When most people hear the words ‘hacker’ or ‘hack’ they probably conjure up an image of a teenager trying to hack into a government mainframe or a hardened criminal trying to get into a banks computer network. Such an image is of course only one interpretation of those words and over the weekend TechCrunch hosted the Disrupt NY Hackathon where over 160 teams of hackers and developers have 24 hours to ‘hack’ a product. In this context a ‘hack’ is, according to Wikipedia, “a solution to a problem, doing a task, or fixing a system that is inefficient, inelegant, or even unfathomable, but which nevertheless (more or less) works“.
Each team gets 60 seconds to demonstrate their hack and sell it to the judging panel. What the event creates are some great insights into new and novel opportunities that often build on or bring together existing components, often in the area of information or big data. The winner of this year’s event was Rambler which brought together FourSquare check-in data with payment details from your credit card to show you where you had spent money, and on what over time using Plaid.
As individuals, unless you find yourself in a night out akin to the movie ‘The Hangover’, you could say that there is little benefit from watching the cash fly off of your credit card over time, overlaid on a map. If you’re a business, however, then this kind of visualisation has great potential and shows a real application for big data. If you can bring forward an aggregated, segmented, multi-dimensional data set of spend over time then there are many uses.
You could be a commercial landlord who is about to negotiate a lease extension with a retailer. If you understood the financial transactions that flowed through your lessee then you may be able to use this to drive a higher rent.
If you are a retailer then you would be able to see the flow of transactions in your neighborhood through time, and perhaps either optimise your opening hours or the mix of products you offer to better mach the footfall.
There are many potential opportunities and Rambler is one example of how a tool that took less than 24 hours to create can give insight using ‘big data’ and why so many companies are trying to capture or create their own big data-sets.
In the financial world we are seeing the existing players such as Mastercard and Visa, being attacked from multiple directions. The banks are offering apps to allow for direct payments on the move. Many existing brands, such as mobile phone networks such as O2, trying to extend their brand into the payment space and in particular the mobile payment space. We are also seeing the likes of Paypal growing in prominence and offering alternatives to the traditional intermediaries.
It is potentially lucrative space as not only can they charge a fee for acting as an intermediary, increasingly they have the opportunity to tag the data with location, time, spending trend, then anonymise their data and use it to provide valuable insights to drive business intelligence. As of today this largely focuses on helping advertisers target their messages, however, over time this will expand out to a wider offering such as the two that I highlight above.
In the world of big data, the data is but one element of the solution, what is required to open up the market is, as Rambler demonstrates, intellect based analytics, and visualisation. Only when we manage to bring together these three elements will we stand a chance of unlocking the real power of ‘big data’ and those who see the potential of commercial innovation such as this will undoubtedly have an advantage for a period.
