It’s more important than ever for companies to get a grip on their data. This starts with data virtualization technology that acts as a bridge across multiple diverse data sources to fuel analytics. These analytics allow for better business intelligence to get companies out in front of their competition. Providing a modern data layer enables users to access, transform, and deliver datasets with breakthrough speed and cost-effectiveness. There are many ways in which data virtualization technology can be a game-changer for businesses in a variety of sectors.
Data Virtualization Capabilities
Having the proper data virtualization tools in place has a tremendous impact on analytics for business users to get ahead of the game. This expands from databases and data lakes to cloud data and documents from various platforms. There are also common systems that can use virtualization including Oracle, Excel, and Amazon Redshift. This establishment of the right virtual layers makes sure that no data remains untouched to get a proper assessment of the information before you and your employees.
There are four components needed to meet urgent business needs with data virtualization. Agile design and development help to model individual services while making modifications to automate difficult work and improve time to get to solutions. High-performance runtime allows for virtualization to create optimized queries that allow for up-to-the-minute analytics and less replication. Caching essential data allows for the proper information to be delivered across a web service, boosting performance. This is also accomplished through a catalog that makes all available data easier to find.
Virtualization Use Cases
Data virtualization technology is established across three main use cases: analytical, operational, and emerging. Analytics use cases highlight the need for easier access for data storage and maintaining data governance through a virtual outlet to integrate physical data. This makes for a semantic layer for analysis, and the establishment of a logical data warehouse that is able to handle a workload of any size at any time. By having this technology, there is a better method of preparation for varied data sources, and an ability to implement regulatory constraints on the information.
Operational cases put an emphasis on legacy system migration into a modern data center for easier virtual access, eliminating the risk of data errors brought on by a lack of transparency or standardized systems for input. Having this abstract data access is beneficial to maintaining a registry-style master data management system. Emerging cases focus on taking data sources into the next generation to garner insights for business decisions. This includes cloud-based data sharing, data hub enablement, and edge data access in IoT integration.
Industry Sectors and Data Virtualization
One of the greatest qualities of data virtualization is that it’s beneficial to just about any business. In the communications and technology sector, companies are now able to differentiate their market research services, taking this big data to help increase their revenue per customer. This leading-edge innovation improves customer insights and optimizes customer care. In the energy sector, data virtualization architecture can optimize production, while improving upon maintenance and repair based on the data quality and the delivery of information in less time than ever before.
In the financial services realm, data virtualization software is helping business users to improve trade reconciliation and empower data democracy, accelerating client onboarding. For manufacturers, virtualization dashboards can help optimize logistics, as well as the global supply chain, to improve asset utilization. In the health care industry, data virtualization can actually drive the innovation of new products, improving patient care in the long run. The truth is there are plenty of applications for this form of self-service access for business users to get the most of their raw data.