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How organizations are leveraging data virtualization for digital transformation

data virtualization enables an agile, real-time approach without having to physically replicate the data in the interim

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DQINDIA Online
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Digital transformation is in full swing across a number of industries, but companies will never retire from the age-old “cost cutting” to maintain profitability. After all, “Business 101” dictates that profits are the only reason companies exist, and opportunity cost dictates that without it, capital could be better used somewhere else. Especially in troubled times like the current Coronavirus pandemic, which is causing an unprecedented reduction in budgets and staff, CIOs are better advised to invest in technologies that enable the continuation of digital transformation projects while lowering costs. These technologies could very well be the ticket to CIOs continuation in their current role.

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Applying a Modern Integration Approach

Ensuring accurate and consistent data delivery remains the illusive nirvana for companies today. Scores of data integration approaches and technologies continue to convolute the market and create confusion among many organizations. However, some technologies are reaching maturity and are causing organizations to look for a modern, viable option for data integration. One such technology is data virtualization, which enables an agile, real-time approach without having to physically replicate the data in the interim.

Traditionally, legacy data integration technologies such as the ETL, extracted the data from source systems, moved them into an intermediary system to transform the format of that data to be compatible with that of the target system, and then loaded the transformed data into the destination system. Even though it is still used widely, ETL, the workhorse of data warehousing architecture, is waning in use. This is due to its rigid programmatic/scripting approach, the need for a bevy of engineers to create and tweak the scripts whenever the data and sources change, and the high cost to store the intermediate data to apply transformations. Any changes to the source systems could break the brittle ETL scripts, requiring time to fix them, and interrupting business operations.

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Data virtualization leaves the data in its sources and creates an abstraction layer on top of them; thereby, disintermediating the sources from the data consumers. Such abstraction shields analyst and business users from any changes to the underlying systems and enables IT to independently modernize the underlying systems without any interruptions to business operations. Analytical and reporting tools  request the data from the data virtualization layer, which tracks the movement of data as IT sunsets older systems and migrates to newer systems, takes care of finding the data wherever it is—the data that was on on-premises a week ago might be in the cloud today—and delivers that data in real-time back to the users. And it does so securely and rapidly, requiring 1/4th of the resources and cost. Data virtualization’s no-code/low-code approach requires fewer developers, and saves on storage costs since it does not store the data.

Use cases: Who uses DV?

Data virtualization is used in projects such as cloud modernization, data science, data discovery, 360-degree views of customers, products, and assets, data warehouse offloading, and integrating data-in-motion like IoT data with the data-at-rest. As a horizontal technology, companies in almost every industry are successfully using data virtualization for modern use cases such as:

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  • Cloud modernization – As companies are moving away from tying up expensive capital in data centers to the cloud, they are not just lifting the on-premises applications and shifting them to the cloud. Instead, they are rearchitecting them to take advantage of specific cloud capabilities.  As the firms retire the on-premises applications and onboard their cloud equivalents, they use data virtualization as an abstraction layer so that business can continue their operations without having to worry about the data being in the old or new applications.
  • Data science – A pharma customer once said, “We have the cure for the cancer, but it’s lost somewhere in the data!” As organizations mine their data for additional intelligence, data scientists need access to all of the enterprise data with the flexibility to apply different data models to ensure the correct questions are asked and answered. With its logical data model capability, data virtualization enables data scientists to apply various data models in a safe, sandbox environment with all of the available data, before deploying them for business use.
  • Data discovery – As the enterprise data layer that knows all of the information inside and outside of an organization, data virtualization also catalogs it. It provides details such as where data is located, its format, associations, as well as who is accessing it, how often, and for what purpose. Instead of having to search for data in different repositories, business users can now go to the single data virtualization layer and query for the data to see in which systems they reside, their relationships with other data, and business definitions associated with them.

Data Virtualization Can be the Foundation for Digital Transformation

Data virtualization is to digital transformation as mechanization is to industrial revolution. They both aim to improve labor productivity and output, reduce costs, and increase revenue and profit. As companies modernize their IT infrastructure with technologies that will help lower their ongoing costs, assuring the availability and integrity of data and enabling business operations to continue uninterrupted has never been more important. Data virtualization is the CIOs panacea to accomplish both goals—enable digital transformation while lower costs.

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The article has been written by Ravi Shankar, Senior vice president and chief marketing officer, Denodo

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