This is my dumping ground for quotes and other stuff relating to the wonderful world of digital & communications.
a program called GLEAM (Global Epidemic and Mobility Model) that divides the world into hundreds of thousands of squares. It models travel patterns between these squares (busy roads, flight paths and so on) using equations based on data as various as international air links and school holidays. The result is impressive. In 2009, for example, there was an outbreak of a strain of influenza called H1N1. GLEAM mimicked what actually happened with great fidelity. In most countries it calculated to within a week when the number of new infections peaked. In no case was the calculation out by more than a fortnight.
As we acquire more data, we have the ability to find many, many more statistically significant correlations. Most of these correlations are spurious and deceive us when we’re trying to understand a situation. Falsity grows exponentially the more data we collect. The haystack gets bigger, but the needle we are looking for is still buried deep
Society might be well served if the model makers pondered the ethical dimensions of their work as well as studying the math, according to Rachel Schutt, a senior statistician at Google Research.
“Models do not just predict, but they can make things happen,” says Ms. Schutt, who taught a data science course this year at Columbia. “That’s not discussed generally in our field.”
Models can create what data scientists call a behavioral loop. A person feeds in data, which is collected by an algorithm that then presents the user with choices, thus steering behavior.
the role of the campaign pros in Washington who make decisions on hunches and experience is rapidly dwindling, being replaced by the work of quants and computer coders who can crack massive data sets for insight.
Online, the get-out-the-vote effort continued with a first-ever attempt at using Facebook on a mass scale to replicate the door-knocking efforts of field organizers. In the final weeks of the campaign, people who had downloaded an app were sent messages with pictures of their friends in swing states. They were told to click a button to automatically urge those targeted voters to take certain actions, such as registering to vote, voting early or getting to the polls. The campaign found that roughly 1 in 5 people contacted by a Facebook pal acted on the request, in large part because the message came from someone they knew.
BUSINESSES avidly mine data to improve their efficiency. Non-profit groups have plenty of information, too. But they can rarely afford to hire number-crunchers. Now a bunch of philanthropic geeks at DataKind, a New York-based charity, are helping other do-gooders work more productively and quantify their achievements for donors, who like to see that their money is well spent. A typical DataKind two-day “hackathon” last month in London attracted 50 people who worked in three teams. One pored over the records of Place2Be, which offers counselling to troubled schoolchildren. Crunching the data showed that boys tend to respond better than girls, though girls who lived with only their fathers showed the biggest improvements of all. The charity did not know that.
In this world of huge and big data, you won’t be able to program machines for everything they should know,” said Ms. Rometty. “These machines will have to learn what is right, what is wrong, what is a pattern.” It is the third wave of computing, she said. At first, computers could count. Today, they are programmed to follow “if this, then that.” Next they will need to discover and learn on their own, she said, not just as a search engine, but proactively.
Using data amassed from loyalty card usage, Kroger is now offering personalized discounts in-store to give shoppers money off their favorite brands in real time. The store has begun to offer the service to its Kroger Rewards scheme customers, providing a price for certain products depending on each member’s individual shopping history… There is no need to print off and scan and the coupons in-store as the prices are automatically added to each users’ Rewards card and applied to the final bill at checkout.
In June 2011 we analyzed a sample of approximately 500 companies across Europe to assess how big data analytics favor the productivity growth of companies. The findings are quite compelling: companies at the frontier of big data analytics, (top 10 percent) are able to generate between 7-10 percent better productivity than others. This finding is robust across industries and across company size, age, etc
consider the case of Belgian telecom companies fighting for their share of triple play. Typically, companies would predict sales based on their past sales performance and marketing effort activations. Sales data from competition would be a good add-on to better prediction. However, data released from costly market research or from public sources, is only released quarter by quarter, thus a few months after sales have been processed. Looking at data retrieval in real time for all telecom operators—the intensity of branded search queries on Google as well as the amount of social mention valence on sites such as Twitter, Facebook and others—we found that the correlation with operator’s sales is strongly positive. Further, the correlation increased when sales was matched with search queries and social mentions captured six weeks in advance of sales.7 This time lag suggests that online data is leading and a powerful indicator of future sales for a company and their competitors
In advanced, “digital” oil fields, instruments constantly read data on wellhead conditions, pipelines, and mechanical systems. That information is analyzed by clusters of computers, which feed their results to real-time operations centers that adjust oil flows to optimize production and minimize downtimes. One major oil company has cut operating and staffing costs by 10 to 25 percent while increasing production by 5 percent.
Leading retailers are monitoring the in-store movements of customers, as well as how they interact with products. These retailers combine such rich data feeds with transaction records and conduct experiments to guide choices about which products to carry, where to place them, and how and when to adjust prices. Methods such as these helped one leading retailer to reduce the number of items it stocked by 17 percent, while raising the mix of higher-margin private-label goods—with no loss of market share.