We at Inform believe that data tells a story, across all industries, and every week we round up the most interesting ones. This week: Facebooking the whales; cutting down on legal bills; and catching health code violators faster.
Making Facebook for Whales
Keeping tabs on and identifying the 500 North Atlantic right whales left in the world is an arduous process. Researchers must take aerial surveys, then painstakingly comb through a database of images and identify each whale individually. One biologist, Christin Khan, turned to Facebook for a solution.
We all know that the social media platform can (creepily) identify us by face. Why not develop a similar algorithm to identify right whales by their distinctive markings? Khan held a contest tasking data scientists with creating such an algorithm.
The winning team’s algorithm is able to identify whales “with 87-percent accuracy.” Such a development is useful to scientists for multiple reasons. Those who study whales’ genetics will know right away if they’ve already taken a biopsy from a particular whale, and most importantly, it will “free up researchers’ time to do actual research.”
How Big Data Is Disrupting Law Firms And The Legal Profession
While the legal system generates huge amounts of data, little of it is used beyond billing, time management, marketing, and customer relations — in other words, it’s not being used in the real practice of law. One legal research and analytics firm wants to change that.
LexisNexis and Westlaw provide a wealth of legal data. However, they are mostly used as search engines. The firm uses an analytical algorithm to draw often surprisingly insights. For example, they can scan through judges’ decisions and determine which might be the most sympathetic for a particular case. They’re also teaming up with Harvard Law School to digitize their entire U.S. case law library, and make it publicly and freely available by 2017.
Using data in such away may lead to greater efficiency, transparency, and accountability. Legal costs could be reduced, as well as the need for time-consuming appeals and retrials.
Foursquare’s Plan to Use Your Data to Make Money—Even if You Aren’t a User
While at 50 million monthly active users, Foursquare’s apps might lag behind social network behemoths such as Twitter, Instagram, and Pinterest, the company is still putting all that data to use by developing an ad-targeting and location data business.
While many apps can access your GPS coordinates, more difficult is matching those coordinates with real places. Foursquare is doing just that using the “massive database” users have helped to compile over the years. As a result, for instance, frequent fast food visitors could be identified and served up ads for fast food chains or “perhaps healthier alternatives or gym memberships.”
How big data can drive employee engagement
One software company is looking to help employers better track employee satisfaction and therefore more accurately predict when workers might be thinking about leaving.
While annual performance reviews are valuable to an extent, they may not paint a real picture of an employee’s happiness or lack thereof. The company suggests that their software, which collects employees’ self-reported moods in real-time, would be more accurate. In addition to recording moods, the software allows both employees and customers to give immediate shout-outs to colleagues.
Chicago Is Predicting Food Safety Violations. Why Aren’t Other Cities?
Like most U.S. cities, Chicago inspected its eating establishments in the traditional way: scheduling inspections by going down a list. However, this method didn’t allow for those most likely to violate health codes to be inspected first, and time is definitely of the essence in such a situation. The longer a potential violator is allowed to operate, the more of a chance diners will get ill.
To address this, Chicago’s Department of Innovation and Technology built an algorithm to analyze the city’s publicly available data and predict which eateries would be most likely to violate health codes. Not only did the algorithm identify violations faster than the traditional way — 7.5 days earlier, to be exact — it was designed in a way that made it easy for other cities to replicate.
However, only one city has followed suit so far. We should remember that even with Chicago’s model, such an endeavor requires a lot of work. However, making the code and method freely available is a vital first step.