Propagandizing for Analytics Success
If an advanced analytics project is going to succeed you gave to market it -- hard.
- By Steve Swoyer
- April 11, 2016
When you're budgeting for that shiny new advanced analytics initiative, be certain to include an all-important extra line item -- for marketing-related expenses. If an advanced analytics project of any kind is going to succeed, particularly an ambitious effort to retrofit and restructure an organization in order to make it "data driven," people need to know about it. They don't simply need to know about it, either: they need to understand what it will mean for them and how it will affect them.
Informing users might seem like a simple issue of communication and education. If an advanced analytics project is going to succeed, however, communication and education must be embedded in -- and in a sense subordinated to -- marketing. "This is is so overlooked and once you get knee-deep into [an advanced analytics effort], all of the [successful adopters] say, 'I wish I had known this at the beginning, because I would have spent more time naming my group, communicating what I do," says Jack Phillips, CEO and co-founder of the International Institute for Analytics (IIA).
Phillips cites IIA's Analytics Maturity Assessment, which -- among other factors -- rates companies on their ability to communicate the strategic purpose of data-driven decision-making to internal constituencies. "One of the ways you get very penalized in our model is ... if you have [set] unrealistic expectations through your communications, and [if] you dramatically over-promise and under-deliver. That is quite penalizing in our model because that means you aren't at all mature."
This is at once a question of communication -- or, as Phillips says, of "getting [your messaging] right and making sure it matches reality" -- as it is of education. In other words: it's a marketing problem.
As a rule, people in organizations dislike -- to put it mildly -- change. When radical transformation along the lines of an advanced-analytics retrofitting is proposed, it's more than likely to frighten people. To cite just one example, human subject matter experts (SME) might have realistic concerns about whether or not the equivalent of an advanced-analytics expert system could eventually replace them.
Right now, the twin spheres of data-driven and what we'll call "agential" decision-making are on a collision course. The former is a much more powerful force than the latter, however; in the months and years to come, it will continue to encroach into -- and gobble up -- the agential, or data-informed, sphere of decision-making.
It makes complete sense that a rank-and-file employee might fret about the possibility of replacement -- or bemoan the loss of human influence or agency. In such cases, she can and will form with other like-minded people to form pockets of resistance. The upshot, experts stress, is that people don't just need to know how a proposed change will impact them, they need to be convinced it's a good thing, on balance, both for them and for the organization of which they're a part.
Marc Demarest, a research analyst with information management consultancy Noumenal Inc., goes this one better. Merely marketing an advanced analytics project isn't enough, he maintains.
Instead, Demarest argues, organizations must effectively propagandize to prepare the way for analytic success. "It's a question of how you talk [to people] about their groups and the [role] of [their] groups in the corporation. It's important to be clear about what you're doing or when you're going to deliver what. It's important to manage not just expectations but perceptions in an organization."
Managing perceptions is a problem of communicating, educating, and, yes, propagandizing, Demarest argues -- and there isn't anything wrong with this. He cites his own analytics advisory work with several prominent companies -- including a major manufacturer of agricultural equipment and a prominent academic research institution based in the Northeast -- as cases in point.
"One of the things that has struck me about every single one of the advisory clients I've worked with is that they don't even have basic definitional models in place, so they allow what used to be called 'business intelligence' to become 'analytics' in some cases. Alternatively, 'analytics' is defined only as a black box behavioral prescription," Demarest argues, noting that -- in the latter case -- the purpose of an analytic retrofitting remains opaque to internal constituencies.
"A culture begins with 'cave drawings,'" he continues, invoking the metaphor of primitive cave drawings, which capture and depict the foundational ideas, priorities, and truths that are important to -- and distinctive of -- a culture. These cave drawings must appeal to people on a personal level. They must capture the imagination and, to the degree possible, the allegiance of internal constituents.
"The question is, what are your basic cave drawings? How do you [market] those cave models to everyone so that everybody's talking about the same words?"
About the Author
Stephen Swoyer is a technology writer with 20 years of experience. His writing has focused on business intelligence, data warehousing, and analytics for almost 15 years. Swoyer has an abiding interest in tech, but he’s particularly intrigued by the thorny people and process problems technology vendors never, ever want to talk about. You can contact him at [email protected].