|By Robert Eve||
|February 15, 2013 08:00 AM EST||
Last July, I wrote Data Virtualization Q&A: What's It All About, an ambitious article that attempted to address the topic of data virtualization from numerous angles including use cases, business benefits, and technology.
Since then, with the continued rapid expansion of big data and analytics, as well as data virtualization technology advances, my 360 degree view of data virtualization has evolved.
Data Rich, Information Poor
As I think about data virtualization today, the big data and analytics challenge that data virtualization best addresses is helping enterprises take advantage of their data.
In other words, enterprises today are data rich with loads of enterprise, cloud, third party and Big Data. But they remain information poor.
In this context, let's consider the role of data virtualization with ten, back-to-the-basics questions and answers.
What is Data Virtualization?
Data virtualization is an agile data integration approach organizations use to gain more insight from their data.
Unlike data consolidation or data replication, data virtualization integrates diverse data without costly extra copies and additional data management complexity.
With data virtualization, you respond faster to ever changing analytics and BI needs, fast-track your data management evolution and save 50-75% over data replication and consolidation.
Why Use Data Virtualization?
With so much data today, the difference between business leaders and also-rans is often how well they leverage their data. Significant leverage equals significant business value, and that's a big advantage over the competition.
Data virtualization provides instant access to all the data you want, the way you want it.
Enterprise, cloud, Big Data, and more, no problem!
What Are the Benefits of Data Virtualization?
With data virtualization, you benefit in several important ways.
- Gain more business insights by leveraging all your data - Empower your people with instant access to all the data they want, the way they want it.
- Respond faster to your ever changing analytics and BI needs - Five to ten times faster time to solution than traditional data integration.
- Fast-track your data management evolution - Start quickly and scale successfully with an easy-to-adopt overlay to existing infrastructure.
- Save 50-75% over data replication and consolidation - Data virtualization's streamlined approach reduces complexity and saves money.
Who Uses Data Virtualization?
Data virtualization is used by your business and IT organizations.
- Business Leaders - Data virtualization helps you drive business advantage from your data.
- Information Consumers - From spreadsheet user to data scientist, data virtualization provides instant access to all the data you want, the way you want it.
- CIOs and IT Leaders - Data virtualization's agile integration approach lets you respond faster to ever changing analytics and BI needs and do it for less.
- CIOs and Architects - Data virtualization adds data integration flexibility so you can successfully evolve your data management strategy and architecture.
- Integration Developers - Easy to learn and highly productive to use, data virtualization lets you deliver more business value sooner.
How Does Data Virtualization Work?
Data virtualization's business views provide instant access to the data your business users require, while shielding them from IT's complexity.
- Develop - Your IT staff uses data virtualization's rich data analysis, design and development tools to build the business views (also known as data services).
- Run - When your business users run a report or refresh a dashboard, data virtualization's high-performance query engine accesses the data sources and delivers the exact information requested.
- Manage - Data virtualizations management, monitoring, security and governance functions ensure security, reliability and scalability.
Data virtualization vendor products such as the Composite Data Virtualization Platform provide all these capabilities in a complete and unified offering.
When to Use Data Virtualization?
You can use data virtualization to enable a wide range of information solutions including:
- Agile Analytics and BI Solutions
- Data Warehouse Extension Solutions
- Logical Data Warehouse Solutions
- Data Virtualization Architecture Solutions
- Data Integration and Management Solutions
- Business Solutions
- Industry Solutions
When Not to Use Data Virtualization?
Data virtualization is not the answer to every data integration problem. Sometimes data consolidation in a warehouse or mart, along with ETL or ELT is a better solution for a particular use case. And sometimes a hybrid mix is the right answer.
You can use a Data Integration Strategy Decision Tool to help you decide when to use data virtualization, data consolidation or perhaps a hybrid combination.
What is the Business Case for Data Virtualization?
Data virtualization has a compelling business case. The following drivers make data virtualization a "must have" for any large organization today.
- Profit Growth - Data virtualization delivers the information your organization requires to increase revenue and reduce costs.
- Risk Reduction - Data virtualization's up-to-the-minute business insights help you manage business risk and reduce compliance penalties. Plus data virtualization's rapid development and quick iterations lower your IT project risk.
- Technology Optimization - Data virtualization improves utilization of existing server and storage investments. And with less storage required, hardware and governance savings are substantial.
- Staff Productivity - Data virtualization's easy-to-use, high-productivity design and development environments improve your staff effectiveness and efficiency.
- Time-to-Solution Acceleration - Your data virtualization projects are completed faster so business benefits are derived sooner. Lower project costs are an additional agility benefit.
How to Deploy Data Virtualization?
You can start your data virtualization adoption with specific projects that address immediate information needs.
Which Vendor Should I Select?
If you are like most, you would prefer to go with data virtualization market leader. But how do you define the market leader
Is it the one with the most mature product? For example, one data virtualization vendor has spent a decade delivering nearly 400 man years of R&D, six million lines of code and millions of hours of operational deployment.
Is it the one with the most installations? For example the same vendor is used by nearly two hundred of world's largest organizations
Is it the one with them most domain knowledge? This same vendor's data virtualization thought leadership assets demonstrate the expertise they can bring to bear for you. These include:
- The first book on data virtualization, Data Virtualization: Going Beyond Traditional Data Integration to Achieve Business Agility.
- Data virtualization's foremost microsite, the DV Café
- The Data Virtualization Leadership Series of analyst reports on data virtualization
- Data virtualization's only dedicated blog, the Data Virtualization Leadership Blog
- The Data Virtualization Channel on YouTube with users, analysts, chalk talks and more
- The Data Virtualization Leadership Awards honoring users
- Data Virtualization Day Resources, assets from the premier events in data virtualization
- Data virtualization's longest running newsletter, Enterprise Information Insight
With so many new opportunities from Big Data, analytics and more, today's challenge is how to take big advantage. This article suggests that data virtualization can be that path, and provides answers to key questions about data virtualization. The time is now.
The essence of data analysis involves setting up data pipelines that consist of several operations that are chained together – starting from data collection, data quality checks, data integration, data analysis and data visualization (including the setting up of interaction paths in that visualization). In our opinion, the challenges stem from the technology diversity at each stage of the data pipeline as well as the lack of process around the analysis.
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