SMEs should now consider data virtualization

When new technologies emerge, large telecoms, pharmaceutical and energy companies are deployed quickly, and retail, government and smaller businesses typically start to deploy after new technology has entered the mainstream market.

In view of the fact that data virtualization is already in the mainstream market, how is the data virtualization deployment of SMEs? How will these companies benefit from this?

What is Data virtualization?

Data virtualization is a method of data consolidation, a technology that companies use to provide users with data business perspectives, improve business agility, and reduce IT costs.

Data virtualization technology is a form of data integration middleware that utilizes high-performance software and advanced computing architectures to provide business information from multiple sources in a loosely coupled logical way.

Most business applications, including business intelligence, analytics, and trading systems, can access data through the data virtualization layer. This consumption comes from the demand of the original data source, including trading system, operation data storage, data warehouse and data mart, large data, external data source, etc.

Key enabling technologies include:

A data service view that uses the logical data model to provide complete, high-quality, and operational information

High-performance query algorithms and other optimization techniques to ensure timely data transmission

Standard API and development architecture to simplify consumer connectivity to data sources

What is the difference between data virtualization?

Over the past 20 years, the solution for accessing different data is to consolidate the data and generally integrate it into the data warehouse and related data marts.

By deploying a virtual data consolidation layer between the data consumer and the existing data source, the enterprise will greatly reduce its inability to consolidate and replicate data storage requirements.

Data virtualization enables enterprises to accelerate the delivery of new and revised business solutions while reducing solution costs when compared to the data Warehouse-centric consolidation approach.

Because data virtualization provides the flexibility and agility that traditional methods do not have, IT teams begin deploying data virtualization to complement their existing data warehouses and ETL deployments.

How does data virtualization improve the flexibility of business intelligence?

This is a very important question.

We analyzed data virtualization deployments for 10 enterprises. Business agility, which can help companies quickly seize new business opportunities, is a key differentiator for these enterprises from data virtualization deployments.

These enterprises found that data virtualization captures three elements of business agility: Business decision flexibility, solution flexibility, and resource flexibility.

For business decision flexibility, data virtualization helps provide the necessary knowledge and insight that comes from complete, high-quality, operational information.

Data virtualization provides solution flexibility for data consolidation, iterative development processes, and simple ways to adapt quickly to change. With data virtualization, these IT departments can quickly develop and provide the information necessary to support the new analysis and decision-making process.

Data virtualization also provides greater resource flexibility, mainly thanks to developer efficiency, lower infrastructure costs, and better data consolidation solution optimization. Because IT operations are one of the largest resource expenditures, and IT infrastructure is one of the largest capital expenditures, the benefits of this resource flexibility are significant and can save millions of of dollars a year for larger companies such as the New York Stock Exchange and Qualcomm.

(Responsible editor: The good of the Legacy)