Machine Learning Algorithm Enables Customer Support of the Future

Mobile intelligence company Carrier IQ has announced a machine learning algorithm that will potentially revolutionize customer support for your mobile device. Carrier IQ, which sells support applications to mobile network operators, is using machine learning and Big Data analysis to automatically detect common customer problems. The company's 'IQ care' software suite mines a large amount of device data, and uses that Big Data to look for trends that are not obvious from simple statistical analysis. The information gathered from this process is used to automatically diagnose individual device problems, and a cell provider is able to support 50 million reporting devices simultaneously using this software. The two main things this software looks for is to differentiate network problems from device problems, and to find resource draining applications on your phone that will negatively impact user experiences. The ultimate goals for cell phone carriers that use this technology is to increase customer satisfaction, stop unnecessary device returns, and to allow customers to easily troubleshoot their own devices.

Amazon Offers On-Demand Big Data Analysis

In a promising sign for the future of Big Data Analytics, Internet giant Amazon is now offering a hosted version of the R programming language.

R is a programming language that is mainly used for statistical analysis. Although it has been around since the 1990s, it has recently become popular for its use in Big Data mining and analysis. Amazon is offering 'Revolution R Enterprise 7' (RRE) on its cloud service, which is a commercialized version of the R language developed by Revolution Analytics. RRE will run on either Windows or Linux platforms.

While R itself is free and open source, Revolution Analytics claims that its service offers "higher performance and higher scalability for dealing with large data sets". There is also a version of RRE that will run on multiple processors simultaneously. However, this service doesn't come cheap - the base rate for RRE starts at 1.25 per hour per core.

Personally, it is difficult to see the added value of this service. While there is a certain level of convenience by using this on demand service, not to mention the knowledgeable tech support that Revolution provides, the appeal for this product is limited at best. Revolution themselves acknowledges this by saying that it would be more cost effective to buy their product outright for any data set exceeding 1TB.

In essence, this service offers a convenient way for more people to get in on the Big Data trend and test out the R programming language, but faster and more cost effective alternatives will likely arise with the increasing popularity of the R language and Big Data Analytics in general.

The Booming Big Data Market

Big Data has been a fast emerging trend in technology, and a new report shows that it has started to take off in the business world – and in a big way. The market for Big Data Analytics climbed to 18.6 Billion in 2013, a 58 percent increase in spending over 2012. Market forecasts show that the spending spree won’t slow down in the near future, with Big Data services projected to rake in 50 Billion USD by 2017.

But why are companies suddenly willing to invest in Big Data? As the report mentions, the rapid maturing of this new field has boosted confidence in Big Data products and services among all kinds of businesses. These products have also become increasingly more secure, and have allowed for better privacy capabilities. The widening availability and feasibility of using the Apache Hadoop framework for Big Data processing has also helped improve the popularity of Big Data services. As the Big Data market continues to mature, more businesses will utilize Big Data services as more polished applications emerge, and as security and data privacy issues are addressed. 

Sentiment Analysis, Social Media, & the Super Bowl

On the day of the 10 year anniversary of Facebook, most of us recognize how social media has revolutionized the way we communicate online. 

Social networking has been proven to be popular with the public at large over the years, with no signs of the trend slowing down. Consequently, businesses have adopted social media as a way to keep in touch with their customers. More recently, the corporate world has begun to harness the power of social media differently - through gathering and analyzing the large quantities of opinionated data available on social media sites concerning their company or products. 

So how do they do this? Companies can use software tools that gather and analyze data on the web using a process called sentiment analysis. Sentiment analysis, also known as opinion mining, is a process that looks at large volumes of text in order to gather specific information, called sentiments. Sentiments are defined as personal opinions or feelings that an author has concerning a particular subject. Ultimately, the goal of sentiment analysis is to derive the writer’s attitudes, opinions, and conveyed emotions in a particular piece of writing.

A good reputation is critical for any company or brand, and the viral nature of social media communcations makes online repuation management even more critical. In this week's Super Bowl, many of the advertisers took to the web to monitor reactions to the commercial while they were happening. Volkswagen set up their own social media "war room" to monitor major trending topics Twitter during the Super Bowl. They then reacted to the trends to create related tweets and videos on the spot. This reinforced connection with the customers, while at the same time effectively managing their brand on social media. 

Social Sentiment: Are You Listening?

Sentiment analysis is a field in computer science that seeks to derive opinions from text based data. In this video, IBM's Jonathan Taplin explains sentiment analysis and its relation to social media and 'big data'.