Top 10 blog posts on big data and analytics from the first half of 2014

Social Media Strategist, Information & Analytics Group

Data science has been called the “sexiest job of the 21st century,” and based on the posts you have been devouring on IBM Big Data & Analytics Hub, the old adage “sex sells” definitely applies. Three of the most popular blogs from the first half of 2014 are about data scientists, and three more are on Hadoop, which also leans toward the data side of discussions. Let’s review what else you really enjoyed:

  1. Data scientists: Myths and mathemagical superpowers. Interesting trivia: This post was the top post last year at this time too—talk about staying power! 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 on the same subject. Double Bonus: Listen to James discuss these myths further in this podcast.
  2. Data scientist: Master the basics, avoid the most common mistakes. Kobielus strikes again with another solid post on data science, which he says, “is a human craft, demanding just as much nuanced judgment and intuitive technique as you’d expect from any skilled artisan. One of the downsides of using the word ‘science’ in this context is that people think that statistical analysis is just some sort of cut-and-dried laboratory procedure that you follow step-by-step to arrive at the ‘truth.’ That’s not true in the slightest.” Read on to learn why.
  3. Data scientist: Closing the talent gap. I’m sensing a theme here. You guys really like posts by Kobielus on data scientists. In this one, he talks about the growing alarm that we won’t have enough data scientists to go around and alleviates concerns that the big data revolution will come to a screeching halt due to the shortage.
  4. Hadoop meets SQL. Big data technologies like Hadoop are providing enterprises a cost-effective way to store and analyze data. Enterprises are looking at using Hadoop to augment their traditional data warehouse. Compared to traditional data warehouse solutions, Hadoop can scale using commodity hardware and can be used to store both structured as well as unstructured data. 
  5. Where does Hadoop fit in a business intelligence data strategy? For many people, big data equals Hadoop. But the ability to access a data store does not imply that all business intelligence capabilities are readily available or even appropriate. Over the past 20 years, a number of different data structures and technologies have been introduced to increase performance or enable a BI capability; Hadoop is another data storage choice in this technology continuum. 

    wordle for davids top blog posts first half of 2014 blog.JPG

  6. 35 "Big-brained" leaders on big data and analytics. Publications and blogs frequently run lists of the top thought leaders and influencers on big data and analytics, and many of whom have appeared right here on IBM Big Data & Analytics Hub. Here’s a rundown of several blog posts, videos and podcasts from these big-brained people, all in one handy spot—from yours truly!
  7. 10 big data implementation best practices. Many organizations are looking for guidance on how to roll out big data projects successfully. Sushil Pramanick lists 10 best practices that implementation teams should follow to increase the chances of success.
  8. Running Hadoop in the cloud. With the growing popularity of cloud computing, enterprises are seriously looking at moving workloads to the cloud. There are issues around multi-tenancy, data security, software license and data integration that have to be considered before enterprises can make this shift. As enterprises start evaluating Hadoop, one of the questions frequently asked is “Can we run Hadoop in the cloud?” This post from Ven Kumar answers that question.
  9. What Koby's tea leaves foretell for big data in 2014. In early January, Kobielus pulled out his crystal ball to make predictions for the big data market this year. He explained, “Big data industry predictions are a delicate art; they need to reflect original thinking but not be too blue-sky. They need to have integrity and be grounded in the unique viewpoint of a specific observer. And they need a practical utility that guides IT professionals in realigning their own planning frameworks.” Check out this post to see if James’ predictions have come true so far this year.
  10. An analyst’s examination of IBM Watson Foundations. On January 9, 2014, IBM announced that it was forming a new organization (the Watson Group) and investing a billion dollars in future Watson development. The company also announced three new Watson cloud services (The Watson Discovery Advisor, Watson Analytics and IBM Watson Explorer) as well as the availability of something called IBM Watson Foundations. Industry analyst Joe Clabby gives his insights on these moves in this early 2014 post.

Want more?