Blogs

James Kobielus
Big Data Evangelist, IBM
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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.

Using real-time analytics to identify who's scooping whom in online journalism

August 14, 2014
Working journalists are locked into a never-ending race against time. Not only are reporters always up against deadlines, but they are constantly scrambling to make sure they break the news before the competition. As more people turn to online news sources (including, but not limited to traditional news websites, streaming broadcasts and mobile apps) it's a bit bewildering to figure out who is scooping whom when on which breaking topics. Before long, we can expect to see the news services' data scientists build streaming tools that analyze how fast they and the competition are breaking news online, and bragging with data when they find themselves doing the scooping. Read More

Wearables driving real-time actionable analytics into the modern lifestyle

August 7, 2014
Wearable devices are becoming central to the modern lifestyle. These new devices will be among the first places where users originate personal data. They will also become the ultimate membrane where people consume the big data-driven personalized guidance being delivered from the cloud. In the process of supporting myriad roles in users' lives, wearables will almost certainly cache working data sets that push more deeply into big data territory in terms of their volumes, velocities and varieties. Nevertheless, under any likely scenario, individuals' personal data clouds will undoubtedly hold far more on the volume side of the equation than people store locally today. Read More

Empowering athletes with real-time, data-driven decision support

July 31, 2014
There are complex challenges that a data scientist might face in statistically modeling real-time decision-support scenarios in fast-moving athletic competitions. Each sport needs to be modeled on its own terms. A within-game decision-support predictive model for one sport cannot be applied directly to another sport, even ones that share a common ancestor or many surface similarities. No two sports have exact same "game evolution" structure, embody the exact same rules, play on the same surface, use the same equipment or generate the same types of performance data. Read More

Real-time healthcare compliance analytics can keep patients alive and well

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. Read More

Real-world experiments and the Facebook controversy over mood manipulation

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. Read More

Big data as a factor in life-or-death decisions

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. Read More

Transforming the agile data warehouse in the age of the in-memory cloud

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. Read More

Streaming media and narrative power of video content analytics

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. Read More

IBM Watson and the power of conversation in the cognitive fabric

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. Read More

The delicate art of data science project prioritization and triage

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? Read More

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