Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, data mining and others that analyze current and historical facts to make predictions about future events. In business, predictive models exploit patterns found in historical and transactional data
I recently returned from Las Vegas where IBM hosted its annual Information On Demand conference with over 12,000 attendees. The theme for the conference was “Think Big,” and the bulk of sessions centered on the way different industries are using big data to improve business results in their
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
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
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.
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
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
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
This paper defines what big data analytics mean to Communications Service Providers (CSPs), provides an overview of their most valuable uses in telecommunications, outlines what companies must look for as they adopt new big data analytics solutions and discusses how IBM can help.
Unlike many other Big Data Analytics blogs and books that cover the basics and technological underpinnings, this e-book brings a practitioner’s view to Big Data Analytics. The author has drawn the material from a large number of workshops and interviews with business and IT leaders.
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