Data is at the center of every company. Through the information that is generated by a company’s processes on a daily basis, companies can improve decision-making capabilities now for better business results down the road. However, every day, companies generate more data. Statista predicts that by 2025 the world will reach the 181 zettabyte mark of data generated annually. As data volumes continue to grow and become more diverse, how will companies be able to reliably access data that can still tell them something of value about their business? Data virtualization is the way out of this predicament.
What Is Data Virtualization?
Data virtualization is a special kind of data integration technology that provides access to real-time data, across multiple sources and extremely large volumes, without having to move any data to a new location. Think of it as a cable TV interface that lists all available programs on a variety of channels, which viewers can preview without having to switch through the different channels.
With data virtualization, data consumers can access and manipulate each piece of data, regardless of its physical location or formatting. A wide variety of enterprise data sources can be accessed with data virtualization, including those on-premises and in the cloud. For this reason it can adapt quickly to companies’ existing infrastructures.
Why Is Data Virtualization Essential Today?
Modern organizations recognize that to make smarter decisions, delight their customers, and outperform their competitors, they must exploit their data assets more effectively. This data-driven business trend is nothing new, but due to the impact of the pandemic, the pace of transformation has increased dramatically.
According to Gartner, global CEOs believe that intelligent data and analytics will become a key technology to emerge from the COVID-19 crisis. Harnessing the power of data analytics and workflow automation is one way for companies to accelerate new revenue streams and reduce costs.
But this is not achieved without overcoming a major challenge: corporate data is stored in disparate locations with rapidly evolving formats such as:
- Relational and non-relational databases
- Cloud/software-as-a-service applications
- Data from social networks or websites
- CRM/ERP software data
- Data lakes and enterprise data warehouses
- Simple flat files in XML, CSV, or JSON formats
But the locations where data is stored are not the only challenge; the demand for faster and larger volumes of increasingly complex data also raises questions about how to provide self-service capabilities for data users, how to drive time efficiency in data management, and how to achieve reliable data quality.
To meet these challenges, organizations recognize the need to move from disparate data silos and isolated technologies to a business-focused data architecture in which data and analytics are simply part of the everyday lives of business users, because they can seamlessly access data when they need it. The first step to achieving this is implementing data virtualization.
Data Virtualization Key Benefits
Using data virtualization to integrate business data from different sources brings several benefits:
The acceleration of analytical processes
- Through immediate access to data, all data can be integrated in real time without extensive technical knowledge or manual coding effort.
- Real-time access differentiates data virtualization from slower, batch-oriented data integration approaches.
- Data virtualization promotes faster design and prototyping, as well as faster return on investment (ROI).
- Information is instantly available for a wide range of reporting and analytical functionality, greatly accelerating and improving the decision-making process.
Flexibility and simplicity
- Rapid prototyping enables faster test cycles before migrating to production environments.
- Data sources appear in a unified interface; data virtualization hides the underlying complexity of a heterogeneous data landscape.
- The virtual layer enables users to quickly adapt business logic to changing demands.
Unlike traditional data warehouses, no extra infrastructure is required with data virtualization, as the data remains on the source systems. This approach is more cost-effective because:
- A change in data sources or front-end solutions does not require a complex, expensive restructuring effort.
- Data virtualization acts as middleware, enabling existing infrastructure to be easily integrated with new applications, eliminating data silos.
- Having one data access point, rather than multiple points for each department, offers simple user and permissions management with full LGPD compliance.
- KPIs and rules can be centrally defined, so that critical metrics can be more easily understood and managed across the enterprise.
- With real-time access to data, errors can be detected and resolved more quickly.
The speed of innovation and the ability to adapt to rapidly changing market trends depends on a company’s ability to quickly analyze data. Data virtualization is the lever used by forward-thinking companies to do just that.
Here on the Kriptos blog, you’ll learn much more about how to use data for business. Keep following along and keep an eye out for all our articles!
- What is Data Virtualization? Understanding the Concept and its Advantages - February 17, 2022