In today’s world, many can compare their big data landscape to a vast wilderness. We know it exists and it is growing in size, volume and complexity – but do we know what is really growing and taking roots. Are there valuable resources that, if mined, could create new growth and opportunities? Or
Since big data is still relatively new technology, many of you are conducting research and seeking quality educational resources. That's apparent in this list of the 10 most popular analyst reports, ebooks and white papers. We are proud to offer such an extensive library - more than 40 items - from
Co-authored by Kim Minor, Worldwide Industry Marketing Manager for Insurance at IBM.
Claims fraud is an important topic, so we’ve written about it several times before. In this blog, I want to discuss how IBM big data capabilities can augment an existing fraud system at any insurer. By wrapping
The top 10 blogs from the first half of 2013 show you are interested in learning how to get started with big data – especially if you’re in the banking industry – and looking at just what it is that gives a data scientist that elusive star power. It’s also clear that good blog posts have long legs
How CSPs are Transforming Call Centers to Lower Churn, Costs and Stress
The day in the life of a call center agent can be very stressful, especially when information needed to solve the customer problem is not accurate, not up-to-date, not consolidated and not immediately available. Slow service
We’re continuing our look at the most popular content on IBM Big Data Hub for the first half of 2013. Here are the most-watched videos.
Big Data, Big Opportunities for Communications Service Providers
Big Data, Big Opportunities: Energy & Utilities
T-Mobile: Network Engineering Success
With one half of 2013 behind us, let’s take a look at what has been on the top of your mind over those six months. All week long, we will be reviewing the most read, most watched and most listened to content on IBM Big Data Hub. Today, we’ll look at the top 10 most popular podcasts.
Top 5 Big
Here’s a big data problem for you. Let’s say you’ve accidentally traveled back in time 30 years and the only way to get Back to the Future is to transfer 1.21 Gigawatts of energy into a beat-up DeLorean.
Well, back in 1985, the solution, by Hollywood standards of course, was the
Big data presents important opportunities for enhancing the efficiency, safety, productivity and cost-effectiveness of oil and gas operations. Yet it comes with an array of operational technology challenges that often impede the use of big data for operational gains. For example, companies need
After a successful promotion, a consumer products company minimizes OOS and maximizes sales
In April, I introduced a video demonstration called Optimizing Consumer Product Promotions Effectiveness with Analytics. In this demo we met Mary, the marketing brand manager for DuraBar, a nutritional bar
With multiple channels and numerous ways to interact with companies, today’s customer journey is a complex weave of paths. Often, customers start and end their journey before the business is even aware of it. With today’s competitive market place, the companies that best understand their customer–
This first posting of a seven-part blog series is my attempt to present, in small, easily consumable bites, findings from IBM Institute for Business Value and University of Oxford’s study and excerpts from a white paper, “Analytics: the real world use of big data in financial services.”
I’ve been trying to delve into a topic I normally avoid—clinical trials. But it’s not about how clinical trials are run, or how to design one. I’m more interested in what happens to clinical trials data once they are completed.
Do folks actually mine their clinical trial archive for insights?
Colin White and I recently wrote a white paper for IBM titled Technology Innovations for Enhanced Database Management and Advanced BI. In it we discussed the fact that IT leaders across all industries need to review and enhance their information architecture to support new requirements, such as big
Analytics solutions designed to handle the volume and variety of data available today also help insurance companies improve catastrophe risk modeling, through which companies determine the exposure of current policies and predict the probable maximum loss (PML) from a catastrophic event.