Big data integration is challenging

Today’s businesses are looking for the best way to handle big data. Each day, 2.5 quintillion bytes of data are created. By 2020, experts are predicting a tenfold explosion to 44 zettabytes (or 44 trillion gigabytes).

Big data equals big business, but many companies aren't unlocking the value they would like from their data. Most struggle to manage an average of 33 unique data sources, which are diverse in structure and type and are often trapped in data silos that are hard to find and access.

Blue icon - data virtualization helps reduce costs

What is Data Virtualization?

For decades, companies have tried to break down silos by copying data from different operational systems into central data stores for analysis such as data marts, data warehouses and data lakes. This can be costly and error prone.

With data virtualization, you can query data across many systems without having to copy and replicate data, which reduces costs. It also simplifies analytics and makes it more up-to-date and accurate because you’re querying the latest data at its source. 

Why IBM Cloud Private for Data?

Always-current analytics at data sources with data virtualization

Access current data

Always-current analytics across distributed data sources. No need to store data outside your data center. Experience a single data repository where your SQL applications can connect and run.

Data virtualization means faster speeds and less replication

Unprecedented speed

Leverage networked devices for polynomial processing gains. Automatically self-organize your data nodes into a collaborative network for computational efficiency. Define constellations with large or small data sources.

Data virtualization provides a layer of security and privacy

Security and Privacy

Data isn’t cached in the cloud or on other devices. Credentials for your private databases are stored encrypted at the local device and are private to that device.

Data virtualization makes data sources look an integrated database

Flexibility

Supports multiple application query languages (SQL, stored procedure languages, R and Python) and data sources like Hadoop, Db2, Db2 Event Store, Informix, Oracle, PostgreSQL, SQLServer and Teradata. 

Machine learning and data virtualization simplify data management

Ease of use

A single web console with an interactive interface to query data, manage users and visualize data node constellations. System optimization is automated through machine learning and adaptive algorithms.

Industry Uses of Data Virtualization

Compliance analysis at financial branch locations

For financial institutions, quickly finding and stopping non-compliant transactions can have a positive impact on their bottom line. With data virtualization, they 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 enable 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 only process and analyze 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, 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 number of sensors collecting data growing, data volume is set to explode. Moving data analytics to the edge with a data platform that can analyze batch and streaming data simultaneously speeds up and simplifies analytics - 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.