Data virtualization is a newer technology that is being used to manage data. Virtualization brings the physical into the digital in a logical format. By gathering data from various, disparate sources, data virtualization allows data to be stored in different locations and accessed through a single point of access. This makes data from different data sources easier to manage and reduces the amount of data storage space. In this article, we’ll go over some examples of data virtualization and what they mean in regard to data management.
What are the benefits of data virtualization?
Because data virtualization is the process of creating a single source of truth out of various sources of data, there are several benefits to its implementation.
Large companies can access and use data from both internal and external sources of data stores; disparate data sources become consolidated into one, accessible data store. This allows for the creation of a data model, something which further simplifies the data virtualization process, making it easier to change or add to the data stores. A well-designed data model can improve performance and reduce the complexity of your source data. A bad design for a data model will do the opposite and lead to frustration. You can enlist the help of a software developer with a background in data governance like data virtualization technology to help build your model.
Another benefit of data virtualization through a model is that it makes marketing a faster process, speeding up business processes that are consumer-based and require taking big data sources and making sense of them. The improved agility and response time to market changes are included in data virtualization.
Data virtualization meets the number one business need: reduced costs. Data virtualization reduces the need to duplicate systems to store different copies of the same data source. It can also reduce bandwidth requirements to make it easier for business users and their analysts to manage big data sources.
What are some uses for data virtualization?
Now that we know what the benefits of data virtualization are, what are some examples of it? The examples all narrow down to how you want to access the data: all at once all the time, or whenever you need it.
Data virtualization works by creating a “virtual” or “logical” table. This table is populated by data from the various source systems with an optimized query process compared to other tables. The advantage of using a virtual table is that you don’t have to worry about the underlying structure of the data or its storage structure. You simply specify what information you need and let the system take care of the rest.
There are several different ways to create a virtual table. One common approach is to use a data warehouse or data mart as a central repository for all the data. This can be especially useful if you want to consolidate information from multiple source systems into a single view, creating a logical data warehouse, which is better for analytics.
Another option is to use an ETL (extract, transform, load) tool to populate the virtual table automatically on demand. This approach can be useful if you want to keep your source systems separate but still have access to consolidated data for reporting and analysis purposes. This means your different data sources will still be separate, but virtualization will still give you a single point of access when you need it. This is a good choice to pair with a CRM and other data management services.
One of the most important aspects of any business is the ability to manage and access data. However, as a business grows, it can become increasingly difficult to manage all that data. This is where data virtualization comes in. Data virtualization allows businesses to manage and access data in a more efficient manner, which can help improve business operations.
Data virtualization is an important process for businesses because it allows them to combine data from multiple sources into one cohesive unit. This is helpful for businesses because it allows them to get a better understanding of their data and how it relates to each other.