Topic:

Analytics and Visualization

Latest Headlines

Latest Headlines

Visualizing data with Datameer's 4.0 update

In the "oh, this is cool" column of interesting things happening in visualizations is Datameer's 4.0 update this week. Instead of creating a visualization at the end of your work on the results, you can visualize the data on your screen while you're working with it (in real-time) as long as your working with Datameer's analytics tool. 

Big data app development brought to you by Hortonworks, Cascading

Hortonworks has added Concurrent's Cascading SDK to its Hadoop distribution. Such helps developers operationalize their data. In addition, Hortonworks will certify, support and deliver Cascading--the most widely used App development framework for data applications on Hadoop.

Too few sources and 'the model organism problem'

One of the great benefits found in the throes of the maturation of any technology is the ability to see problems clearly. That which we can see, we can fix. 

Big data 'revolution' needs to happen, now

Yes, there is a big data revolution afoot and this time around things are getting real! 

3 ways data visualizations can lie

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.

Beijing government looking to build big data platform

Rest assured that all governments are or will be using big data. The latest to stake claim to its benefits is Beijing.

Transforming elections in India using big data

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.

Big data is not about petabytes, but complex computing

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.

9 reasons why getting big data right is so danged hard

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…

Roll of the big data dice: will car makers risk litigation or go for preventative certainty?

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?