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
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 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
Leading organizations in financial services, telecommunications, retail, healthcare, digital media, insurance and other industries are outperforming their competition by generating new, actionable insights from big data. There are three dimensions in their performance that indicate a correlation
The Saïd Business School at the University of Oxford and the IBM Institute for Business Value conducted this global survey of more than 1100 business and IT executives. The study highlights the phases of the big data journey, the objectives and challenges of organizations taking the journey, and
Information On Demand 2012 (IOD) was a sensational event from start to finish. This was my sixth consecutive IOD, and my first as an IBMer. Long before I joined IBM, I always looked forward to IOD. This annual confab is always a great opportunity to drill deeper into the myriad information
As promised, we’re going to revisit a topic I introduced awhile ago in "Why Static Stinks". Based on what I’m seeing recently, static still stinks, so now is a good time to resurface our discussion. Collectively, we’re just not moving fast enough to fix the glaring issues that static–otherwise
"Value" is the key word in several of my top picks this week. From saving money to saving lives to saving time in who you follow on Twitter, we're still finding new ways to get value from data.
“What Executives Don’t Understand About Big Data,” by Michael Schrage, Harvard Business Review HBR Blog
Predictive analytics is not just about forecasting what might happen. It’s also about detecting the warning signs of bad things that, if we don’t act quickly, might prove catastrophic or highly disruptive.
In the engineering world, for example, many organizations use statistical tools to predict
Did you miss me? After taking off the month of August to launch the IBM Big Data Hub (and to run the 199-mile Hood To Coast Relay), I’m eager to be back on the “Top Reads in Big Data” beat, rounding up great articles, blog posts, videos, podcasts and infographics. It was a short work-week in the
Based on his recent blog post, “Why Static Stinks,” Tom Deutsch, program director of IBM big data portfolio, further explains why non-personal recommendations – or “static” – are bad. Deutsch states that it is important to understand not only the trends of consumers, but who they are as people,
Among healthcare executives interviewed for the 2010 IBM Global CEO study, 90% expect a high or very high level of complexity of data over the next five years, but more than 40% are unprepared to deal with it. The volume, velocity and variety of data are outpacing the ability for healthcare