Organizations have traditionally relied upon conventional data integration solutions to fulfill their business intelligence goals. However, with the rise in the complexity of IT infrastructures, a growing need for real-time access to data for effective analytics and decision-making have forced businesses to look for solutions like Data Virtualization to cater to their Business Intelligence requirements.
Data Virtualization allows companies to access data from disparate sources like data warehouses, NoSQL databases and data lakes without any physical data movement, through a virtual layer that hides source data complexities from the end-user.
Since Data Virtualization does not require large infrastructure, implementation costs are significantly low. According to Forrester, data virtualization is becoming a critical asset for enterprises looking to overcome big data challenges, today. Moreover, in a 2017 report, Gartner predicted that the organizations embracing data virtualization will be spending 40% less on integrating data from a diverse set of sources as opposed to those who adopt the traditional data unification techniques.
These numbers prove that many big game players are considering Data virtualization to streamline their data integration process.
Now that we know what data virtualization is, let’s delve deeper and look at some of the benefits of data virtualization and how it can consolidate data silos in business.
Three Benefits of Data Virtualization
In addition to decreased implementation costs, data virtualization technology offers some other significant advantages for effective data warehousing like:
Real-time access to data
Information is readily available for effective BI and analytics because most deployments are updated as soon as source data is changed. This instant access to current data aids enterprises in making significant business decisions.
Moreover, integrating data from diverse sources allows information from data lakes, data marts, legacy databases, and NoSQL databases to be unified within a single, virtual layer. This hides data source complexities, requiring less technical involvement for reporting and analysis and serves as a distinct point of access for the end-users. Additionally, it accommodates new data sources by virtually adding them to the enterprise data warehouse, giving the system the ability to provide real-time access of information to its users.
Data virtualization uses a simplified infrastructure to combine information from an array of sources and organizes them for query and analysis. Legacy data integration tools and ETL are good for physically moving data in bulk but since it uses a series of complex transformations, it is a good idea to test the system with tools like data virtualization first, without building point-to-point integrations.
Enhanced security and data governance
Since there is a central access point for data, improved authorization management gives users an enhanced security experience. KPIs and rules can be defined centrally; therefore enterprises better understand the key performance metrics.
Global metadata information helps in providing a deep understanding of the organization’s data through features like data lineage and metadata catalogs. Furthermore, system faults can be detected and resolved faster as compared to other data integration techniques.
How Data Virtualization helps Consolidate Data Silos
With features like query pushdown, caching and query optimization, Data Virtualization can address many of the big data pain points. Let’s look at a few use cases as examples.
Agile Business Intelligence
Legacy BI systems today are unable to keep up with the growing enterprise BI requirements. Businesses now need to operate more competitively. For this reason, they need to increase the agility of their processes. Currently, businesses have rigid and static structures.The most dormant cause for this rigidity is the design itself, which is database-centric. The architectures are mostly based on a chain of data stores like the production databases, data staging areas, data warehouses, and data marts, etc. which compromise on their ability to deliver results.
Data Virtualization, on the other hand, can make systems more agile by incorporating data in an on-demand fashion. Moreover, it provides unified access in a centralized layer where data can be integrated, transformed and cleansed. Through DV organizations can also generate consistent BI reports for analysis with simplified data structures and deliver outputs to relevant decision-makers instantly. Therefore, even with information coming from myriad sources, applications only access a single, large database. This is just one example of data virtualization.
Virtual Operational Data Store
Another interesting use of Data Virtualization is the Virtual Operational Data Store (VODS). With VODS, users can perform further operations such as monitoring, reporting, and control on the data assessed through data virtualization. A good example of VODS is of GPS apps. Travelers can use these apps to look for the quickest route to a particular destination.
A VODS virtually collects data from an array of data stores and creates reports on the go. The traveler, therefore, gets information from a diverse pool of sources without having to worry about the original data store.
Three Things to Consider for Data Virtualization
Data virtualization can efficiently eliminate data silos in enterprise data systems. But, without a careful, thought-out plan, things with Data Virtualization like manageability, data quality and performance might become challenging. So, ensure that you consider the following aspects when using Data Virtualization:
Design from an enterprise perspective
A Data Virtualization solution should be able to evolve with the business requirements. It becomes rigid as layers are added to it. With duplicate business logic, testing system performance gets challenging. So it is a good idea to sit with the system architects in time to analyze the system infrastructure pitfalls.
Consider Data Security
The extent of security required for an enterprise system impacts its data virtualization design because it helps expose a virtual view of a broad set of information to the system users.
For example, if the data is being revealed to new users, Information security can help determine the type of regulations .e.g, Health Insurance Portability and Accountability Act (HIPAA) that can be applied.
Assess Data Quality
In some cases, Data Virtualization can be leveraged to provide secure access to an enterprise’s operational data. Data quality teams may use this to analyze system data for data quality-related issues and consider resolving them.
How to Get Started with Data Virtualization?
To get started with enterprise data virtualization, you should have a working plan, considering the aforementioned aspects. It is critical to take C-level executives on board, explaining the benefits alongside the numerous Data virtualization examples to them. In addition to this, conduct in-depth research on tools specially designed for effective data virtualization such as Astera Centerprise.
To catch up with ever-increasing data complexity, agile data integration approaches such as Data virtualization have become critical. Organizations can promptly retrieve up-to-date information from diverse sources at a unified point of access. Furthermore, features like database caching and query optimization make it ideal for effective business analytics and reporting.
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