How Data Virtualization Is Changing The Business Landscape

Last Updated : 23-February-2024

Take a second and think about the types of data formats you have used today. Does it include some or all of these? - .xls (Excel), .pptx (Powerpoint), CRM, .jpeg (images), pdf, Databases, Visualization tools etc.

If your answer is yes, then you can think about how companies are transforming to adapt their businesses to growing demand, competitiveness, and changing requirements by integrating with various data sources. One of the key requirements with such process and business integration is that such integration shouldn’t impact neither their business nor the customer’s perception.

Big Data Virtualization is the modern answer to unleash your enterprise architectures from the burden of data replication, speeding up the tasks of data cleansing, integration, federation, transformation, and presentation.

According to Gartner “Through 2022, 60% of all organizations will implement data virtualization as one key delivery style in their data integration architecture.”

Data virtualization (DV) is a data access platform that aggregates disparate data sources to create a single version of the data set for consumption. It provides a unified, abstracted, organized and encapsulated view of the data coming from similar or heterogeneous (diverse type) data sources while the data remains in source systems.

Data virtualization solutions addresses the data movement challenge by ensuring data remains at the source — yet is also available for consumption in real-time for consuming applications.

Its data collaboration approach allows an application to retrieve data as a single view component without the user requiring its technical details, such as its physical location, source formatting information, security parameters, configuration settings, etc. This platform substitutes extract-transform-loads (ETLs) and data warehousing in areas such as - business intelligence and analytics, application development and big data consumption.

Organizations have traditionally anticipated conventional data integration solutions to fulfil their business goals. With the sudden rise in the complexity of IT infrastructures, a growing need for real-time access to data for effective decision-making and analytics have forced businesses to look for solutions like Data Virtualization to cater to their business requirements.

Data Virtualization use cases allows organizations to access data from disparate sources like DW, data lakes, and NoSQL databases without any physical data movement, through a virtual layer that hides source data complexities from the end-user.

Since Data Virtualization does not require extensive infrastructure, implementation costs are comparatively low. According to Forrester, DV is becoming a critical asset for enterprises looking to overcome big data challenges today. Moreover, in a report, Gartner predicted that the organizations embracing data virtualization would be spending 40% less on integrating data from a multiple set of sources as opposed to those who adopt the traditional data unification techniques. These numbers prove that many big organizations are considering Data virtualization to streamline their data integration process.

1. Unified Data Collection

When dealing with big data, there is the difficulty of a unified, standard way to collect and manage it. This data can be coming from a significant number of sources (both web-based, and from your various applications). That said, as a business person, you understand the need for integration and cross-talk between all this data. What data storage virtualization does, it provides a software-based virtual environment that can span cloud infrastructure, while still behaving like a single device or storage medium. On top of providing more stability and security, this can give something of a frontend that can mitigate incoming and outgoing data.

2. Increased Operational Efficiency

With data virtualization, a real-time collaboration of data is possible. Things like reports, proposals, complex projects, and much more can be mirrored across all devices, with any change made by anyone revealed to everyone in seconds they occur. This eliminates the need to handle local copies of things and then upload them, thereby removing version conflicts.

Along with this kind of collaborative power, big data virtualization allows all devices, all applications, and all users to draw from the same set of protocols with actual parallelism. This means no access queue can otherwise arise as overworked traditional servers try to handle far too big workloads. The result is a massive boost in operational efficiency.

3. Speed

Since DV imports the metadata of tables and creates virtual tables which mirror the source objects, complex ETL to achieve real-time reporting can be avoided. Adding new tables or fields in ETL could take a few weeks to months as star schemas have to be built and designed, but having Data Center or Storage Virtualization in place, the shape of the data can be preserved to be the same as the source and hence adding new fields or tables will be much easier and quicker.

4. Better Business Outcomes

Data virtualization allows users to experiment with new ideas to gain and use data. This leads to business growth. There is also less risk of serious downtime, users can detect, examine, and fix any data problems that might occur quickly and easily. Customized virtual views enable business users to request new data and quickly put it to use.

Pfizer

Pfizer is the world’s largest drug manufacturer. With a complex portfolio of projects that are continually changing, it required a simple way to obtain integrated information that supports portfolio decisions, analysis, and resource allocation decisions. Using Data Virtualization, Pfizer reduced the time needed to derive new information from months to days, which helped in improving data quality, and decreased R&D project dates missed by 60 per cent.

Qualcomm

Comcast

Millions of Comcast consumers manage their services online. Comcast was looking to improve the accuracy and performance of account ownership changes. With Data Virtualization tools in place, Comcast accelerated customer requests for an ownership change from 10 seconds to 1.2 seconds, reduced customer service costs by US$2000 per day, and improved customer satisfaction.

Global 50 Energy Company

One of the world’s largest oil and gas producers needed a way to provide access to information stored and managed in multiple systems and locations for analysis, reporting and decision making. With Data Virtualization solutions, the company reduced overall development costs by 40 percent, reduced risk, increased revenue, and improved efficiency and resource allocation for greater competitiveness.

Fortune 50 Computer Manufacturer

Outsourced manufacturing operations required global visibility into orders and inventory across six regional procurement systems. The company chose a data virtualization approach to integrate global procurement data for analysis and reporting. This solution was in production faster than a data warehouse alternative and reduced infrastructure and development costs by more than US$1M annually. Faster inventory turns, and improved customer satisfaction also provides an ongoing return on investment of millions of dollars per year.

Today’s challenges associated with managing and effectively using massive data stores will continue to grow. Data virtualization is the only approach proven to help businesses achieve better business outcomes, faster. World’s leading companies are harnessing the power of their data to achieve significantly better business impact.

You can start your data virtualization initiative with specific projects that address immediate information needs with Polestar Solutions now.

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