Data virtualization offers a more efficient and secured way for organizations to provide data access at the source, without the need to move the data. It connects the right data to the right users, and at the right time, facilitating faster access to accurate queries and better insights.
Businesses today recognize the value of data and leveraging it to identify new market opportunities, enhance customer experiences, as well as improve operational efficiencies and employee productivity. As it is, they realize that an estimated 60% to 73% of their corporate data goes unused for analytics.
With more consumers across the globe coming online, there will be more data from which to glean insights and improve services. With digital transformation accelerating, both structured and unstructured data will continue to grow, residing in different platforms, including social media, sensors, and Internet of Things (IoT) devices.
By 2025, the volume of data across data centers, edge systems, and endpoint devices, is expected to hit 175 zettabytes, up from 33 zettabytes in 2018, according to a study conducted by IDC and commissioned Seagate.
More than 6 billion consumers also will interact with data on a daily basis by 2025, when the average connected individual will have at least one data interaction every 18 seconds, the study reveals.
Every byte of data represents an opportunity for businesses to gain important insights. However, many are finding it increasingly difficult to do so when the data resides across multiple platforms and in different environments, including public clouds and on-premises.
Data movement and preparation consume the bulk of a knowledge worker's time, taking up resources that could have been spent on higher value tasks. Challenged by disparate data, enterprises also often look to plug silos by copying data into central data pools, such as data warehouses and data lakes, where they can be used for analysis.
This is not only costly, but also susceptible to error when companies find themselves having to manage hundreds of data sources. In addition, it is inefficient to duplicate large amounts of data just to run an artificial intelligence (AI) or machine learning model. Moving data from a cloud to do so also can result in significant expenses, as cloud providers impose a fee for any movement of data.
Go where the data resides
Such costs and inefficiencies can be easily avoided with data virtualization. Any organization can do this easily with IBM's Cloud Pak for Data.
The AI-based data architecture platform enables you to connect multiple data sources across locations and generate one logical data view. This makes it easier to extract actionable insights from your data.
The virtual data view further facilitates real-time analytics without the need to move or duplicate the data. It does not require additional storage, hence, significantly cutting the time needed to process the data.
With centralized authentication and automated governance, Cloud Pak for Data also ensures employees access data sources in a trusted environment. It provides granular access management to the virtualized data assets, so Cloud Pak for Data users can use only specific data virtualization functions assigned to them based on their job description.
In addition, all communications between the environment and application are encrypted with IBM technology as well as SSL/TLS encryption.
Cloud Pak for Data delivers central data governance and security using Watson Knowledge Catalog, which also eases data discovery. The data catalog tool powers intelligent, self-service discovery of data and readies data for AI and machine learning.
Data virtualization offers significant cost savings by removing the complexities of integrating different data types and structures, automatically connecting multiple data sources as a single virtual data fabric.
In fact, Cloud Pak for Data can reduce the number of extract, transform, load (ETL) requests by 25% to 65%, according to a Forrester study commissioned by IBM.
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Download NowCloud Pak for Data speeds up AI deployment
Cloud Pak for Data helped Highmark Health cut its AI development and deployment lifecycle from a year to just six weeks.
The Pittsburgh-based healthcare delivery network's researchers and data scientists knew they could build a model from inpatient clinical data to predict and prevent sepsis mortality. The ability to identify patients at greater risk from the disease can help medical workers prioritize care and better manage high-risk and costly inpatient admissions.
Highmark Health worked with IBM to develop a new platform based on Cloud Pak for Data, with components for data modernization, DataOps, and AI lifecycle automation. These included IBM Watson Knowledge Catalog and IBM Watson Studio.
The goal was to enable Highmark Health to predict acute events months in advance using claims data from millions of members across multiple siloed data sources.
In a six-week proof of concept, the Highmark Health team and IBM built a model, then scored and identified patients likely to develop sepsis. The platform further enabled Highmark Health the ability to tap new research findings as COVID-19 evolved.
With IBM Cloud Pak for Data, the healthcare services provider was not only able to eliminate data silos, but also reduce data preparation by cataloguing all data attributes in one place. It also integrated insights into the application workflow and enabled monitoring of insights for bias, trust, and transparency.
Data virtualization helps break down data silos, removing the need for duplication and data movement so you can access data at the source. With IBM Data Virtualization, you can simplify and democratize your data landscape to get trusted, governed data into the hands of your data scientists, developers, and engineers more quickly than traditional ETL processes
Apart from healthcare, data virtualization also can benefit other verticals that need retail customer behavior analysis, IoT sensor data monitoring and analysis, better manufacturing efficiencies, and remote monitoring and analysis for oil and gas equipment.
IBM Cloud Pak for Data provides a cost-efficient and consumable data fabric, integrated with best-in-class insights platform to enable smarter and data-driven decisions more quickly. It allows businesses to accurately and effectively tap growing data volume, velocity, and variety.
Data virtualization on the IBM platform also assures higher data accuracy and governance enforcement. In addition, customers consume less resources due to lower ETL processed and have less disk requirements.
Running on Red Hat OpenShift, Cloud Pak for Data simplifies and automates how data is collected, organized, and analyzed data, with a unified data and AI platform.
