A recent TDWI report sponsored by Alteryx shows that predictive analytics is having a huge impact on the data and analytics landscape within organizations. The infographic below highlights the data types and new use cases that benefit from the increased use of predictive analytics, and why business analysts are best positioned to make the greatest contribution.
The O’Reilly Strata + Hadoop World Conference is one of a few conferences that seriously can deliver on the mission of providing a state-of-the-art perspective on the big data industry. Here is a selection of video presentations made by industry luminaries that can guide enterprise thought leaders.
FIELD REPORT Last week I attended the long-anticipated useR!2014 international conference at the UCLA campus, my alma mater. The four day event had something for everyone in attendance – all the brain cycles centered around the use of the R statistical environment. Since R is a primary tool for my work in data science and […]
It was Saturday, June 14 and I was up at the crack of dawn (which is quite an achievement for a late night data hacker like me) to get over to the Big Data Camp 2014 happening at the DirectTV campus in beautiful El Segundo, Calif. (actually a spartan industrial area just south of LAX). I was anticipating a great day of big data technology focusing on three advertised session tracks: Data Science, Hadoop, and NoSQL.
Seventy-five percent of businesses have yet to successfully deploy big data analytics solutions to gain business-impacting insights, despite 65 percent increasing their investment in analytic services and technologies in 2014. These findings are part of “Analytics 2014,” Lavastorm’s second annual survey on analytic usage, trends, and future initiatives.
Everyone knows that data scientists love data and the more of it, the greater the love. As a result, the surging interest in wearables is just what the doctor ordered because these electronic devices collect enormous treasure troves of data. In turn, it is the job of data scientists to make sense of it all, unlock secrets, and assign economic value. As a data scientist, it is a dream come true!
I found an interesting discussion going on in the Global Big Data & Analytics group on LinkedIn – “Why do Hadoop projects fail?” Having just returned from the Hadoop Summit 2014 in San Jose, I witnessed plenty of use case examples Hadoop implementations that were wildly successful. I was therefore intrigued by the notion to itemize causes for failed projects.