Data is one of the most valuable resources available to any enterprise. Unfortunately, many organizations still aren’t leveraging their data well enough to fully monetize it. For an enterprise to succeed in a modern world of big data and digital transformations, figuring out how to best use your data must be a top priority.
If you really want to practice effective data management and make your data work for you, then you’ll need integrated software systems. Modern businesses can no longer afford to have their data stored in disparate data warehouses or in software solutions that have no way to communicate. Sharing data between such sources would require frequent manual transfers—a process that is both time-consuming and prone to human error. This is why business users need to become familiar with data virtualization.
What is data virtualization?
Data virtualization software is the modern answer to the traditional extract/transform/load (ETL) process. This involves first pulling data from the original data source, which could be an application or data warehouse. You would then need to replicate data and modify it so that it’s compatible with the APIs your other systems are running. Finally, you would load the new data into the desired systems.
With data virtualization, this entire process can be done in a fraction of the time and at much lower costs. It collects all data into a modern data layer and handles the ETL process automatically, meaning that all your data can be combined and used in a single source of truth. Data that’s shared across multiple systems is referred to as master data, and managing this is crucial for business intelligence. Here are just a few of the greatest use cases for data virtualization and how it boosts industry growth.
It vastly improves data analytics
With all your data collected in one place, it’s much easier to analyze it for patterns and draw actionable insights from them. While you may have once relied on disparate data sources to store your customer data and sales data, you can now easily combine them to explore trends on a deeper level. For example, analyzing both sales data and customer demographics from your CRM system at once will give you deeper insights into which customer like which products, so your promotions can target your audiences better.
You can take things a step further with predictive analytics. With a combination of historical sales data and current customer trends, you can predict future market needs and prepare accordingly for a competitive edge.
It helps you optimize your supply chain
Every enterprise needs to optimize activity on the manufacturing floor, and there’s no fleet manager who wouldn’t love a way to plan more efficient routes. Data virtualization capabilities let you do both. Data virtualization makes it easier to process and analyze big data, and thanks to advancements in machine learning, image recognition can be used on factory floors to help assemble products. Not only that, but cameras with similar technology will be able to detect unusual activities and instantly send alerts to supervisors. With IoT sensors, you can even detect things like unusual temperatures to improve safety.
Sharing data between your inventory tracking software and your point of sale system also helps you automatically stay on top of product restocks. GPS systems can also share geolocation data with fleet managers to help them prepare the fastest and most convenient routes.
While there are plenty of other use cases, it will be virtually impossible to focus on all of them at once. To make the most of data virtualization, it’s important to prioritize use cases based on how much business value they offer. Over time, you can start expanding into other areas and take advantage of everything data virtualization has to offer.
