Silicon Valley is a great TV serial, I am not sure if you’re a non-tech person, but for a nerd like me, it has plenty of tech-jokes and good lesson in each of its episodes. I am going to tell the story of what interesting stuff I learnt from season 1 episode 3, titled ‘Articles of Incorporation’.

There is a part whenever a Venture Capitalist named Peter Gregory mumbled about sesame seed on top of each bread sold by Burger Kings. He asked his assistant to buy one of each offered by burger kings and found that every bread / burger has sesame seed on top of it. Burger King selling bunch of bread with sesame seed on top every day, so he tried to find more information about sesame seed especially its price. He want to know which factor will affect its price and how he can take benefit out of it. He come to one of the possible thing: cicada.



By (probably) doing data gathering and aggregation helped by his assistant, he found that top 3 of sesame seed producing countries are Myanmar, Brazil, and Indonesia. Indonesian sesame seed future prices is low, and he knows from the fact that unlike Myanmar and Brazil, Indonesia has no cicada population. An interesting fact about cicadas is its periodical life cycles.

Magicicada spp. spend most of their 13- and 17-year lives underground feeding onxylem fluids from the roots of deciduous forest trees in the eastern United States.[3] After 13 or 17 years, mature cicada nymphs emerge at any given locality, synchronously and in tremendous numbers. After such a prolonged developmental phase, the adults are active for about 4 to 6 weeks.[4] The males aggregate into chorus centers and attract mates. Within two months of the original emergence, the life cycle is complete, the eggs have been laid and the adult cicadas are gone for another 13 or 17 years. —

Within that story, cicada variant lives in Myanmar and Brazil has each 13 and 17 years life cycles. So there will be a time when both countries have cicadas plague and attacking sesame seed in Myanmar and Brazil that will cause supply of sesame seed dropped and then the price will go up. Indonesian sesame seed future price (as per present time) is still low, so if he purchase it now and the world sesame seed price going high due to cicadas plague in Brazil and Myanmar within few years from now – even only by 10% — he will gain margin of hundred of millions USD.

Investor use data to calculate future revenue, to decide if he will invest on something or not. He will try to retrieve as much data as possible, aggregate, analyze, and make decision. If an investor made decision based on what he ‘heard’ from others without looking into real data, then most likely he is gonna make wrong decision.

In the past, people had to conduct a complex procedure in order to retrieve data. Collecting data was expensive task to do. But with interconnected world as today, lot of data are available and accessible through internet. If we are Peter Gregory, we might retrieve more details about sesame seed from FAOSTAT website.



We can download the data, do some analysis, and get the result to make decision.



The trend of open data and big data recently also helped data collection process, people can retrieve data from various sources in Internet. Lets say opengovernmentdata, or, projects for Open Government Indonesia (OGI). People actually can use various data from these sources for various different purpose other than businesses, for example: education, food and nutrition, improve the welfare of society, etc. I believe with the trend of “everywhere data” these days, people can take benefit of data themselves for their own problem set. People can make a decision based on provided data set as in business person analyze data for their future product. People can decide, why should they choose a specific stock option, how to choose insurance company suitable for their family, should they use train or car to office in an afternoon rainy day, track their expense, etc etc.

However, as Jeremy Howard said on his recent interview,

Data science is a very sexy job at the moment,” he says. “But when I look at what a lot of data scientists are actually doing, the vast majority of work out there is on product recommendations and advertising technology and so forth.

So it is time for data scientist and tech-savvy to build something that can help people not only by giving recommendation of a product or advertisement, but also to develop an application that is more useful for people’s life.


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