We Assign Moral Charge to Data

Introduction

Organizations organize their data and the strategies they inform into mental models. It's a good thing to propose, teach, and direct such mental models.

But that doesn't mean models are "good" or that disagreeing with them is "bad". All mental models are simplifications. As such, all mental models are wrong; some are useful. And while we as business leaders move our organizations forward with such simplifications, we also ignore data and stifle innovation using our mental models.

One way we do that is by deeming the models "good" and challenges to those models "bad". This is especially stifling to innovation when the challenges arrive in the form of data that contradict our mental model. The Innovation Hypothesis is "We Assign Moral Charge to Data".

We Need Mental Models to Move Organizations Forward

Mental models are critical. They allow us to simplify out noise and move together as a large group of people. The world our organization lives in and markets it competes in are too complex to avoid such reduction to simpler concepts that fit into collective mindset. For example:

• "Customers want us to integrate better AI into their workflows so we're investing more heavily in AI integration".
• "Customers are unhappy with how long it takes us to adjust to changes in their enterprise IT so as a company we'll work to respond faster".

Both of those mental models are based on data and are almost certainly true for some companies. It's a good thing when such companies sum up simply where they want to go and communicate strategy to the employees who can make it happen.

It's not hard to imagine a company that won't improve in any way because they don't discover and communicate mental models like those.

But Mental Models Are Not "Good"

But that doesn't mean the model is "good" and any data that reconfirms it is "good". Especially when we as business leaders take that to imply that any contradictory data must be "bad". If your sales, strategy, and engineering teams are doing their jobs, someone is going to say something like:

• "Our customers are asking for more AI integration merely for the sake of AI integration. We won't meet the need if we don't understand what AI helps and what AI hurts customer workflows."
• "The reason our customer IT enterprises are asking for us to change is because less capable competitors are convincing them to change in ways that will ultimately cost our customers more. We're actually facing a competitive challenge, not an engineering or process challenge."

Laid out like this, any business leader can imagine that these are correct, especially if they have data to back them up.

But for various reasons, organizations frequently don't respond well to challenges to mental models.

Differences between the data and the mental model are not "bad"

There's no ethics or morality involved in seeing the mental model in the data or not. Company executives analyzing these hypothetical examples see readily that there's no "good" or "evil" in a mental model or a challenge to it.

But in general, for various reasons, organizations don't respond with such open minds. Organizations are highly political organizations.  They naturally attempt to discover who is "on board", who "gets it".  This is not strictly irrational behavior.  Organizations don't move and change (as they must) unless those most aligned with strategy are most empowered to act.   Executives and their organizations are right to deal effectively with resistance, incompetence, and a range of other factors in their employees that prevent needed change.

But facts are stubborn things, and strategies that don't consider them are destined to fail.  While organizations are correct to identify personnel willing to align with strategy and back needed changes, they are unwise to prioritize the facts and data they collect according to whether it aligns with their strategy.  

But many organizations do just that.  In the same way they sideline people unwilling to align, they sideline data that don't align to the presumptions their strategy is based on.

Innovation Requires Radical Regard for the Data

...Not Just "Seeing Things Differently"

Innovation isn't seeing things differently nearly so much as having a radical regard for the data. That regard sometimes brings us into conflict with shared mental models.

Yes, it is often said that people who innovate are able to "see things differently". But true innovators are not just those who see things differently because it's possible to see things differently and simply be incorrect. True innovators are the ones who see things differently because they're observing the data signals that others are not, especially when the reason others aren't interpreting the signals that way is because they have a preconceived bias about what they should discover.

Thus, innovation is about a radical regard for the data. By "radical", I mean divergent from common opinion, common sense, or shared mental model. It's the ability to go against the grain and be right about it.

Innovation Happens When We Adapt Our Mental Model to Data It Doesn't Currently Explain

True innovation occurs when we as a group are able to recognize that our shared mental model differs from what the data indicate in a significant way. I emphasize "in a significant way" because no data will perfectly fit the mental model we share. Innovation should be concerned with deviations from data that "move the needle", deviations that show true better ways of doing things to meet customer need.

A Note on People Who Disrupt for Bad Reasons

Even though no business mental model should classify people into the "ethical" ones who buy into it and the "unethical" ones who don't, there are some ethics involved here. Some people exaggerate the importance of noise or disrupt the mental model simply because it does not benefit them. It's a poor response that can derail progress for everyone.

Because there are at least two types of disruptors in organizations--those who are innovating and those who are disrupting for the sake of it--decision makers must exercise sage judgement and discern between the types. The best practice is to examine the data and trace the conclusions of everyone who recommends a change in direction before deciding one of:

1. If the disruption is well founded, either:
   • Modify the mental model and organizational strategy or
   • Allow pilot investigations to continue without disrupting the larger strategy
2. If the disruption is not well founded, stop its disruption to current strategy

Conclusion

Innovators within an organization have a special gift that, treated properly, can springboard growth. But they also cause disruption. That disruption needs to be properly managed to avoid its damaging current business, but it can't be treated as either "good" or "bad" as it typically is.