Most Popular Blog Posts of 2013 - So Far

Social Media Strategist, Information & Analytics Group

Untitled-1.pngThe top 10 blogs from the first half of 2013 show you are interested in learning how to get started with big data – especially if you’re in the banking industry – and looking at just what it is that gives a data scientist that elusive star power. It’s also clear that good blog posts have long legs: half of these posts were written last year, but they’re still going strong. Let’s take a look at the most popular posts so far this year.

Data Scientists: Myths and Mathemagical Superpowers

James Kobielus busts 10 myths about data scientists and reveals the secrets behind their "mathemagical" superpowers. Bonus: This post also contains a link to a very popular SlideShare presentation. Double Bonus: Listen to James discuss these myths further in a new podcast.

New Study Details How Real-World Enterprises Are Using Big Data

In late 2012, IBM’s Institute for Business Value (IBV) and the University of Oxford released the information-rich and insightful report “Analytics: The real-world use of big data.” Based on a survey of over 1000 professionals from 100 countries across 25+ industries, the report provides insights into organizations’ top business objectives, where they are in their big data journey, and how they are advancing their big data efforts. It also provides a pragmatic set of recommendations to organizations as they proceed down the path of big data.

BONUS: You can take part in the 2013 survey – register here!

Data Scientist: Consider the Curriculum

Data science’s learning curve is formidable. James Kobielus describes the core curriculum of training and certification behind a business-oriented data-science curriculum designed to produce expert developers of statistical and predictive models

Hadoop Myths Debunked

Hadoop has acquired a large body of prevailing myths in its short history as the hottest new big data technology. James Kobielus tackles 8 of them head-on.

10 Big Data Implementation Best Practices

Since big data is still relatively new with many organizations, and its significance in business processes and outcome has been changing every day, Sushil Pramanick, outlines the 10 steps that implementation teams need to do in order to increase their chances of success.

Where Does Hadoop Fit in a Business Intelligence Data Strategy?

The key question isn’t “Does your BI tool support my Hadoop technology?” It really needs to be “What is the best way to leverage an Hadoop infrastructure with my BI tool?” So says Product Stategist Tina Groves, who includes helpful illustrations with her explanation.

Making a Federal (Use) Case out of Big Data

Pundits say the U.S. federal government should digitize all the existing files for key programs such as veterans’ administration and healthcare. Andrew DiStefano looks at what it will take for government agencies to best manage and govern enormous volumes and types of information, while protecting citizen privacy and civil liberties.

Analytics in Banking Services

The banking industry is data-intensive with typically massive graveyards of unused and unappreciated ATM and credit processing data. Sushil Pramanick highlights industry research reports and looks at how fraud and risk analysis, along with customer analytics, are helping leading banking institutions leverage their data to improve operations and profits.  

Big Data in Banking: Driving Value in Next Best Action

“Where there are challenges, there are opportunities. Banks that are harnessing big data find they can derive more insight about their business than ever before.” That’s the message behind Bob Palmer’s post about how banks are using big data to improve customer focus and service.

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