Technology analyst Judith Hurwitz recently penned “Cognitive Computing and Big Data Analytics,” which is an in-depth look at a many-headed emerging technology. She told us cognitive computing is, in fact, an amalgamation of many things. Individual aspects can be challenging. But, according to Hurwitz, taken as a whole, this new computing paradigm shows promise.
In this edition of Talking Data, Tech Target editor Ed Burns recaps two recent conferences he attended: the TDWI Executive Summit in Las Vegas and the Sloan Sports Analytics Conference in Boston. The first half looks at the trend of taking a cloud-first strategy to data management. In the second, Burns explains some of the new ways sports teams are using analytics to run the business side of their operations.
The combination of parallelism and machine learning is potent, and it has data scientists at companies like Allstate Insurance, Cisco Systems and Pandora Media in its thrall. They want to build complex machine learning models and run the models repeatedly to improve the results — and they want to do that work as quickly as possible, reporter Jack Vaughan tells podcast cohort Ed Burns in this edition of the TechTarget Talking Data podcast. They discuss this and related doings at the Strata + Hadoop World 2015 conference held recently in San Jose, Calif.recently in San Jose, Calif.
There are a lot of moving parts and a lot of ways to view this thing called Watson. So we tried to get several perspectives on the IBM effort to bring cognitive computing to life in this episode of the Talking Data podcast. Senior SearchCIO.com News Writer Nicole Laskowski told us cognitive computing can be viewed as a way of transitioning the role of the computer so it becomes a partner to the user. SearchBusinessAnalytics.com Site Editor Ed Burns said that IBM may find easier tracking by going after here-and-now call center tasks and travel planning improvements. There is a view on the competition, a Watson user and more in this special report.
In this edition of Talking Data, Ed Burns sits down with special guest John Myers of Enterprise Management Associates to discuss who new report looking at the maturity of cloud-based analytics applications. Has the time come to move your analytics to the cloud? Take a listen and find out
This edition of Talking Data looks at the trends of 2014 via our favorite tweets. Among the topics of discussion is the growing interest in protecting data privacy through online services.
Predictive analytics initiatives can be great, but businesses need to stay vigilant. In order to get any value out of the project, a sharp focus on specific goals is necessary. This podcast takes a look at some recommended strategies and potential pitfalls for getting to real business value in predictive modeling
Bleeding-edge users have been working with open-source Hadoop and MapReduce for a few years. Now a Hadoop-compatible MapReduce alternative known as Spark is coming online to provide a timely streaming type of analytics. Hadoop and Spark have much in common, as discussed in this episode of the Talking Data podcast.
The European take on big data is different than that in the United Kingdom, which bears a bit more resemblance to the Hadoop-happy big data take of the U.S., according to Brian McKenna, business applications editor at TechTarget’s Computer Weekly website in London. We spoke with McKenna about this and more for a ‘continental flavored’ episode of the Talking Data podcast.
It has become a recurring theme this year. The Spark framework has gained attention, at the same time taking some wind from the Hadoop framework’s sails. Over the summer such news was a major preoccupation of the big data community, as discussed in this episode of the Talking Data podcast.