understand what the future might look like and be able to prioritize which data are more valuable and might produce more insight.
The navigator is the project manager, the one who understands the goals of the mission, the limitations of the team, and the cost of making the wrong decision.
The driver drives, the mechanic fixes, but the navigator uses whatever tools and information are available to tell her to figure out which roads to take and how long it’ll take to get to their destination.
And why are all these people doing all this work? Why are all these resources spent on this effort?
Because, in the back seat, all strapped in and safe, is the department manager, the division chief, or the CEO who wakes up every now and then and asks, “Are we there yet?”
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