How to Use Data to De-risk Innovation


When most people think of innovation, they associate it with freewheeling words like “creativity,” “imagination,” and “brainstorming.” For most builder executives I speak with, innovation also conjures less-fun adjectives like “uncertain,” “expensive” and “risky.” The image of throwing spaghetti against a wall comes to mind.

It doesn’t have to be that way. A better way begins with how we define innovation. I define innovation as the process of devising solutions that address unmet customer needs. Just like any other process, the innovation process can be designed, structured, controlled and optimized for efficiency. And to do that, we need boring stuff like data, science, formulas and math.

If you asked an engineer to design or optimize a process for anything from building a home to customer service to paperwork approval, she would very likely employ a construct such as Lean Six Sigma (LSS), a data-driven process design strategy aimed at minimizing waste and variability.

[Related: Navigating new business with data]

The core steps in LSS process development are Define, Measure, Analyze, Design and Verify (DMADV). Applying LSS to the innovation process results in what I call Engineerovation—the engineer’s way to innovate:

  • Define. Define the customer and the “job” they’re trying to gfet done, then comprehensively and clearly collect from all the customer needs (success metrics) associated with that job.
  • Measure. Use quantitative surveys to measure the importance and satisfaction level of the customer to calculate the degree to which the needs are unmet (high importance and low satisfaction).
  • Analyze. Statistically evaluate the quantitative needs data to discover the resultant innovation opportunities (highly unmet needs) and develop an appropriate innovation or growth strategy.
  • Design. Utilize the data model to design or improve products, services, processes or business models to address the unmet needs.
  • Verify. Validate the new solution with customers to measure its improved effectiveness in meeting customer needs.

Utilizing data and an engineering approach, we can turn traditional innovation, which is often a game of roulette, into a stable system of highly predictable innovation inputs and outputs.

[Related: How a zero-injury culture endangers construction workers]


  • Your innovation investment producing growth-stock returns, with treasury-bond risk.
  • Being able to actually predict and plan for your future innovations.
  • Not having to place dozens of innovation bets and hope for one jackpot to pay for all the losses.
  • Being able to finally do away with the wasteful and self-deceiving “fail fast and pivot” nonsense that so many unproductive innovation programs use as a crutch.
  • Spending far less money on innovation, while dramatically reducing your time to market.
  • Knowing with high confidence before you launch a new service, enter a market or make an acquisition, that the investment will pay off.

This nerdy, data-driven approach to innovation may not seem as exciting as the traditional “innovation roulette” you’re used to, but it will absolutely transform your innovation results. And if you ask me, boring success beats exciting failure any day.



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