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CloudExpo® Blog: Article

Unify Cloud, Big Data and Enterprise Data with Ease

Make Better Business Decisions using More Data

While Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS), and Infrastructure-as-a-Service (IaaS) offer applications and resources at an attractive, pay-as-you-go price, they also create silos of data that are difficult to access by your analytics and BI solutions.

Likewise, Big Data offers new storage techniques, processing capabilities and analytic opportunities.  But traditional analytics and BI solutions are typically built assuming relational, SQL-based sources. These often struggle when trying to deliver value from these new No-SQL sources.

Cloud and Big Data Silos Add Value to Analytics and BI
Analytics and BI opportunities today are abundant and can significantly add value to a business. 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."

Data is the critical success factor.  Because without data, there can be no analysis. The more data the better, including data sourced from the cloud, Big Data and existing enterprise data warehouses.

The Data Integration Bottleneck
Providing analytics and BI solutions with the data these require has always been difficult, with data integration long considered the biggest bottleneck in any analytics or BI project.

For the past two decades, the solution has been to consolidate the data into a data warehouse, and provide users with tools to analyze and report on this consolidated data.  However, data integration based on these traditional replication and consolidation approaches have numerous moving parts that must be synchronized.  Doing these properly slows solution delivery.

The Data Warehousing Institute confirms this lack of agility. Their recent study stated the average time needed to add a new data source to an existing BI application was 8.4 weeks in 2009, 7.4 weeks in 2010, and 7.8 weeks in 2011. And 33% of the organizations needed more than 3 months to add a new data source.

Cloud Data Integration is Especially Challenging
Besides being slow to develop, traditional consolidation ETL approaches don't work well with cloud data because you don't want to physically move large datasets over the internet.  Further, the cloud requires specialized integration techniques, beyond many ETL products today.

For example, when you integrate data from a SaaS provider such as Salesforce.com with an on-premise customer data warehouse, you will need:

  • deep knowledge of Salesforce's APIs
  • a common semantic definition that rationalizes terms and  structures
  • the ability to query data through a firewall across the Web
  • on-demand, rather than batch mode, integration operations.

How to Simplify Cloud and Big Data Integration
To provide the data that analytics and BI applications require, data virtualization products such as the Composite Data Virtualization Platform allow enterprises to flexibly integrate cloud, Big Data and enterprise data warehouses.

Key cloud and Big Data integration capabilities include:

  • Rapid, Standard-based Development - Data virtualization supports a wide range of cloud based and Big Data sources via industry-standard APIs that simplify and speed new development.
  • Internet-friendly Data Services - Data integration services authored and run by data virtualization platforms are perfectly suited to operate across the Internet.
  • Lowest Cost Approach - Because data virtualization accesses, federates, abstracts and delivers queried data on demand, no additional cloud-based or big data storage is needed.
  • Safe and Secure - Further, data virtualization's support of multiple security models ensures proper authentication, authorization and encryption both on-premise and in the cloud.
  • Rich Source Data Adapters - Data virtualization easily integrates with popular SaaS applications such as salesforce.com, IaaS cloud-hosted applications such as SAP and Oracle E-Business, as well as popular Big Data types such as Hadoop, often using Hive to translate HDFS files to more analytic and BI friendly SQL formats.

Data Virtualization also Brings Agility to Analytics and BI
According to Data Virtualization: Going Beyond Traditional Data Integration to Achieve Business Agility, data virtualization significantly accelerates data integration agility. Key to this success is data virtualization's

  • more streamlined data integration approach
  • more iterative development process
  • more adaptable change management process

Using data virtualization as a complement to existing data integration approaches, the ten organizations profiled in the book, many who were integrating cloud and Big Data, cut analytics and BI project times in half or more.

This agility allowed the same teams to double their number of analytics and BI projects, significantly accelerating business benefits.

Make Your CIO Happy
CIOs understand the importance of analytics and BI. According to a 2012 survey of 2300 CIOs by Gartner, analytics and BI are their number one technology priority.

Analytics and BI can make bigger business impact when they can access more data. With data in the cloud and Big Data silos, accessing and integrating these new sources can be a challenge for enterprises used to a traditional enterprise data warehouse centric data integration approach.

Data virtualization enables integration of cloud, Big Data and enterprise data warehouse sources with ease and agility.

So make your CIO happy and give data virtualization a try. You'll both be glad you did.

More Stories By Robert Eve

Robert Eve is the EVP of Marketing at Composite Software, the data virtualization gold standard and co-author of Data Virtualization: Going Beyond Traditional Data Integration to Achieve Business Agility. Bob's experience includes executive level roles at leading enterprise software companies such as Mercury Interactive, PeopleSoft, and Oracle. Bob holds a Masters of Science from the Massachusetts Institute of Technology and a Bachelor of Science from the University of California at Berkeley.

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