This is a guest post written by Scott Raspa. He works at IKANOW, a big data software company, where he is involved in the company’s sales and marketing efforts supporting public and private sector clients. He can be found on Twitter @sraspa. Big Data is the biggest trend in IT right now, however the term is loosely thrown around and becoming increasingly ambiguous. Everyone seems to be doing some sort of “Big Data” nowadays, which can cause great confusion among organizations with actual Big Data needs. We at IKANOW focus on unstructured data analytics, and may be a little bias, but believe it is an essential part of any Big Data offering. One question we hear all the time is “what’s the […] Continue Reading
This is a guest post written by Jagadish Thaker in 2013. Hadoop is changing the perception of handling Big Data especially the unstructured data. Let’s know how Apache Hadoop software library, which is a framework, plays a vital role in handling Big Data. Apache Hadoop enables surplus data to be streamlined for any distributed processing system across clusters of computers using simple programming models. It truly is made to scale up from single servers to a large number of machines, each and every offering local computation, and storage space. Instead of depending on hardware to provide high-availability, the library itself is built to detect and handle breakdowns at the application layer, so providing an extremely available service along with a cluster […] Continue Reading
Guest post by Tonya Chestnut, Associate Director of Admissions at Florida Polytechnic University. The study of science, technology, engineering and mathematics (STEM) is taking the field of big data analytics to new heights. Students pursuing a degree in big data analytics study the process of analyzing large datasets to discover patterns, connections and other useful, pertinent information revealed by data. Companies are increasingly turning to data analytics to harness customer insights to produce better business decisions to drive growth. As a result, the big data analytics field is in high demand and showing no sign of slowing down. Here are the top five reasons students should pursue a degree in big data analytics. Reason 1: The Field is Flourishing Big data analytics […] Continue Reading
The world of big data is evolving rapidly and the general public is starting to adopt it as their own. The question that often arises is; we want to do ‘something’ with big data, but what? For large companies this question is easily solved by hiring data scientists. Sure, there are many useful tools out there that show graphics from which you can extract conclusions. However, small and medium-sized companies often don’t know where to start putting big data into practice. At Datatrics we believe in doing this a bit differently. We developed a platform for small and medium-sized marketing teams that turns big data into concrete actions, so they immediately know how to approach their audience.
How your data can turn Black Friday into a White Christmas Returns are a huge area of concern for many retailers; indeed, the sale has not been completed until the customer actually decides to keep the product. But with Black Friday around the corner, and all of the flash buying that entails, can you predict what products are going to cause you the most problems, or even each which customers could give you a headache?
For one, the common knowledge is that you’d have to be really big in the publishing universe to use such tools. Secondly, most people, Marcello Vena thinks, don’t understand the difference between analyzing big data and “normal” data. He distinguishes three key features that set these two kinds of analysis apart. Big data uses a very large volume of unstructured data that standard database management systems simply cannot cope with. Big data needs “adequate data-centric processes from capture, ingestion and curation to search, modelling, analysis and visualization, not to mention other critical operations like storage, maintenance, sharing, transfer, security and availability.” Big data looks at all (or most of) the available information and doesn’t sample it to make it more […] Continue Reading
We’re in the golden age of gaming. In the US, more than half the households now own a console. Tablets and smartphones are packed with games. The video game market has already topped the movies and music markets. From a juvenile distraction to a mostly grown-up entertainment, this momentum is not about to dissipate. If anything, with the help of big data, it’s increasing faster than ever. You probably don’t know what this is. It’s called the Brown Box, and it’s the grandfather of all game consoles. The prototype was built in 1967 by a man named Ralph Baer. The basic traits of a console are there and have never changed: multiplayer controls and a variety of games to choose […] Continue Reading
In a previous post, I wrote about the various applications of big data insights in the gambling industry, from tailored marketing initiatives to odds calculation. However, the very same data used to great commercial effect by gambling companies could have another utility: protecting customers from addiction. Gambling brands are under increasing pressure to either relinquish their data insights to independent regulators or take internal measures to protect their customers. But what exactly is being proposed? And how precisely can big data analysis be used to combat problem gambling? New research and innovative safeguards A recent Wall Street Journal article explored the efforts of addiction scientists and industry consultants to spot ‘high-risk’ players through analysis of customer-tracking information. Working in collaboration […] Continue Reading
If you’re reading this you probably already have an inkling of what a data scientist is. Have you ever considered what a data scientist isn’t? According to Vincent Granville, author of Developing Analytic Talent: Becoming a Data Scientist, data scientists are: Not statisticians Not data analysts Not computer scientists Not software engineers Not business analysts Data scientists do have some knowledge in each of these areas but also some outside of these areas. NEITHER STATISTICIANS NOR DATA ANALYSTS: One reason the gap between statisticians and data scientists has grown over the last 15 years is that academic statisticians, who publish theoretical articles (sometimes not based on data analysis) and train statisticians, are… not statisticians anymore. Also, many statisticians think that […] Continue Reading