Innovation eats data for breakfast

I read this amazing article from Prof. Viktor Mayer-Schönberger and Thomas Ramge on Harvard Business Review. Follow the link here –

They have made an excellent case for a data driven innovation. Citing examples of how Google, Apple and Amazon are using data to further innovation and outsmarting not just their existing competitors but also the startups, they have made it very clear that the future innovation will be owned by enterprises which are able to leverage AI in generating insights from the data and not just rely purely on human ingenuity.

I am sharing here the two adjustments, they mentioned about, that enterprises need to make:

First, they need to reposition themselves in the data value chain to gain and secure data access. Second, as innovation moves from human insight to data-driven machine learning, firms need to reorganize their internal innovation culture, emphasizing machine learning opportunities and putting in place data exploitation processes.

Now, one interesting point to ponder about is – what do we mean by data really ? In all of their examples it means the data generated by users which is being used by large corporations to generate insights for improving their products or services.

What does it mean for companies whose product or services are not consumed or delivered online ? eg. Pharmaceuticals

In that case they need to look at the data that is publicly available. Thankfully, there is a lot of data that is available publicly eg. information on clinical trials, research publications, disclosures to regulatory bodies, reviews from regulatory bodies, data from different scientific congresses across the world, theses from universities, patents from major patent bodies globally, data from major global regulatory bodies etc.

In the context of the second adjustment above, enterprises need to leverage AI to automate not just the collection and curation of data but also the generation of insights, specific to key processes, from the data. Entire Drug Development process is very long and complex, with a lot of tasks / processes which are repetitive in nature or involve a lot of manual content reviews at each step. It is up to the enterprises to empower their teams to step up the value chain from doing manual analysis of data to being the domain specialists who can partner with Data Scientists to come up with good training data sets, to validate the output of these algorithms and to be the data quality supervisors.

In the end, it will only improve the overall satisfaction of employees as they will free to solve real problems instead of clerical tasks, which ultimately leads to happier and more productive workplace.

P.S. The title (Innovation eats data for breakfast) is just a pun on the famous “Culture eats strategy for breakfast”, a phrase coined by the legendary Peter Drucker.

Looking at the world’s best restaurants for Innovation

I came across this article on HBR today which looks at the world’s best restaurants to find out how they balance Innovation and Consistency.

Here’s the summary :

Despite being able to charge hundreds of dollars for a meal and being fully booked months in advance, top restaurants often still have a hard time turning a profit. And they face an added challenge of maintaining flawless consistency, while simultaneously being innovative and cutting-edge. This requires dedicated time and space for research and experimentation, as well as a thorough process for both iterating on and standardizing new inventions. Examples of restaurants that have made  both the Michelin Guide and 50 Best Restaurants of the World list show how they encourage creativity and learning beyond the leadership or lab teams, and generate, refine, and standardize ideas.

The original article is here –

All the projects follow a specific development process, alternating between collective ideation or feedback and focused work by a small team. For restaurant dishes, the development team will quickly prototype and iterate through numerous versions of the dish and its components, either in the lab or if a lab is not available, in the main kitchen during slow hours. The trials can go over for months as numerous variations are tested in a race against seasonal ingredients.

This just goes on to prove that Consistency and Creativity are not mutually exclusive – restaurants need to be both at the same time. Innovation which is not a result of a set process will  soon become unsustainable – one may be able to innovate even without process at times but no one can repeat the feat a number of times. That consistency is possible only with a process.