I had a chance to attend MassChallenge Awards event tonight at Boston Convention Center. For those of your who is not familiar, MassChallenge is global startup competition and acceleration program. You can get more data about MassChallenge here and by visiting their website. What is distinguish MassChallenge from many others similar program is the fact it is one of the largest non-profit accelerator created with the vision to build global network of value creation programs.
MassChallenge Award event gathered few very interesting speakers. Among others, Mass Governor Deval Patrick, Google Chairman Eric Schmidt and Uber CEO Travis Kalanick. You can get more details about award event here. To me, the presentation of Uber’s Travis Kalanick was one of the most impressive. Travis talked about Uber development in Greater Boston as well as demonstrated few very interesting data points about Uber and Boston.
I want to share with you few slides and examples from Uber presentation. The first one shows a snapshot of active Uber rides at specific moment of time in Boston.
The following one shows drivers efficiency distribution
The next one shows median time (in minutes) you need to wait to get Uber car.
The last and the most interesting shows data about arrivals of cars at Boston Fenway over the course of RedSox season.
These slides reminded me of an article from Uber’s blog I had a chance to read few years ago, Uberdata: how prostitution and alcohol made Uber better. Yes, I know, it’s quite an unusual topic, at least for this blog. Nevertheless, I think this is a must read article for anybody who deal with product lifecycle software these days and here is why…
Uber folks are running some very interesting analysis of data over the course of Uber lifecycle. Think about Uber as a product with its own lifecycle. Uber is gathering information about Uber customers, requirements, environment and product experience. After that, Uber folks are running some very interesting analysis of data related to Uber in a specific city and multiple cities. Here is a very fascinating quote from Uber blog:
This finding is a perfect example of the fascinating insights you can get when you combine big, seemingly disparate datasets. By trying to figure out how to predict where to position our cars, we got a peek at the ebb and flow of the life and crimes of San Francisco. Expect more of these kinds of posts in the next couple of weeks.
Article describes a very interesting story of correlation between Uber rides, welfare payments, population dencity and social activity in the city. The last includes statistics by prostitution crime by week/hours.
The approach Uber is taking is getting more and more popular these days. It made me think about completely different way of looking on every product. Think about product lifecycle. The majority of functions covered by PLM today are related to management of engineering and manufacturing planning data. However, step out of traditional product lifecycle mantra and think about experience and product data. Think about combining these data sets together. PLM tools should look on the product and how improve product experience. We are not using data efficiently today and it resulted in suboptimal decisions made by manufacturing companies and bad experience.
What is my conclusion? PLM companies need to think how to move from the position of “just capturing data” to the position of using data proactively to manage product lifecycle in a very smart way. It includes capturing every bit of information about product experience, customers, environment and translating it into meaningful actions. It will start a new era of PLM managing smart product lifecycle. Just my thoughts…
Best, Oleg
