When Uber decides to launch a service in a new city or when they are assessing demand in an existing city they use crime data as surrogate to measure neighborhood activity. This measurement is a basic input in calculating the demand. There are many scenarios and applications where access to a real dataset is either prohibitively expensive or impossible. But, a proxy is almost always available and it is good enough in many cases to make certain decisions that eventually can be validated by real data. This approach, even though simple, is ignored by many product managers and designers. Big Data is not necessarily solving the problem of access to a certain data set that you may need, to design your product or make decisions, but it is certainly opening up an opportunity that didn’t exist before: ability to analyze proxy data and use algorithms to correlate them with your own domain.
As I have argued before, the data external to an organization is probably far more valuable than the data that they internally have. Until now the organizations barely had capabilities to analyze a subset of their all internal data. They could not even think of doing anything interesting with the external data. This is rapidly going to change as more and more organizations dip their toes in Big Data. Don’t discriminate any data sources, internal or external.
Probably the most popular proxy is the per-capita GDP to measure the standard of living. The Hemline Index is yet another example where it is believed that the women’s skirts become shorter (higher hemline) during good economic times and longer during not-so-good economic times.
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| Source: xkcd |
Proxy is just a beginning of how you could correlate several data sources. But, be careful. As wise statisticians will tell you, correlation doesn’t imply causation. One of my personal favorite example is the correlation between the Yankees winning the worldseries and a democratic president in the oval office. Correlation doesn’t guarantee causation, but it gives you insights into where to begin, what question to ask next, and which dataset might hold a key to that answer.This iterative approach wasn’t simply feasible before. By the time people got an answer to their first question, it was too late to ask the second question. Ability to go after any dataset anytime you want opens up a lot more opportunities. At the same time when Big Data tools, computing, and access to several external public data sources become a commodity it would come down to human intelligence prioritizing the right questions to ask. As Peter Skomoroch, a principal data scientist at LinkedIn, puts it “‘Algorithmic Intuition’ is going to be as important a skill as ‘Product Sense’ in the next decade.”
(Cross-posted @ cloud computing)
