Posts Tagged Data analytics
EMC has been touting its “Cloud Meets Big Data” messaging for nearly two years now, and today it took a major step in transforming that message into reality.
EMC announced that it is forming a new “virtual organization” focused on Big Data and application development in the cloud. EMC is calling the new organization the Pivotal Initiative and it will include 800 employees from EMC’s Greenplum and Pivotal Labs divisions, and 600 employees from VMware’s vFabric, Cloud Foundry, GemFire, SpringSource and Cetas organizations. EMC owns over 80% of VMware, where former EMC COO Pat Gelsinger joined as CEO earlier this fall.
Cloud computing, in-memory analytics, and mobility were the buzzwords at Day 1 of SAPPHIRE, but judging by Co-CEO Jim Hagemann Snabe’s own words, SAP is focused on one thing: simplicity.
“I actually believe that is one of the biggest tasks in the industry,” said Snabe, speaking to SiliconANGLE founder John Furrier and chief Wikibon analyst David Vellante live in theCube from the show floor. “Over the years we’ve added complexity and now it’s all about dramatically reducing the complexity. [But] not by solving simple problems. We need to continue to solve the complex problems of business, but we need to add the dimension of simplicity.”
It’s an accepted maxim that companies generally derive the majority of their revenue from existing customers. So you’d think perfecting the art of customer service and keeping existing customers happy would be a high priority at most companies. But it isn’t.
That’s clear to anyone that’s tried to call a mobile phone carrier or an airline only to get lost in a maze of automated prompts. Or waited for what seemed like an eternity for a response during an online chat session.
Even companies that do one silo of customer service well – say IVR or web support – often do so at the expense of other channels.
This week, SAS Institute unveiled a new analytics tool that it will offer in conjunction with data warehouse vendors Teradata and EMC-Greenplum. Called SAS High Performance Analytics, the tool will live inside the data warehouse, a technique known as in-database analytics that is becoming more and more popular in the era of Big Data.
By embedding scoring and modeling capabilities inside the database, in-database analytics allows users to run complex analytics against large data sets without having to transfer the data to a separate analytics or business intelligence application. Loading large volumes of data into an analytics platform can take hours or even days, and in some cases isn’t even possible. As a result, users must often be content to analyze just samples sets of data, which can sometimes lead to inaccurate analysis.
Integrating social media data analytics with customer relationship management (CRM) software has been getting a lot of attention lately, the latest development being Salesforce.com’s acquisition of Radian6.
But an Atlanta-based company called ClickFox is betting on a different type of CRM-related analytics it calls customer experience analytics. While social media analytics aims to help companies understand what customers are saying about them on the web, customer experience analytics is more concerned with identifying and improving how customers interact with companies both online and off by analyzing customer touch-point data.
It’s statistics 101: the larger the sample size, the more accurate the results.
So if you want to analyze your customers’ behavior patterns – do they shop online or in stores, when do they make purchases, how often do they make returns – the more customer data can run through your analytics engine the better your results.
But what if you didn’t have to rely on sample data sets, but could analyze all your customer data? You can’t get any more accurate a picture of customer behavior through data analytics than that.
In case you were unsure, I can confirm that the era of ‘Big Data’ is here to stay. Generating over 140 million Tweets a day, Twitter alone could keep the ‘Big Data’ moniker relevant on its own.
Companies of all types are eager to get their hands on data generated from Twitter, Facebook, blogs and other social media to better understand their customers. But they need help. There’s just too much data. That’s where Hadoop comes in.
In the pre-Twitter days, customer analytics basically consisted of loading some CRM and sales data into a data warehouse, slapping a business intelligence tool like Crystal Reports on top, and pumping out charts and graphs covering customer demographics and sales patterns.