What is data virtualization?

Break down data silos and speed queries

Companies often try to break down silos by copying disparate data for analysis into central data stores, such as data marts, data warehouses and data lakes. This is costly and prone to error when most manage an average of 400 unique data sources for business intelligence.¹ With data virtualization, you can access data at the source without moving data, accelerating time to value with faster and more accurate queries.

Data virtualization with IBM Db2 on IBM Cloud Pak for Data

Data virtualization with IBM Db2 on IBM Cloud Pak for Data (02:03)

Why data virtualization

Benefits of data virtualization software

Data virtualization case study

Data virtualization use cases

Data fabric

Build a trusted data foundation with a data fabric

Connect the right data to the right people at the right time with an intelligent data fabric. This architectural approach simplifies access to various data types across hybrid, multicloud environments with data governance, security and compliance. With data virtualization from IBM, you can increase data accuracy with near real-time and self-service access to trusted, quality data at the source.

Universal queries

Access disparate data with a universal query engine

With the IBM Cloud Pak for Data AutoSQL framework, use a single distributed query engine across multiple data sources. AutoSQL combines with data virtualization to query across clouds, databases, data lakes, warehouses and streaming data without copying or data movement. You gain faster access to the data you need most.

IBM data virtualization tool

IBM Cloud Pak for Data

Integrate data sources, types and locations without data movement or replication

Access to current data

Get current analytics without external data storage. Run SQL applications in a single repository.

Unprecedented speed

Automatically self-organize your data nodes into a collaborative network for computational efficiency.

Security and privacy

Encrypt database credentials and keep them private on local devices, not cached on other devices or clouds.

Flexibility

Support popular application query languages and multiple data sources across your enterprise.

Ease of use

Automate optimization and use an interactive console to query, manage and visualize data and users.

How industries use data virtualization

Compliance analysis at financial branch locations

For financial institutions, quickly finding and stopping noncompliant transactions can have a positive impact on their bottom line. With data virtualization, institutions don’t have to move their data to a central data center or to cloud for processing and analysis. Querying microdata centers in financial institution branches enables analytics to happen in real time.

Mobile data thinning

How can a company quickly find which ad is having the most impact, while eliminating the noise happening around it? Data virtualization and edge analytics enable companies to better understand how to thin big data and process and analyze only the information necessary to the query, saving cost and time.

Retail customer behavior analysis

Brick-and-mortar stores are looking for any competitive advantage they can get over web-based retailers. Data virtualization enables near-instant edge analytics, providing unprecedented insights into consumer behavior. This helps retailers better target merchandise, sales and promotions, and do more to provide exceptional customer experiences.

IoT sensor data monitoring and analysis

IoT sensors are creating massive amounts of data. With the growth in the number of sensors collecting data, data volume is set to explode. Moving data analytics to the edge with a data platform that can analyze batch and streaming data speeds up and simplifies analytics, simultaneously — providing insights where and when needed.

Increasing manufacturing efficiencies

Automated manufacturing environments prioritize alarms by augmenting their quality and process techniques with meta-learning or rules. With data virtualization and machine learning methods, manufacturers can increasingly sift through patterns of alarms and convert them into actionable information.

Remote monitoring and analysis for oil and gas operations

Data virtualization and edge computing can achieve reliable operations for the manufacturing industry. Having near real-time analytics performed at the site where data is being generated can help organizations identify issues promptly and, in so doing, prevent unexpected operational outages and interruptions.

Related products

IBM Cloud Pak for Data

A unified data and AI platform that simplifies and automates how you collect, organize, and analyze data and infuse AI across your business

IBM Cloud Pak® for Data as a Service

A starter set of IBM Cloud Pak for Data platform services fully managed on IBM Cloud®