Top big data and analytics podcasts from the first half of 2014

Social Media Strategist, Information & Analytics Group

Half of 2014 has already passed us by, but it was chock-full of educational, thought-provoking and sometimes inspiring content here on the IBM Big Data & Analytics Hub. All week long, we will be reviewing the most read, most watched and most listened to content so you can catch up on anything you may have missed. It’s even better than binge-watching “Breaking Bad” on Netflix! Today, we’ll look at the top 10 most popular podcasts.

6 trends in big data and analytics

Through talking with some 3000 clients, prospects and industry leaders, Inhi Cho, the vice president and general manager of Big Data, Integration & Governance at IBM, and her team identified six important trends that are driving big data forward. Inhi joined us to talk about those market and technology forces.

Top 5 big data use cases

This podcast was originally published in February 2013, but it’s still a favorite as listeners seek to discover "what can I do with big data?" Five key use cases have emerged that hold high potential value for many organizations. Learn more about these five use cases and listen along to this explanatory podcast.

pug wearing headphones.jpgBig data is all data

Mark Myers, market segment manager for IBM Watson Explorer, says the usual "3 V's" definition of big data is too narrow. "This way of looking at the issue limits big data to a technical challenge and misses what has become the real significance of big data: finding new ways to use data to create business value. To me, that is why I think big data is all data." Listen to him explain the thinking behind this approach and give advice on how to begin getting more value out of your data.

Data Scientists and their curriculum

Here’s another “oldie but goody,” published in October 2013. Data science has been called the "sexiest profession of the 21st century." Well known big data evangelist James Kobielus describes what a data scientist is and does, what paradigms and practices one must know and what skills and knowledge one should possess.

Foretelling 2014 trends in big data, Hadoop, data science and more

What's in store for big data, analytics and data science in 2014? Big data evangelist James Kobielus walks us through what he sees shaping up in those areas, plus cognitive computing, machine learning, Hadoop, NoSQL and more.

Watson Foundations: The story behind the big data and analytics platform

Beth Smith is General Manager of IBM’s Information Management Division, which includes database, data warehouse, information integration, master data management, integrated data management & governance and big data offerings. In this podcast, she explains Watson Foundations (IBM's Big Data platform) and the capabilities of that platform to help organizations discover fresh insights, improve operations, and establish trust in their data so they can act with confidence.

How in-Hadoop analytics are changing the game

Big data without analytics is just data, but how do you perform the analytics? This podcast answers that question and gives examples of how in-Hadoop analytics are changing how organizations collect and analyze their data.

Big data innovations in retail

Nearly every company wants to acquire more customers and keep them longer. Yet, in some ways, that’s more challenging now than ever with so many options available to consumers. But there is also tremendous opportunity for companies that are using data and analytics in creative and innovative ways. IBM Fellow, Distinguished Engineer and Chief Scientist of the business analytics and optimization group, Michael Haydock describes how one popular food and beverage retailer is marrying billions of transactional records with weather and social media data to create highly personalized promotions.

Building the business case for real-time analytics at CenterPoint Energy

Industry analysts project that 30 percent of companies will “monetize their data” by 2016. But what does this mean? Many organizations are trying to figure out how to turn their data into a gold mine. The reality is each business is unique and data monetization projects should be customized to focus on a particular outcome for that organization. Two IBM clients joined us for this podcast to talk about their real-world data monetization projects.

Where the magic happens: predictive analytics in media and entertainment

Some companies in the media and entertainment industry are monitoring social media and integrating social data with other data to form elaborate predictive analytics models. Graeme Noseworthy describes how they are doing this and what they've learned along the way, including some surprises, that has helped them fine-tune their marketing and promotional plans.

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