Month: December 2017

The need for labs …

(img source: https://www.pinterest.com/pin/89157267601160497/)

It is becoming increasingly evident that companies should be establishing labs inside their entities, for exploring new deep tech independently.

  • The labs should have the autonomy to go far-out into futuristic new deep tech without having to worry about current limitations.
  • The research function should be independent and should encourage creativity.
  • To a large extent the labs work should not have time pressures.
  • Productising these research ideas would be the key to differentiating the companies offerings in this hyper-competitive space.
  • These labs should not be led by architects or EMs. They should be led by people who have experience in doing research, preferably PhD.
  • Research rigour is important.

Some companies have been doing this for a very long time – Mercedes, Airbus, Sabre etc. Ixigo Kitchen Sink is a classic example from a few years ago. I am hearing of more newer companies starting to do this – Amadeus Labs, Rivigo labs etc.

It would be refreshing to see this happening in all the unicorns. For instance, I would imagine Swiggy would benefit immensely — so much funky stuff can be done on IoT, route planning, kitchen optimization etc. Similarly Go-MMT does a lot of research along with the day-to-day work. I believe that this is not the right approach. You should separate these two out – for best optimality. Else, engineers and PMs are permanently at a conundrum to see which is more important – long term research drivers or short term revenue drivers.

What do you guys think?

Small data

We keep hearing about Big data everywhere – sometimes in places where we do not even expect to here it. I was listening to the latest Trailblazers podcast from Walter Isaacson, and he just casually dropped this nugget.

Small data is the capturing of the small, subtle nuances of a customer. A lot of times, these small seemingly insignificant subtle pieces of information lead to huge product insights.

While extracting big data, and distilling data out of it is important – it is still generalisation. It is amortised data. It is a collective. It is very important for PMs to observe customers at close quarters on a regular basis.

In my three years of observation, being a product leader, I have seen that insights distilled from big data, can only result in incremental improvements.

To get 10X improvements, we need to observe and incorporate these behavioural, often times visual, subtle insights. These are few in number – viz small data.