I read this amazing article from Prof. Viktor Mayer-Schönberger and Thomas Ramge on Harvard Business Review. Follow the link here – https://hbr.org/2018/02/are-the-most-innovative-companies-just-the-ones-with-the-most-data
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.