Blogs

Energy and Utilities - Switching to Big Data

November 14, 2012 | by Jeff Nedwick, Industry Marketing Manager, energy & utilities, automotive, and chemical & petroleum industries, IBM
Energy and utility companies face increasing pressure to accurately predict the supply of energy attributable to renewable resources. By factoring in weather and other key variables, utilities can determine their capital investments and where and when to deploy new generation assets. They also seek...

Big Data, Big Analytics, Big Results – and Big Challenges?

November 13, 2012 | by Dena deBin, Senior Market Manager, IBM
I think we all understand that Big Data is a Big Deal. Every day we are hearing and seeing results that others are achieving and are more than intrigued about what is possible. The entire concept is a breath of fresh air, a significant breakthrough, that helps us efficiently and effectively harness...

3 Undergrads Walk Into a Room . . .

November 13, 2012 | by Whitney Hepp, Director of Marketing and Operations, TerraEchos, TerraEchos
A Computer Science major, an Information Management major and a Marketing major sit in a classroom in Montana…No, It’s not the beginning of an epic joke; it happens to be a glimpse into the first ever undergraduate IBM InfoSphere Streams course. The University of Montana, with the help of IBM and...

The Role of Data Quality in a Big Data Environment

November 12, 2012 | by Emily Cotter
How can we ensure the quality of big data? Big data, in its constant growth, relies on massive volumes of data that come from inconsistent sources, with ambiguous lineage and uncertain data currency. This has created one of the greatest challenges in today's big data environments. Integrating...

Data Scientist: Potential Superstars in Prediction Markets

November 9, 2012 | by James Kobielus, Big Data Evangelist, IBM
Prediction markets are where data scientists will attain superstar status. It’s no coincidence that the current age of the “superstar” in professional sports began in the 1970s, when the legal constraints that had prevented the most accomplished athletes from seeking top dollar on the open market...

Small Data + Big Data = Major Awakening in U.S. Election

November 8, 2012 | by David Pittman, Social Media Strategist, Information & Analytics Group
While we eagerly await the first post-election “victory lap” article by Nate Silver – who correctly predicted the outcome of all 50 states in the United State Presidential election Tuesday – I want to share with you several of the top articles that address the role of data and analytics in this...

Healthcare Analytics – Is it paying for itself?

November 8, 2012 | by Sushil Pramanick, Sr. Managing Consultant for Business Analytics & Optimization Practice, IBM
Healthcare organizations are in the eye of an information overload storm. With EHR, EMR and with HIE implementation, there’s tons of data that will be available to payers, care providers and care management companies. The data latency will be reduced to days and hours–if not minutes–from the weeks...

Part III: IBM’s Strategy for Big Data and Analytics

November 7, 2012 | by Krishnan Parasuraman, CTO, Digital Media and General Business, Netezza, an IBM Company
In Part I of this series, we looked at the key considerations for an analytic enterprise to stay competitive in today’s world, and in Part II we discussed how those translated into imperatives for a supporting big data platform. In this post we will cover how IBM has applied those considerations...

NYSE Euronext Uses Big Data to Innovate and Prepare for the Future

November 6, 2012 | by Rima Mukherjee, Client Reference Marketing Specialist
NYSE Euronext, a leading global operator of financial markets and a provider of innovative trading technologies, operates exchanges in the United States and Europe. NYSE Euronext equities marketplaces represent one-third of equities trading worldwide. Being a global trading and technology provider...

Next Best Action Rides the Best-Fit Model

November 6, 2012 | by James Kobielus, Big Data Evangelist, IBM
In a recent LinkedIn discussion group posting, I sketched out a five-layer framework for low-latency analytics in the cloud. Those layers were: data latency execution latency modeling latency insight latency results latency What they all address, in the ultimate extreme, is the need...