You'll find plenty of food for thought there on the limitations and opportunities in raw data dumps.
Make no mistake, Twitter is a big data company and it's looking to get even bigger. Hence yesterday's acquisition of Gnip, added to about four or so other similar acquisitions earlier. The end goal: Make Twitter a $100 million big data business.
Yes, there is a big data revolution afoot and this time around things are getting real!
While some data will also be proprietary and closely guarded, the vast majority of data will be joined to the open data movement. Why? Because the more data people can access, the more discoveries can be made.
You might think this post is about how data and edtech are being used to improve our education system. But you would be wrong.
You've heard me sound this warning before: big data can and does lie. Most often the lies told are not intentional but rather a result of bungling the project.
Last week the American Farm Bureau Federation, a national independent farmers' group, met with John Deere, Monsanto and DuPont Pioneer to hammer out guidelines to protect farmer privacy and prevent potential market manipulations.
Rest assured that all governments are or will be using big data. The latest to stake claim to its benefits is Beijing.
U.S. politics is not the only place you'll find big data at work. It's increasingly used in countries around the world with mixed results. Indeed, it is at the core of elections now underway in India.
You've heard me and several others repeatedly say that the term big data is unfortunate because it's really not about the size of the data, but about the complexity of the computing. In other words, big data tools are not contained to usage where there are petabytes of data. Those tools are useful with just about any sized data if you're doing complex computing with it.
Before privacy protection efforts go overboard and drown the big data infant in the Sea of Good Intentions, it's important to help the public--and other big data users too--understand the many benefits to be had in big data.
There's an interesting post in ClickZ on a roundtable discussion held at ClickZ Live New York last week. The question of whether or not big data, or any data analytics at all, are working for marketers was addressed.
Bulk cash is exactly what it sounds like: oodles of money bound, hidden and smuggled from one country to another as one of the three top preferred international money laundering schemes used by criminals. We're talking about seriously big time criminals here with really big bags of cash. This is not a small time players' game. Somehow it seems fitting that big data would be the tool most likely to find bulk cash, doesn't it? Here's how that works…
Big data technologies and techniques are maturing but they are far from mature. The good news is that everything's headed in the right direction. Eventually we will master the whole shebang and things will get easier. Meanwhile, here are five trends that are driving us forward…
Bluetooth backers are pitting the technology's data gathering and sharing capabilities against Near Field Communications (NFC), particularly for the top spot in connecting retailers with shoppers in the store or walking nearby. Here's what's up with that…
Yesterday the Court of Justice tossed out the Data Retention Directive of 2006, declaring the directive "entails a wide-ranging and particularly serious interference with the fundamental rights to respect for private life and to the protection of personal data, without that interference being limited to what is strictly necessary." This comes as good news to some who cheer the possibility of more coherent rules and better privacy protections. But others worry that new restrictions might actually be worse.
Big data naysayers and expert evaluators do add value to the process by pointing out where real problems exist. That actually helps the rest of us correct our sights. Here are nine of those problems in one neat little list by two very good expert evaluators…
Automotive manufacturers currently have to assume costly manufacturing errors in their balance sheet: namely a rainy day piggy bank for the inevitable recalls and litigation costs. Predictive diagnostics can quickly change that scenario by finding problems in vehicles before they start killing people and ringing up the jury awards. The question is, will big data inform automakers that taking the litigation risk may actually be cheaper than fixing the problems? And if so, which path will automakers choose to take?
InBloom, formerly Shared Learning Infrastructure, is a huge ed-tech project designed to pool student data from multiple states in the cloud. The purpose was to make that data easily accessible to developers and schools to fast-forward education to producing phenomenal graduates. Until last week when it lost its last remaining customer, inBloom had nine state partners. While inBloom says it will continue its mission anyway, it is a gloomy example of just how fast privacy concerns can shut down a big data effort.