Analytics Adoption: Evolutionary vs. Revolutionary Technology

In this post about analytics adoption, I’d like to start with a short story.

The wife and I got ourselves each a Samsung Galaxy S4 over the weekend. Though it’s a great phone, we couldn’t help but feel that there was a distinct lack of a “wow” factor.

We both moved to the S4 from the S2. Back in its day (about two years ago) it was the latest and the greatest, and though technology has come some way since then it’s still a very capable phone. I remember when we got the S2… boy, did we feel like country bumpkins moving into the city. Everything was wow, wow, wow.

Even I, a self-prosessed can’t-go-a-day-without-the-computer-nut, could go a day (a day! can you imagine??) without touching my computer because everything I needed to do on it I could do on the phone. It was amazing to be able to send free text messages, and access e-mail and Facebook on the go. I didn’t know what I was missing until I tasted the data-plan-backed mobile life.

But having had such a capable phone already, the S4 comes to us an evolutionary and not revolutionary move. Sure, things are snappier, bright, faster, and larger. But that’s about all they are. It hasn’t been the same habit-changing killer app.

Evolutionary vs. Revolutionary Technology

Now, the S4 may be a evolutionary technology step for me, but for plenty of people who haven’t yet made the move to a relatively capable handset like the S2, it could well be a revolutionary move. The thing is that where a user is in the lifecycle of technology adoption makes a big difference to how that technology is perceived.

I would imagine that the biggest winners in analytics ROI (i.e. dollar returned per dollar invested) would well be those who have been avoiding it thus far. Because there’s such a huge gap to be bridged between no analytics and some analytics, even the smallest investments in analytics could give huge returns. (Whether it scales or not is another story for another day.)

And with the recent improvements in analytical processes/methodology, software, and thinking (a very important point here), things are far cheaper — and not just in terms of money, but of time, executive buy-in, and ease-of-adoption.

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