What is Data Virtualization or DV?
Data Virtualization or commonly referred to as DV for short is an agile data integration layer for companies with Big Data or more than one data system. DV fosters more information for business intelligence, receives real-time federated data, and reduces operational overhead up to 90%.
Who needs Data Virtualization?
Data Virtualization is used by those who have company interest such as:
- Frequent mergers or acquisitions – Whether your company is forming corporate partnerships or acquiring new assets the integration of multiple data sources will soon follow. Conversely, the implementation of DV can reduce the overhead cost of these projects.
- Commodities in volatile markets – When it’s time to pull the trigger, data virtualization will be there to provide the business intelligence and analytics in real-time that is needed to move forward with confidence.
- Government cyber-compliance – Since healthcare requires secure agile integrations that comply with HL7; DV can pull data from multiple sources in a single dashboard view that is more secure than cloud systems and faster than staging databases.
How Can Companies Benefit from Data Virtualization?
- Business gets the trustworthy data it needs without delay. Other integration tools most likely use information silos or staging databases. While these are helpful to archive snapshots for historical use, they wont do you any favors when it comes to deploying this data for integration into Business Intelligence or analytics and reporting that’s needed to make decisions that are in the best interest of your business. How do you expect to conquer today with the data of yesterday?
- Lose “tools” that require maintenance and will drain your IT resources. Why get something that creates more problems than it solves? Most of these require hard coding to be integrated into custom data sources, producing an unstable outcome at best that forces IT teams to work overtime when a new object needs to be transformed. By the time the data is ready for consumption, it’s useless. Wouldn’t it be great to be able to federate data on the fly?
- Chaos is always lurking around the corner. Prepare wisely. Lets presume that you do have your process set and integrated just the way you need them to be. What if that database or cloud storage is comprised? With virtualization your data is “held”, not stored, thus proofing you from crashes. The caching function can also allow data to be reused, avoiding the page load created by multiple users hitting the same portal over and over again.
When should you use Data Virtualization?
Data Virtualization is best used when an enterprise needs to be agile when accessing real-time, secured, federated data to optimize the following activities:
- Analytics and Business Intelligence (BI) – Analytics and BI projects can suffer from the “gridlock” of data integration that attempts to consolidate data into a data warehouse, and then provide users with tools to analyze and report on this consolidated data. Using data virtualization as a complement to existing data integration approaches significantly accelerates the data integration agility by streamlining and using a more iterative development process with a more adaptable change management process.
- Address the unexpected with a sense of urgency – You need fresh data to solve the ever-evolving list of problems your business faces. Data Virtualization can take large volumes of transnational data and centralize and govern consumer accessibility in less time and less cost than ” legacy” integration tools.
- Extend Data Warehousing capabilities – Data Virtualization can accelerate the curve to help improve data warehouse deployments by integrating, cleaning, validating, and consolidating the copious amounts of important data that may exist outside of the warehouse. The flexibility afforded by DV ensures that the data warehouse can be leveraged as the enterprise grows and evolves.
How Does Data Virtualization Integrate?
Data virtualization is an innovative and dynamic new approach to data integration wherein an application is given access to retrieve and manipulate data as well as transform objects without requiring technical details about the data, such as how it is formatted or where it is physically located.
Legacy integration tools tend to rely extract, transform, load (“ETL”) process and then use an enterprise service bus or EBS to “drive” the data to users.
With DV, the data remains in place, and real-time access is given to the data regardless of source type, thus reducing the risk of data errors and reducing the workload of moving data around that may never be used. Different from data federation, this new integration pattern does not attempt to impose a single data model on the data. Data Virtualization also supports the bi-directional reading and writing of transaction data updates back to the source systems.
- What is Data Virtualization or DV?
- Why Harness Data Virtualization?
- What Are The Advantages of Data Virtualization?
- Who Needs Data Virtualization?
- How Does Data Virtualization Integrate?
- When Should Data Virtualization Be Utilized?
- How to Utilize Data Virtualization?
- Why Choose Stone Bond’s Enterprise Enabler with DV?