Part 4: How Data Virtualization-as-a-Service Addresses Key Forces

Against these forces, traditional data integration approaches, designed for simpler, less competitive times, don’t stand a chance.

Ask yourself, what do you need to change? And what would the impact be if you made the change?

Said another way, what three data integration capabilities would your organization love to have, if only you could provide them?

Agile Data Integration

In today’s ever-evolving business and data landscape, victory goes to the swift. You need agility in your data integration methods.

Agile data development and deployment methods let you quickly configure new datasets in hours or days, avoiding the complex software development lifecycle (SDLC) and long times-to-solution associated with ETL and data warehousing.

Metadata-driven, data virtualization does not require you to move and consolidate data physically to integrate it. You can quickly change models within a semantic middle layer, and voila, you have delivered your business user the exact data set they need. And on the agility point, look for a data virtualization solution that is easy to use. Your data engineers won’t care. They are used to the hard stuff. But with a clean, simple UI, your citizen data specialists will love you.

The Research Informatics team at Pfizer uses data virtualization to integrate data faster.14 Their data virtualization implementation allows them to pull new datasets together in a few hours, much faster than the weeks and months required. With this agility, new product development delivers products to market more quickly, a capability of global significance when developing the Pfizer – BioNTech COVID vaccine.

Adaptive Data Architecture

Because business needs, technologies, and data architecture design patterns (e.g., Data Fabric, Data Mesh, and whatever will be in vogue next year) continue to change, you need to adapt your data architecture to keep pace. With a more agile, adaptive data architecture, you can deliver business value faster and take advantage of technology advancements.

Data virtualization lets you create a more adaptive data architecture. It does this by decoupling how you manage data from how you consume it. As a result, you can manage each data type optimally—within the original source, in an on-premises data warehouse, in a cloud data lake, on an edge device—wherever it makes the most sense for your users and use cases.

Data Virtualization-enabled Data Lakehouse, Data Fabric, and Data Mesh are typical design patterns that embrace today’s distributed data topology rather than trying to force-fit all your data using traditional data centralization paradigms.

Business-friendly Data Views and Governed Self-Service Access

Everyone wants data-enabled employees empowered to drive data-driven business success. This requires three capabilities.

• Business-friendly data views help you deliver data in a business-relevant way instead of how it is stored. Based on easy-to-learn, consistent business definitions, these views keep everyone on the same page.
• Self-service data access democratizes data for your business users so they can focus on how to apply data to business opportunities.
• Governance and security ensure the right people get the right data, no more, no less, and do so in compliance with all data-related regulations.

Data virtualization supports all three requirements in a single package.

Data virtualization’s built-in semantic layer automatically populates a governed catalog of business-friendly data views. When your business users need data sets, they can find them. And if the perfect data set doesn’t exist, the latest point-and-click interfaces allow citizen users to build what they need, often without IT assistance.

As a result, your users can focus on delivering business value without worrying about IT internals; a win-win for IT and the business.

Data Virtualization-as-a-Service

Given all the research that shows how data’s gravitational center has shifted to the cloud, doesn’t it make sense to move your data integration gravitational center to the cloud?

Such a cloud-native data virtualization platform must support your

• Wide range of use cases and integration patterns
• Diverse business and technical users
• Fast-growing cloud-resident data as well as traditional on-premises sources
• Most demanding reliability, availability, and scalability service-level agreements.

Moving to the cloud helps your IT team do more. It frees cycles IT would normally spend optimizing your instances, scaling up for greater loads, resolving issues, and myriad other activities. Unleashed from these operational burdens, your IT team can spend more time improving agility, architecture, access, security, and more.

But buyers, beware! Be sure you are getting a complete “as-a-service” solution that not only resides in the cloud, but also frees you from having to set it up, run it, manage it, and upgrade it. Why bother with all that when your Data Virtualization-as-a-service provider can do that for you?

Conclusion

Now available as a cloud-native service, data virtualization has evolved to meet the challenges of 2023 and beyond. Don’t miss this opportunity.