As IBM's big data evangelist, James Kobielus is IBM Senior Program Director, Product Marketing, Big Data Analytics solutions. He is an industry veteran, a popular speaker and social media participant and a thought leader in big data, Hadoop, enterprise data warehousing, advanced analytics, business intelligence, data management and next best action technologies.
July 24, 2014
Medical professionals are between the proverbial rock and hard place when trying to determine whether, how and why patients are failing to comply with doctor's orders. On the one hand, their ability to help people depends on having intimate, current and accurate knowledge of people's physical conditions and behaviors. On the other hand, doctors can't be Big Brother, engaging in 24x7 surveillance of their patients' private lives and wielding the power to punish recalcitrants. However, physicians can, within the bounds of privacy and propriety, use analytics to assess who might or might not be compliant. Using those insights, the healthcare system might identify the most appropriate real-time interventions to minimize the impacts of noncompliance on healthcare outcomes.
July 17, 2014
The recent controversy over the ethics of Facebook's attempts to influence moods through tweaks to its newsfeed algorithms is overblown. Essentially, Facebook data scientists conducted one of many real-world experiments that are standard operating procedure with them and with most online businesses these days. This was just a routine real-world experiment in big-data-driven sentiment analysis, content optimization and customer experience management.
July 10, 2014
As the quantified-self (QS) movement picks up steam, it will become more feasible to instrument more at-home infants with 24x7 physiological monitoring. It's increasingly feasible to cradle the baby's entire birth journey (prenatal, delivery, postnatal) in a comforting stream of vital signs, real-time alerts, prescriptive analytics and big data. The same QS-cradled infrastructure could conceivably serve as an early warning system throughout our lives.
July 9, 2014
IBM Analytics Warehouse for Bluemix is now generally available to all customers for your agile data warehousing and analytics needs. This pay-as-you-go cloud service, leveraging in-memory BLU technology, is designed to provide a single, agile in-memory platform for all applications required for most DW, BI and analytics projects.
July 3, 2014
Video content analytics tools are humanity's unblinking eyes, capable of continuously filtering the world's media streams at scale. Video content analytics algorithms can parse the fine details within and between successive frames of specific streams, supporting pattern recognition, gesture recognition, location detection, motion detection, event detection, production-style detection, dynamic video masking and camera tamper detection.
June 26, 2014
Conversational fluency is fundamental to consumer adoption of personal adviser applications of cognitive computing. IBM's cognitive-computing platform, Watson, drives harmonious conversations in multichannel customer environments. Conversational engagement is fundamental to Watson realizing its core value as a data-driven decision-support tool for disparate business, consumer and other applications.
June 19, 2014
Prioritizing data mining projects is a delicate art, equivalent to the decisions that R&D managers face every single day. How should you prioritize your data mining efforts and allocate your limited resources most effectively? Most important, how do you decide what NOT to work on?
June 12, 2014
Real-world experimentation of a very personal and hyper-analytical nature is what the quantified-self (QS) movement is all about. QS practitioners are playing with approaches that behavioral scientists have traditionally applied to third-party subjects within controlled laboratory experiments. The scientific establishment is beginning to realize the potential of quantified self tools for gaining primary data directly from human subjects in a way that is organic to the biological, behavioral, and psychological phenomena being studied.
June 5, 2014
A data scientist uses machine learning (ML) to find heretofore unknown correlations and other patterns in fresh data. ML is adept at finding both the "known unknowns" and the "unknown unknowns" through the power of supervised learning and unsupervised learning methodologies, respectively.
May 29, 2014
If I'm about to offend your religious sensibilities, I apologize in advance. Please avert your eyes from this post.