How Product Management Shapes Analytics
My first experience with product management was a course called ‘software engineering’.
Of the fifteen teams of students who were using the full throttle software engineering method, complete with UML and a short burst of requirements gathering in the up-front, eight teams failed to deliver a product. Of the three teams I knew that succeeded, they each had one person that did all the software development, while the rest of the team was responsible for the documentation and working the waterfall.
Certainly, this was quantitative evidence that software engineering, as it was conceived of back then, was really ineffective. How could mechanical engineers hang a VW Bug from a sculpture, reliably and safely, using their engineering principles, while, a group of software engineers their same age couldn’t develop a piece of software? Wasn’t there something really, really, fundamentally wrong here?
There was. Waterfall methods, though dominant and popular, weren’t always right.
Then Software As A Service (SaaS) and the continuous deployment methodology came into their own. It changed a lot. It meant that you could start with a very small product and then iterate. You could develop a market as you built. It meant a continuous feedback loop between the market and the engineering team.
Traditional software engineers hated it. For a long time, if you were a ‘web developer’, you were looked down upon. Insulted, really, by the guys with the stacks of UML diagrams and the guy with the assistant who brought the specs from the fax machine to the engineers so the engineers don’t have to talk to the god damned customers (I have people skills).
Ultimately, the relative success of continuous methodologies silenced the smart ones. (The stubborn cling to days of yore.)
Most analytical products, as we think of them, are of the continuous development kind. Not all. But most.
And as a result, the product management of those products really shapes how analytics is done and thought about by practitioners and consultants. They make a huge number of incremental choices that add up to massive benefits (or consequences) over time.
Certain measures, like Time Spent, the word ‘Unique’, and the normative opinions about ‘Bounce Rate’ are three examples. Last-Click Attribution is another one. These have all been implemented, industry wide, because a tiny number of product managers willed them into being.
The result is that consultants and practitioners use these artefacts. They can be shoe horned into the latest framework. They can be loaded into SPSS or R and tortured until they talk. They can even be extracted from the software and put into a spreadsheet (!!!). Fundamentally, innovation beyond the product is constrained to what the product dictates. But by and large there are constraints.
Product management is market shaping. The market, to an extent, can influence the product roadmap. The market is always looking for substitutes. The threat of substitues keeps the dominant players somewhat pliant. Some firms are extremely resistent to being shaped by the market itself. If I had asked the market what they really wanted, they would have asked for a faster horse.
On the whole, product management in analytics is extremely influential in how digital analysts think. If you shape thought, you shape how analytics itself is practiced.
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I’m Christopher Berry
Check out what I do at Authintic.