The huge volume of Big Data produced by sensors, genomic sequencers, electronic exchanges, and connected devices continues to generate headlines but it’s the diverse types of data, not the volume, that’s a bigger challenge to data scientists and is causing them to “leave data on the table.”
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.
Big data is all about finding ways to manage the increasing volume of information being kept on consumers presumably to help with making purchase decisions and fine-tuning the customer experience. Through data science and machine learning, technology-driven businesses have the ability to know more about their customers and potential customers than ever before. Service providers […]
This past week I was hot on the Meetup circuit here in Silicon Beach and I decided to take in a presentation “Data Science @ Activision”. Activision is the publisher of the famously popular video game “Call of Duty.” The company has multiple analytic teams. The talk provided a detailed overview of data science at Activision and provided some additional detail on two of their analytic groups: the Game Analytics Team and the Marketing & Advanced Analytics Team.
I recently ran across a thought-provoking post on the USC Anneberg Innovation Lab blog – “Why Do We Need Data Science when We’ve Had Statistics for Centuries.” With all the debate of late surrounding the relatively new “data science” term, I’ve been thinking a lot about this question, so I thought I’d analyze this notion […]