Building and Measuring Analytics Culture

Culture – how to measure it and how to build it – has been much on my mind lately.

At least when it comes to the measurement part – something we don’t normally have to do – the reason is…different.

My Counseling Family team is going to be doing another fun project – participating in the 538 Oscar Modelling challenge – and our approach is to try and model each nominated movies’ fit to the current Hollywood zeitgeist. The theory behind the approach is simple. It seems fairly reasonable to suggest that while qualitative differences between nominated movies and non-nominated movies might be fairly large, when it comes to selecting between a small-set of relatively high-quality choices the decision is fairly arbitrary. In such situations, political and personal concerns will play a huge role, but so, presumably, will simple preference. Our thought is that preference is likely more a function of worldview than artistry – in much the same manner that people watching a political debate almost always believe that the person who most nearly echoes their opinion-set won. But how do you measure the cultural fit of a movie to a community? It’s no easy task. Our challenges include deciding how to capture a cultural zeitgeist in general, how to focus that capture on Hollywood, how to capture the spirit and themes of each movie, and how to score a match. And, of course, there is the challenge that the Hollywood zeitgeist might be more hot air than great wind and altogether too thin to be captured!

Should be interesting.

Equally, though, I have been thinking a lot about how to build culture – specifically when it comes to analytics. A constant theme running through my recent posts on enterprise transformation has been the challenge of doing digital well in the large enterprise. As I’ve repeatedly pointed out, that challenge is less a matter of technology or people, as it is of organization and culture. Enterprises have and understand the core capabilities to do digital well. Listen to my recent video series and you’ll hear this refrain:

  • The typical enterprise does analytics, they just don’t use it.
  • They have testing, they just don’t learn.
  • They talk voice-of-customer, they just don’t listen.
  • They do Agile, but they aren’t.

To fix those problems requires changes in the way the organization is structured and he way those capabilities are done. Even more, it requires changes in the way people need to think. That’s culture.

So I’ve been pondering how to build a culture of analytics driven decision-making and, of course, how to measure whether you’re successful. Now while my particular problem – building the proper sort of enterprise for digital transformation – may not be the standard one, the problems and challenges of building culture and measuring culture are hardly unique. And since this isn’t my specialty at all, I’ve been trying to read up on common approaches.

By and large, it’s pretty disappointing.

From a building culture perspective, so much of the literature seems to focus on top-down approaches: ways that a senior leader can communicate and encourage cultural change. That’s clearly important and I’m not going to dispute both the need and the challenges around top-down change. But this type of approach often seems to degenerate into leadership self-help advice or cheerleading, neither of which seem likely to be useful. Nor am I much impressed by the idea of carefully crafting a mission statement and promulgating it through the organization. I’ve sat in more than one excruciating mission statement meeting and all I ever got out of it was a sore butt. I’ve said before that if you have to create an innovation capability in your enterprise, you’re already defeated. And if you’re looking to a carefully crafted corporate mission statement to provide a shared vision, you’ve already lost your way.

I wasn’t much more impressed with attempts to measure culture.

It’s hard, obviously.

Most approaches seem to rely on survey instruments and involve categorization of the organization into pre-defined types (e.g. hierarchical) or score a number of separate variables (e.g. perceived alignment of vision). This seems like a close corollary to personality measurement tests. Lots of people love these tests, but they don’t strike me as particularly rigorous.

With regards to categorization, in particular, I’m skeptical that it means much and very skeptical that it might be a useful trigger to action. I can see value – and perhaps even triggers to action – in learning that there are differing perceptions of organization mission or differing perceptions around how aligned the organization is. It’s easy to fool yourself with a view from the top and this type of cultural survey instrument might help correct attitudes of corporate complacency. It’s much less clear to me, however, that such measurement would be a useful part of a continuous program designed to improve analytics (or any other) culture.

I’d very much like to see measures of culture that are behavioral and amenable to continuous measurement, but at least so far I haven’t come across anything very interesting.

It may be that culture is one of those things that is so challenging to measure that the subsequent benefits in clarity and decision-making – at least outside the world of academia – aren’t worth the investment. Perhaps the best way to measure culture is by digital success. If it works, it’s working. You could easily take that point of view from this extended series on digital transformation and I don’t think it’s implausible.

Maybe we just haven’t found the right methods.

Or maybe I just haven’t read the right articles. Indeed, if you have thoughts on either of these issues (how to build or measure culture) or can point me to interesting research, I’d love to hear about  it.

Right now, I have more ideas about how to build analytics culture than I do how to measure your success building it. Some of those ideas are implicit in the recommendations I’ve been making about the integration of analytics, the use of voice-of-customer and the role of experimentation, but they don’t end there.

In my next post, I’ll explain some concrete actions the enterprise can take to build analytics culture and why I think they are both more practical and more impactful than most of what passes for culture building.

At the same time, I’m going to be thinking more about measuring culture and I hope – eventually – to have something interesting to say about that too.

 

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