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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Archiving with Big Data = Better Business Results

Business Value from Mixing Current and Historical Data

Historical data is now an essential tool for businesses as they struggle to meet increasingly stringent regulatory requirements, manage risk and perform predictive analytics that help improve business decisions.And while recent data may be available in from operational systems and some summarized historical data available in the data warehouse, the traditional practice of archiving older, detail-level data offsite on tape makes business analytics challenging, if not impossible, because the historical information needed is simply unavailable.

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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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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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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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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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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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Boulder BI Brain Trust Trip Report

In my last blog, Data Virtualization Returns to the BBBT, I announced Composite’s fifth presentation to the Boulder BI Brain Trust on January 4, 2013. This blog is a “trip report” covering that visit.

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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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