|By Robert Eve||
|January 1, 2012 09:30 AM EST||
Enterprise adoption of data virtualization has accelerated along with a growing need for greater business agility.
The close relationship of business agility and data virtualization was described in my recent Virtualization Magazine article, The Agile Business - Why Data Virtualization Is Needed.
It can also be observed across hundreds of organizations and is clearly evident in the ten case studies described in the recently published Data Virtualization: Going Beyond Traditional Data Integration to Achieve Business Agility.
Three Elements of Business Agility
In their quest to become agile businesses, these organizations address all three elements of business agility: business decision agility, time-to-solution agility and resource agility.
This article addresses how data virtualization delivers business decision agility. Part 2 and Part 3 will address time-to-solution agility and resource agility.
Agile Business Decisions Are Key
Making effective business decisions requires knowledge and insight that can only be developed from access to and analysis of complete, high-quality actionable information. Data virtualization enables the organization to deliver this information in several ways.
Understanding the complete picture is the first step in any decision process. Large enterprises today have thousands of data sources that span multiple transaction systems of record, complementary applications, consolidated data stores, external data sources and more. Each source is a silo with its own unique metadata and data model, data access toolset, and underlying architecture.
The challenge is to integrate data across these traditional silos in order to provide the business user with a single, complete and high-level view of whatever information is needed for analysis and decision making.
Taking this concept to the enterprise level, data virtualization has the ability to provide an organization with a unified view of information across the entire business.
One traditional solution is to consolidate all of the data in a unified, enterprise data warehouse. However, this does not always prove feasible in practice for a number of reasons, including the ongoing proliferation of new data sources and types that must be incorporated into the warehouse.
As an alternative, data virtualization offers virtual data federation functions that enable an organization to integrate its extensive range of internal and external data sources without moving any data. Accessing the data in a new source, for example, simply requires establishing a single connection - from the data virtualization layer to the source - and the creation of virtual views and data services to access, transform, abstract and represent the source data in an appropriate format for consuming applications.
Ensuring that the information guiding business decisions is high quality and fit-for-purpose is a second major business decision agility requirement.
It is rare that as-is, raw data in original source systems is an exact match for the consuming application and business user. At a minimum, some degree of format and syntax transformation is required to bridge the gap between source and consumer data models and technologies. In many cases, additional validation and standards-conformance processing is also needed to improve the integrity and consistency of the data delivered to consumers.
Data virtualization supports multiple techniques to ensure delivery of high-quality information. For example, when providing up-to-the-minute, institution-wide views of equity, option, futures, derivative and debt positions for risk managers in the financial services industry, data virtualization transforms the data as structured in the various trading platform sources into a consistent form and format for consumption by the risk management applications.
Finally, the information decision makers require must be actionable. Time-to-action is an additive function that combines time from event-to-insight, insight-to-decision, decision-to-implementation and implementation-to- results.
Yesterday's data, summarized in the warehouse, is not sufficient when having the most current information is the first step in a time-to-action path. For example, understanding the current location and availability of maintenance staff and repair gear is a critical first step in understanding how to respond to an equipment failure in process industries.
Data virtualization provides high performance query engines and flexible caching to query and deliver source data in near-real time whenever it is requested. This ensures decisions are made based on the most up-to-date information available when appropriate.
Making effective business decisions requires knowledge and insight that can only be developed from access to and analysis of complete, high-quality actionable information.
Data virtualization offers virtual data federation functions that enable an organization to integrate its extensive range of internal and external data sources to provide the business with a complete picture. Further, data virtualization supports multiple techniques to ensure delivery of high-quality information. Finally, data virtualization can query and deliver source data in near-real time whenever it is requested to ensure up-to-date, actionable information.
Editor's Note: Robert Eve is the co-author, along with Judith R. Davis, of Data Virtualization: Going Beyond Traditional Data Integration to Achieve Business Agility, the first book published on the topic of data virtualization. This series of three articles on How Data Virtualization Delivers Business Agility includes excerpts from the book.
- Five Ways Data Virtualization Improves Data Warehousing
- Data Virtualization Technology Advancements Deliver New Value
- Data Virtualization at Pfizer: A Case Study
- Why Bother to Abstract Your Data?
- Data Virtualization Adoption Propelled by Significant Business Benefits
- Extend MDM with Data Virtualization
- Will Data Virtualization Work for Me?
- It’s Here! The First Book on Data Virtualization
- Roadmap for Data Virtualization
- How to Evaluate a Data Virtualization Platform