Big Data Analytics – Better with Data Virtualization

Big Data Analytics Make Business Sense

Big data analytic opportunities are abundant, with business value the driver. According to the Professors Andrew McAfee and Erik Brynjolfsson of MIT:

“Companies that inject big data and analytics into their operations show productivity rates and profitability that are 5% to 6% higher than those of their peers.”

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What Are The Top Data Virtualization Use Cases?

A recently published BI Leadership Benchmark Report lists eleven use cases for data virtualization. The report, Data Virtualization: Perceptions and Market Trends, which includes survey results from 192 BI professionals, was authored by Wayne Eckerson, Director, BI Leadership, a TechTarget research service.
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Integrating Analytic Data Using Data Virtualization

More Data = Better Analysis

The analytic data domain includes all kinds of data, including:

  • traditional enterprise data from sources like the Enterprise Data Warehouse;
  • big data from sources like Hadoop;
  • cloud data from SaaS applications and public providers;
  • social media data from sites like Facebook and Twitter; and
  • personal/desktop data from spreadsheets and flat files.

Data virtualization is an optimal solution to integrate all these sources, thereby improving analysis and business insight.

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Data Challenges in the Analytics Pipeline

Data is the lifeblood of analytics — the more diverse the better.

In their best-selling book, Big Data: A Revolution That Will Transform How We Live, Work, and Think, Mayer-Schonberger and Cukier describe the synergy that occurs when previously unrelated and disparate data is brought together to uncover hidden insights. But these advanced analytics data requirements are a double-edged sword as these more diverse sources complicate data integration and constrain progress.

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Solving the Analyst’s Data Problem

Recently I have been talking to a number of data scientists and business analysts about what they actually do when performing a new analysis of some nature. Their processes were quite surprising because they were far more data intensive and far less modeling / analysis intensive than I had thought.

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Can Data Virtualization Address the Data Integration Bottleneck?

We all understand the business value of BI and analytics as enablers for growth, a means to attract and retain customers, or a way to drive innovation and reduce costs.

CIOs do as well. Both Gartner’s Amplifying the Enterprise: The 2012 CIO Agenda and IBM’s Global CIO Study 2011 place BI and analytics atop CIO’s technology priorities.

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The Greatest Data Virtualization Day Ever!

Each year, I am responsible for leading the premier event in data virtualization, Data Virtualization Day.  It’s a huge responsibility and even bigger endeavor.  But the rewards are amazing, especially when the event is as successful as Data Virtualization Day 2012.

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Analytics the Driver for Latest Data Virtualization Platform Release

This week Composite Software introduced the version 6.2 of the Composite Data Virtualization Platform.

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TDWI’s Data Virtualization Class A Big Hit

I left for vacation right after attending Dave Wells’ Data Virtualization Class at TWDI in San Diego on August 1st, so I hadn’t had the chance to comment on the new course. It’s been a couple of weeks, but I still have positive vibes from the class, as did many of the attendees.

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TDWI Goes for Data Virtualization

You know times are changing if you see The Data Warehouse Institute (TDWI) offer a class that involves anything ‘virtual.’ But the data warehouse has been evolving, and today and tomorrow’s DW is not your father’s DW anymore. 

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