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Microservices Journal Authors: Liz McMillan, Pat Romanski, Elizabeth White, Kevin Jackson, Carmen Gonzalez

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Virtualization: Article

CIOs' Top Priority: Analytics and BI

How to Deal with the Data Integration Bottleneck

Whether as a driver for growth, a means to attract and retain customers, or a way to drive innovation and reduce costs, the business value of analytics and business intelligence has never been higher.

Gartner's Amplifying the Enterprise: The 2012 CIO Agenda as well as IBM's Global CIO Study 2011 confirm this point, with analytics and BI setting atop CIO's technology priorities in both reports.

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

Complex data landscapes, diverse data types, new sources such as big data and the cloud are but a few of the well-known barriers.

For the past two decades, the default solution has been to first consolidate the data into a data warehouse, and then 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 this right extends lead times.

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.

Data Virtualization 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 has been data virtualization's ability to provide:

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

Using data virtualization as a complement to existing data integration approaches, the ten organizations profiled in the book 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.

For more insights on data virtualization and business agility, check out my earlier articles on this topic.

Simplify to Overcome Historical IT Complexity

Data virtualization's simplified information access and faster time-to-solution is especially useful as an enabler for  more agile analytics and BI

Is Data Virtualization the Fast Path to BI Agility? describes how the architectures of most business intelligence systems are based on a complex chain of data stores starting with production databases, data staging areas, a data warehouse, dependent data marts, and personal data stores.   Simply maintaining this complexity is overwhelming IT today.

These classic BI architectures served business well for the last twenty years. However, considering the need for more agility, they have some disadvantages:

  • Duplication of data
  • Non-shared meta data specifications
  • Limited flexibility
  • Decrease of data quality
  • Limited support for operational reporting:
  • Limited support for reporting on unstructured and external data"

From a different point of view, SOA World's Zettabytes of Data and Beyond describes the challenges of force-fitting development methods that were appropriate for earlier times when less data complexity was the norm.

In addition, the proliferation of fit-for-purpose data stores including data warehouse appliances, Hadoop-based file systems, and a range of No-SQL data stores are breaking the hegemony of the traditional data warehouse as the "best" solution to the enterprise-level data integration problem.   The business and IT impact of these new approaches can be explored in the Virtualization Magazine article NoSQL and Data Virtualization - Soon to Be Best Friends.

Self-Service Analytics and BI are Important Too!
Responding to constantly changing business demands for analytics and BI is a daunting effort.

Mergers and acquisitions and evolving supply chains require new comparisons and aggregations. The explosion of social media drives demand for new customer insights. Mobile computing changes form factors. And self-service BI puts users in the driver's seat.

Business Taking Charge of Analytics and BI

In true Darwinian fashion, the business side of most organizations is now taking greater responsibility for fulfilling its own information needs rather than depending solely on already-burdened IT resources.

For example, in a 2011 survey of over 625 business and IT professionals entitled Self-Service Business Intelligence: TDWI Best Practices Report, @TDWI July 2011,The Data Warehousing Institute (TDWI) identified the following top five factors driving businesses toward self-service business intelligence:

  • Constantly changing business needs (65%)
  • IT's inability to satisfy new requests in a timely manner (57%)
  • The need to be a more analytics-driven organization (54%)
  • Slow and untimely access to information (47%)
  • Business user dissatisfaction with IT-delivered BI capabilities (34%)

In the same survey report, authors Claudia Imhoff and Colin White suggest that IT's focus shifts toward making it easier for business users "to access the growing number of dispersed data sources that exist in most organizations."

Examples Imhoff and White cite include:

  • providing friendlier business views of source data
  • improving on-demand access to data across multiple data sources
  • enabling data discovery and search functions
  • supporting access to other types of data, such as unstructured documents; and more.

Data Virtualization to the Self-Service Rescue

In the TDWI survey, 60% of respondents rated business views of source data as "very important," and 44% said on-demand access to multiple data sources using data federation technologies was "very important."

According to Imhoff and White, "Data virtualization and associated data federation technologies enable BI/DW builders to build shared business views of multiple data sources so that the users do not have to be concerned about the physical location or structure of the data.

These views are sometimes known as virtual business views because, from an application perspective, the data appears to be consolidated in a single logical data store. In fact, it may be managed in multiple physical data structures on several different servers.

Data virtualization platforms such as the Composite Data Virtualization Platform support access to different types of data sources, including relational databases, non-relational systems, application package databases, flat files, Web data feeds, and Web services.

To Achieve Self-Service BI, Consider Using Data Virtualization provides additional insights on about how data virtualization enables self-service analytics and BI.

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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