4 Invisible OBSTACLES TO DATA-DRIVEN DECISION MAKING
Data-driven decision making.
Even when everyone in the company claims to want it, data-driven decision making can still be doubly difficult to implement. The first set of challenges are all the ones you see: making sure decision makers have access to data, making sure it’s the right data for the decisions they need to make, making sure the data is in usable formats, that permission structures are correct, that data integrity is protected as changes are made, and so on.
But it’s a second set of challenges—an invisible set—that often sink organizations’ attempts to implement data-driven decision making (and, as a result, the growth strategies and transformations that such decision making supports). These obstacles are secret because they disappear whenever anyone tries to address them directly, only to reform as soon as the organization attempts to move forward again. Like a fog, they keep people from seeing the path forward.
Here are four such invisible obstacles:
Invisible obstacle #1: The Fear of delivering Bad News
If you’ve ever been challenged when bringing bad news to an executive, then it’s going to be cold comfort the next time you have to do it that the bad news will be data-driven—your boss still isn’t going to like it, and it’s still going to be you delivering it. The person whose boss who talks about wanting to know “whose throat to choke” intuitively grasps that it’s going to be the person standing closest to that data who will be most at risk if the data’s story is a bad one.
So while data may tell the truth, it does nothing to alleviate employees’ fear of delivering bad news to a boss who doesn’t want to hear it. Unless leadership is aware of the impact they have on their people’s willingness to tell the truth, this obstacle will remain hidden under frozen smiles.
Invisible obstacle #2: egos
When a job has long included making decisions about what needs to be done, and the move to data-driven decisions means that the person in that job no longer needs to make those decisions, they can perceive the change as a sign that the company has lost trust in their abilities. Management might (and probably will) sell the move as an attempt to liberate the person from meetings and politics and delays, so they can focus on execution and get more done, but it will still probably feel like a vote of no confidence when the move happens.
Something that person used to do has been taken away; people don’t like that. Eventually, everyone might come around to feeling liberated, But in the short run, especially if someone has developed an expertise with certain types of decisions, they may very well feel the move to data-driven decisions as a slap in the face, and react to the change accordingly: with confusion, fear, frustration, resentment, and/or anger.
This obstacle is hidden to leaders because their expectation that their people will react “rationally” is based on an assumption that their people have the same level of insight and confidence that they have… which they generally don’t.
Invisible obstacle #3: doubt in the future
Humans generally don’t like ambiguity, and one of the most familiar ways that we eliminate ambiguity is to put a face to things—especially things that are inherently. Companies, teams, ideas, events—when we give them faces, we make them easier to understand and accept. Apple becomes Steve Jobs; The Chicago Bulls becomes Michael Jordan; Organized crime becomes Al Capone; science becomes Albert Einstein; the moon landing becomes Neil Armstrong. This is similar in concept to the manager who wants to know who’s throat to choke, the difference being, while the manager wants a person to blame; the employee wants a person to follow.
As an example of the power of making a concept possible by humanizing it, take the video phone. The “video phone” stood a representation of a technologically advanced future for 60+ years, and though there were small advances here and there at creating that future along the way, it wasn’t until Steve Jobs—not Apple, but literally, the actual person Steve Jobs—stood on a stage and said it had arrived that we (as a society) actually moved into that future—using Apple’s iPhone to make it so.
Data-driven decision making is, by nature, at odds with the human desire to human things, making it feel uncomfortable and lending itself to the same sort of invisible, non-committal resistance that stymied the video phone for two generations.
Invisible obstacle #4: old habits
Once people get their heads around using data, they frequently expect that data to be like a Magic 8-Ball: ask a question and presto, an answer appears. People don’t like having to work with data to interpret it. They don’t like learning that depending on how they ask the question, the data gives very different answers. They don’t like finding out that the data analytics team put limits around the data they can access which prevents them from seeing what they really wanted to see, and they certainly don’t like defending decisions that data says is right when they personally disagree with them.
What people do when data doesn’t immediately simplify their lives is easy to predict: they revert to whatever they used to do. Old habits die hard. And old habits are very nearly always invisible, because people’s habits are often hidden beneath all the right language about wanting to change.
The transition to data-driven decision making is twice as hard as most leaders expect. Not only must they solve for all the mechanics of data-driven decision making, they also have to address a number of invisible obstacles, as well. That’s one of the reasons why change requires frequent, consistent communication, openness, and way more positive reinforcement than anyone would believe is necessary.