It’s difficult to read a banking technology article or go to a conference without hearing about big data. Most of us now believe that big data is more than just hype, that it can offer business benefits to those that can leverage big data into new business capabilities. But a common question I hear
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 Part III we covered how IBM applied those considerations and
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
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
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
In a recent LinkedIn discussion group posting, I sketched out a five-layer framework for low-latency analytics in the cloud. Those layers were:
What they all address, in the ultimate extreme, is the need
If you think “data scientist” is a pretentious title, think again. Nothing could be more fundamental to science, to engineering, and to the continuous optimization of modern business processes.
So, you may ask, what is true science? And what exactly is a scientist? How can data scientists live up
We all need an attitude adjustment when it comes to analytics.
According to Nate Silver, keynote speaker at IBM’s recent Information on Demand (IOD) conference, New York Times contributor, and author of the recently released book, “The Signal and the Noise: Why So Many Predictions Fail, but Some
The need to innovate and stay ahead of customer demands is even more imperative today. IDC estimates that “in 2012 the digital universe will grow to 2.7 zettabytes.” As customers and the market as a whole generate data, companies are compelled to capture and analyze an ever-greater percentage of
How do businesses address the challenges of growing volume and variety of data? How can I introduce new data sources and workloads into my architecture? How do I achieve better time to value and agility in my infrastructure?
If you're wrestling with these and other related questions, I recommend