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Data Virtualization: 8 Industry-specific Use Cases in 2024

Data Virtualization: 8 Industry-specific Use Cases in 2024Data Virtualization: 8 Industry-specific Use Cases in 2024

Figure 1. Data virtualization vs. ETL vs. API integration.1

Data virtualization is a modern approach to data integration that allows organizations to access data across disparate systems like data silos without the need for physical consolidation. Data virtualization is a way to create a single virtual view of data from different sources, such as databases, applications, and cloud services, without the need to move, store, or copy the data. Despite this, as compared to traditional ETL or API integration approaches, data virtualization remains less popular (Figure 1). Nonetheless, businesses can achieve faster time-to-insight, lower costs, and improve their ability to make data-driven decisions by utilizing data virtualization. Data virtualization can be particularly useful in:

  • Real-time data integration
  • Complex and agile data integration
  • Compliance and security

In this article, we explain eight data virtualization use cases in seven different industries to guide business leaders about its potential applications.

1. Real-time analytics with data services

Real-time analytics is a key business requirement in today’s fast-paced world. Businesses need to be able to access, analyze, and act on data in real-time to stay ahead of the competition. Since 2010, it has observed a peak in Google search results, and the attraction to it has remained almost steady afterward with a small decrease. 

Data virtualization can make real-time analytics possible by providing a unified view of data from multiple sources. Data services are a core component of data virtualization, allowing business users to access data from multiple sources in real time and deliver it to end users for analysis. By using data services, business users can improve their data management, gain faster insights into their data, and make real-time decisions based on the latest information available.

How hedge funds use data virtualization for real-time market analysis

The finance industry relies heavily on real-time analytics to make timely decisions. Data virtualization software can be used to consolidate and analyze data from various sources in real time. For example, a hedge fund could use data virtualization to get a real-time view of market data, stock prices, and social media feeds to make informed investment decisions.

2. Customer 360 view with enterprise data

Customer 360 View has been regularly searched in India and the U.S. in the last 5 years. A customer 360-degree view is a complete, holistic view of a customer’s interaction with a company across all touchpoints. This includes their purchase history, support tickets, social media interactions, and more. Data virtualization can be used for integrating disparate data from these sources.

Enterprise data, which is the data that an organization owns and manages, is the key to creating a 360-degree customer view. Data virtualization can be used to create a customer 360 view by integrating data from multiple systems, such as customer relationship management (CRM), enterprise resource management (ERP), marketing automation, and social media.

How retailers improve customer satisfaction with data virtualization

Retailers can use data virtualization to create a 360-degree customer view by consolidating data from various systems such as point-of-sale, e-commerce, and loyalty programs. With a complete view of customer interactions, retailers can deliver personalized experiences, tailor marketing campaigns, and improve customer satisfaction.

3. Data Virtualization for regulatory compliance

Regulatory compliance is a critical concern for many organizations. Compliance requirements often involve gathering data from multiple systems such as CRM, HR, and accounting, which can be a complex and time-consuming process. In the last 5 years, regulatory compliance has been searched regularly almost all over the U.S. Data virtualization can be used to ensure regulatory compliance by providing a single view of the data that’s needed for compliance reporting.

How healthcare providers achieve HIPAA compliance with data virtualization

The healthcare industry is subject to strict regulations such as the Health Insurance Portability and Accountability Act (HIPAA), which require the protection and proper management of patient data. Data virtualization can be used to consolidate data from electronic medical records, insurance claims, and other sources into a single view, ensuring regulatory compliance.

4. Data virtualization for mergers and acquisitions

Mergers and acquisitions (M&A) can involve integrating data from two or more organizations, which can be a significant challenge because the organizations involved may have different systems, data architecture, and data formats. Additionally, there may be issues with data quality, data governance, and data privacy that must be addressed before the data can be integrated.

Data virtualization can be used to integrate data from multiple companies and systems without physically consolidating the actual data. Data virtualization can enable businesses to access and integrate data from multiple data repositories without physically moving, copying, or storing the reference data. The data virtualization approach can be particularly valuable in mergers and acquisitions because it can: 

  • Streamline the integration process 
  • Reduce the risk of data duplication
  • Improve the time-to-integration

How tech companies streamline M&A integration with data virtualization

In the technology industry, mergers and acquisitions often involve the integration of data from multiple systems, such as customer data, financial data, and product data. Data  virtualization platforms can be used to integrate this data without the need for physical consolidation, reducing the risk of data duplication and improving the time to integration. This can improve data management.

5. Data virtualization for supply chain management

Supply chain management involves managing and optimizing all the activities involved in making the goods and delivering them to the ultimate consumer. This requires access to data from multiple suppliers and other business partners, which is easier said than done. Data virtualization can be used to integrate data from multiple systems to provide a wider view of supply chain data. This can improve data access and improve efficiency in data engineering.

Some states in the United States, particularly Texas and New York, have been searching for both supply chain management and data virtualization, according to Google search results.In these states, data virtualization has been potentially implemented in supply chain management already.

How logistics companies benefit from data virtualization

To get a complete view of their supply chain, logistics companies can implement data virtualization to integrate data from: 

A central point of data makes everything more efficient for logistics service providers.

6. Logical data warehousing with data virtualization

According to Google search results, the logical data warehouse has been searched in India and the U.S. in the last 5 years. A logical data warehouse is a new approach to data warehousing that uses data virtualization to provide a unified view of data from multiple sources. Unlike a traditional data warehouse, a logical data warehouse relies on data virtualization to provide a unified view of data from multiple sources without physically moving or copying the data.

Data virtualization can be used to create logical data warehouses by integrating data from various data stores and presenting it as a single view. As we mentioned above, data virtualization can integrate data from multiple data sources, such as databases, applications, and cloud services, without physically moving or consolidating the data. By creating a logical view of the data, data virtualization can enable businesses to access and analyze data from various sources as if it were in a single, centralized data warehouse.

How firms offering financial services improve business insights with logical data warehousing

In the financial services industry, a logical data warehouse can be used to consolidate data from various business units such as wealth management, trading, and operations. By providing a unified view of data, financial services firms can gain new insights, identify trends, and make better business decisions.

7. Data virtualization for data lakes

The interest in data lakes has significantly increased in the 2020s. A data lake is a large repository of data that allows businesses to store and process large volumes of data in its native format. 

Data virtualization can be used to access and integrate data from data lakes, making it easier to analyze and gain insights from large volumes of data. Data virtualization can allow businesses to access data from data lakes without having to physically move or copy the data. By integrating data from various data sources into a single view, data virtualization can make it easier to analyze and derive insights from large volumes of data stored in data lakes. This can especially ease data engineers’ data management tasks and help them save time.

How government agencies can leverage data virtualization for citizen data analytics

In the government sector, data virtualization can be used to access and integrate data from data lakes containing various sources such as citizen records, government transactions, and social media feeds. By creating a logical view of this data, government agencies can gain insights into citizen behavior, improve operational efficiency, and make data-driven decisions to enhance citizen services. Additionally, governments can prefer to use data fabrics, which can offer data virtualization and improve compliance reporting.

8. Data virtualization for business intelligence

Business intelligence (BI) has been searched worldwide, particularly by African countries, like Zimbabwe and Tunisia, in the last 5 years (Figure 8). BI is the practice of analyzing data to gain insights and inform business decisions. Data virtualization can be used to create a unified view of data for BI by integrating data from various sources, such as databases, applications, and cloud services.

How marketing agencies gain competitive advantage with data virtualization for BI

Marketing agencies can use data virtualization to create a unified view of data for BI by consolidating data from various marketing channels such as:

By analyzing this data, marketing agencies can gain a competitive advantage, optimize marketing campaigns, and improve customer engagement.

For more on data virtualization use cases, please contact us:

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Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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