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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How Data Virtualization Addresses the Big Data Integration Skills Shortage

Big data opens the door to unprecedented analytic opportunities for business innovation, customer retention and profit growth. However, the big data skills shortage is creating a bottleneck at every organization today as they move from early big data experiments into enterprise scale adoption. This constraint limits big data analytics success.

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New Research: Data Virtualization Perceptions and Market Trends

A newly published BI Leadership Benchmark Report concludes that data virtualization software is the key to creating an agile, cost-effective data management infrastructure.

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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Going Deep on Analytic Sandboxes and Data Hubs

Richard Sherman, founder of Athena IT Solutions, which provides business intelligence, data integration and data warehouse consulting and training, has published two new white papers describing a new generation of analytical sandboxes and analytical hubs and their emerging role in enabling agile analytics.

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Trip Report from Gartner BI and Analytics Summit

Last week Composite Software was at the Gartner BI and Analytics conference in Dallas http://www.gartner.com/technology/summits/na/business-intelligence/

It was an incredible event in terms of content and community including:

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Don’t Let Legacy Systems Slow You Down

In our ever changing technology environment, new opportunities are everywhere.  Predictive analytics, big data, mobile, cloud and virtualization are but a few.

These new technologies improve business competitiveness, increase agility, save money and more.

But migrating from existing systems can be risk prone.  And efforts often drag out.

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Architecting Analytic Data Hubs

I Love Solution Architecture

I have been doing enterprise architecture for a long time. From my early days as a consultant at Accenture to my role at CTO of webMethods and for the past several years working with Composite Software’s largest enterprise customers, I find enterprise architecture a wonderful challenge.

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Capgemini Weighs in on Data Virtualization

New Report Addresses BI Urgency

Data Virtualization: How to get your Business Intelligence answers today is the title of a new thought leadership white paper from the Business Information Management group at Capgemini.

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