James Kobielus

Columnist

As IBM's big data evangelist, James Kobielus is senior program director for product marketing for 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. The views expressed here are not necessarily those of IBM.

When big data is truly better

Can a machine detect sarcasm? Yeah, right

Applying analytics to social media? Good luck -- not all words can be taken at face value. Natural language processing helps, but it's no panacea

What's machine learning? It depends on who you ask

As interest in machine learning has grown, its definition has expanded to include a panoply of techniques for automating knowledge and pattern discovery from fresh data

Big data log analysis thrives on machine learning

Huge quantities of log data generated by all sorts of devices opens immense potential for insight, but machine learning is needed to make sense of it

Too big, too small, or just right? Balancing your social connections

An MIT professor analyzes social graph data to find where influence meets connectedness -- and how to maximize it

Never put everything in one database basket, even if it's Hadoop

Those who recommend putting everything in a Hadoop data lake forget some obvious lessons of database history

Machine learning floats all boats on big data's ocean

Machine learning is the unsung hero that powers many of the most sophisticated big data analytic applications

Cognitive computing can take the semantic Web to the next level

As big data analytics pushes deeper into cognitive computing, it needs to bring the semantic Web into the heart of this new age

Big data demands nonstop experimentation

Predictive modeling needs the dynamic experimentation of big data to discern true, underlying correlations. Some organizations are ready for that disruption -- and some aren't

YARN unwinds MapReduce's grip on Hadoop

Hadoop has been known as MapReduce running on HDFS, but with YARN, Hadoop 2.0 broadens pool of potential applications

Sometimes it's OK to treat people like numbers

After all, the customers' own Web activity fuels analytic models, but a caveat: The data can grow too complex

Devops can take data science to the next level

For data scientists, creating a perfect statistical model is all for naught if the compute power required is prohibitive. We need tools to assess the performance impacts of modeling alternatives

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