When It Comes To Innovation, Data Is Useless

By Anand Tamboli

2810
 

While hypothesis testing is the key to generate new and relevant data, experimentation culture and discipline are essential to see that through its full lifecycle. Experimentation culture is difficult to build. But once it is there, it goes a long way in the innovation journey.

With this rather mouthful title, let me assert that there is a context to it.

Do you remember the famous quote of Henry Ford? He said, “If I had asked people what they wanted, they would have said faster horses.” (Because most people had not seen a car before he started making them!) But think about it. Why should that be the case? That is when you start to see the limitations of incremental innovation and ideas around it.

In comparison, radical and transformative innovations are miles apart, especially when comparing their outcomes. Moreover, the process of selling these (radical or transformative) innovations is significantly different. Incremental innovation is often the ‘pull-then-push’ type, whereas radical and transformative innovations are exactly the opposite. They need the ‘push-then-pull’ methodology of selling the concept. It makes all the difference, along with my assertion and the title of this article. Let me explain.

Push-pull versus pull-push

With the car design Ford was working on, there was absolutely no comparison with whatever was available in the market. Therefore, there was no data to compare with and prove the effectiveness of the car. Comparing it with horses would have been meaningless and would not have yielded much. So, they ‘pushed’ the concept in the market as a radical change.

When people became accustomed to using cars, it was easier to ‘pull’ their ideas, feedback, and suggestions and improve the car model. Accordingly, future models of the cars were easier to sell and compare with older cars. ‘Push-then-pull’ therefore helped in this scenario.

Incremental innovation, on the other hand, is essentially ‘good getting better.’ And that means the basis for comparison is available. Most changes are based on customer or market feedback. You can always put forth data-centric comparisons to convince customers to buy newer versions of your product.

Now that it is clear when you can or cannot use data let us see other issues with the data-first approach in innovation.

Problems with the data-first approach

 

 

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