Common Use Cases for Data Virtualization Software
In the business world, data is everything. Indeed, in recent years, many companies have come to realize that business data and customer data are their most valuable resources. Big data is much more than the hot buzz phrase in tech and business—it’s an entirely new way of looking at and running a business.
Of course, all businesses are data consumers, but it’s how they manage their data that makes the difference. Getting the most from your business intelligence infrastructure requires the right tools and strategy. Data virtualization tools have become increasingly popular with the growth of big data, so we’ll take a look at some use cases for this powerful tool. Continue reading to learn about some of the more common data virtualization use cases.
Data management teams use virtualization for data integration.
Once upon a time, data scientists and data management teams had to manually implement data integration through a process called extract, transfer, and load (ETL). It’s about as fun as it sounds and more time- and cost-consuming than you could dare to imagine.
One of the problems with ETL is, as the name suggests, it’s a three-step manual process in which data management teams have to extract data, transfer it to a device and format it, and then load it onto its destination platform. Data virtualization architecture enables the automation of data formatting, expediting data integration processes exponentially.
Business users implement data virtualization for real-time analytics.
Business realities can change in the blink of an eye, and business users value systems that enable them to see these changes in real time. One of the ways business users employ data virtualization is to capture and analyze real-time data. Having fast access to advanced analytics enables them to make decisions much quicker and monitor the results as they roll in.
Data scientists use virtualization to build data warehouses.
One of the challenges data consumers face is managing data from disparate external sources. Data silos often have their own data models, making it difficult for end-users to locate and decipher data insights. Data engineers often use data virtualization middleware to create a logical data warehouse and provide a single format for all their disparate data.
Data virtualization enforces data quality across disparate source systems.
Another prime function of data virtualization tools is promoting data integrity across disparate source systems. TIBCO data virtualization tools mitigate the need for data migration and replication, ensuring all your data is unique and actionable. There’s nothing like data replicates to make you think you’ve found a pattern when actually, all you’ve found is the data you’re already using.
Business users are major data consumers and makers. They need tools that can keep up with the lightning pace of the business world, and data virtualization software is that tool. Data integration is one of the more common use cases for data virtualization tools. Data consumers also use it to get analytics in real time and make better and faster business decisions. Furthermore, they can create a data virtualization architecture for all of their databases and store them in a logical data warehouse for future use. They often use data virtualization for data federation, enabling them to pool all of their databases in one place for easy access.
As you can see, there are numerous applications for data virtualization software. If you’re wondering whether your company needs data virtualization for its business intelligence operations, you can answer it by asking yourself if your company consumes data from disparate sources. Indeed, data virtualization software is something all companies can use to maximize their enterprise data operations.
