The Mindset Shifts For Data-Driven Innovation

by Barry O'Reilly

How many times have you been on a mission…

spending months building the mythical product idea you thought was going to save the business… 

that cost hundreds of thousands, or even millions to launch… 

only to put it out there and find out nobody wants it? 

I’ve seen and been on more of these projects than I care to remember. It sucks. 

So why do we keep doing this to ourselves? Why do we keep building supposed silver-bullet solutions hoping they’re going to provide a massive breakthrough and create revenue beyond our wildest dreams?

You could point to playing it too safe, timing issues (too slow or too early), or starting with the wrong customer. But really it comes down to a leadership conditioning that limits how individuals and teams make innovation investments, that must be unlearned

The sluggish pace and frequency of most organizations’ new product development is leaving them in the dust of those with the necessary systems, capabilities, culture, and tools to continuously test, build, and launch innovative ideas that shift paradigms and create business growth. 

The question becomes, which side will your organization fall on?

If you want to be among the companies that succeed in the new decade, the speed and scale of your innovation has to make a massive leap forward.

What do I mean by a massive leap? This chart gives you an idea of the level of change I’m referring to:

Unfortunately, the majority of the market is stuck on the left side of this chart due to systems of work that inhibit product delivery with speed and safety. Big batches of work, siloed thinking and slow feedback loops play their part in stopping the shift in behavior and status-quo thinking. 

Everybody wants to be on the right, but most struggle to get out of their own way, bogged down by slow decision-making, conflicting goals, inability to embrace uncertainty, and the perceived reputation risk of failure.

But there is a way out. And it’s far easier to start—and faster to scale—than you might think. There’s an actionable system I use to help leaders seed, scale, and sustain innovation throughout their organization. 

It’s not complicated, yet it can feel uncomfortable. But as you start peeling off the layers of legacy conditioning, you start to see a measurable uplift in your performance. 

By Thinking BIG, starting small, and learning fast, you can rapidly achieve extraordinary results in new product development you may have thought impossible. 

In my experience, there are three critical focus points holding companies back from creating the effective innovation strategies they need… not just to stay alive, but to thrive.

 

Shift 1: From Solutions Focus to Problems Focus

Smart people love to jump straight to solutions— “THE” answer to complex problems. Unfortunately, limiting the number and variety of options you’re willing to consider before choosing a solution is a classic leading indicator for product failure. 

So many leaders hand down solutions to be delivered rather than problems to be solved. It’s the classic investment trap that clients continually fall into. 

And most often it’s leaders getting wedded to the one solution they’re most comfortable with. In essence, it’s like looking up to the sky and picking a single star to find life. They tell teams to build the rocket to go there and only there, and expect to succeed.

This approach glosses over the most important concerns: 

  • Have we really defined the problem? 
  • How do we know it’s a problem worth solving? 
  • How will we know we’ve solved it? 
  • What potential options could take us there? 

So how do we start to shift focus from a single solution to making sure we solve a worthy problem? 

The Problem-Assumption Matrix I developed with Jonny Schneider forces people to express the problem and options for solutions, as well as break down their underlying beliefs and assumptions.

Assumptions form the basis of what we test. Using the matrix, we can check for simply solutioning, cross-examine our assumptions, and translate them into questions for customer interviews or use them to design experiments that create a measurable result.

We first crisply define the problem we wish to solve. Then we express options for how we might, in fact, solve it successfully. This prompts us to consider a broader picture: 

  • Do we have a shared understanding of the problem, and is it one worth solving?
  • What signals would tell us the customer’s problem exists?
  • Have we considered enough options as potential solutions? Eg. having only one is not good
  • What changes in customers’ behavior that will tell us our product is a fit for the problem? 
  • What should we measure, track, and test against, as we explore the best options we believe will yield positive results? 

Shift 2: From Outputs To Outcomes

When we jump straight to solutions, we focus on Execution Risk, make implicit assumptions about the problem, and skip evaluating the Market Risk

The method to manage execution risk is to measure and monitor outputs, focusing on how long the initiative is going to take, how much it’s going to cost, and what features will be implemented—three variables that have zero impact on the success of a new innovation. 

By contrast, the two most important variables for innovation, as identified by Douglas Hubbarb in How To Measure Anything, are: 

  1. Will anybody use it?
  2. Will the initiative get canceled?

This is why entrepreneurs focus on early testing—to uncover if they have a problem worth solving and a viable solution to address it.

To shift to an experimental innovation strategy you need to step back and focus on outcomes. If we’re successfully solving the problem we’ve defined, what changes in customer behavior should we see? What percentage increase in our product usage, referrals, or workflow completion would we see?

The trick to establishing meaningful outcomes to measure in your innovation experiments is to start creating stories—imagine the future where you’ve succeeded, and describe what’s different from today. 

This practice of writing stories of success is not new. Amazon famously writes one-page press releases for new products describing the future world with that product in it—including how it will impact and change the life AND BEHAVIOR of their customers. 

I define an outcome as a change in human behavior that leads to a business impact. When we define the new customer behaviors a product will create, we simply need to model and measure them. 

Will we retain 20% more customers?

Will they increase their wallet spend by 30% with us? 

Will 40% of new customers recommend our products over others? 

When you start to envisage, model, and measure outcomes, you can test many options for solutions to see what moves you in the desired direction (or not). On the other hand, when you monitor output, your single-point solution is either done or not. Which do you think will foster a culture of experimentation? 

 

Shift 3: From Opinion to Data 

Opinions rule when we don’t have confidence in our data, because all we have left to rely on is intuition—or one person’s expertise. 

Another reason opinion often trumps data is that the highest paid person is often the one jumping straight to the solution. Hence data is not the focus of their decision-making framework.

The challenge of moving to data-driven decision-making is that it means leaders have to let go, which for many can feel like a loss of identity and personal value. They attribute their authority and credibility to their ability to make decisions, a tendency that needs to be unlearned. 

As you start measuring outcomes, you start to understand what necessary data you’re lacking. Initially, it’s uncomfortable to ask yourself why you never had it before. 

When you identify gaps in your data—plug it with your team’s best thinking. Now that you know about it, you can start to improve the accuracy as you measure it. To help shift from opinion to facts, your product needs an active learning mechanism for you to start understanding how and by whom it’s being used. 

Don’t try to have perfect numbers to start—strive for excellence and continuously improve. 

 

Getting Started With Data-Driven Innovation

Breaking the inertia to get started can be challenging. Each of these shifts—solutions to problems, output to outcomes, and opinions to data—entails unlearning much of people’s leadership conditioning. 

Outcomes are often hard to measure. Output is easy to measure. Managing risk—Market Risk particularly—to outcomes is an unfamiliar and extremely uncomfortable way to control innovation for most people.

The common default assumption is you can’t get into trouble if you stay within Execution Risk by measuring and delivering your output on time, budget, and scope (shifting outcome responsibility to the person who told you what to build).

Managing the Market Risk required for effective innovation, however, means owning the problem and outcome, identifying the data you’re missing and might need, as well as the solutions to get there.

In low-trust environments, no one wants to take on the Political, Personal, or Market Risk of innovation because they lack the skills, the support of leadership, and the systems to make it safe to fail as they explore uncertainty in the pursuit of outcomes.

So if you’re wondering why you’re not innovating at the rate, speed, and frequency you’d like—identify which risks your leaders are encouraging and your teams are seeking to manage. 

High performance teams and individuals seek to manage and feel safe to own them all. 

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