Enterprises today are dealing with enormous complexity when it comes to their data ecosystem. The large volume of data isn’t a new thing, but the real challenge comes with the variance and veracity of the data landscape that they deal with today. Evolving from just binary and textual data for processing, today’s enterprise decision frameworks need to gauge insights from complex streams of data that involve text, audio, video, and other multimedia content. Additionally, they need the capability to acquire critical data residing across multiple cloud channels and on-premises business systems.
Acquiring data from different sources is just one part of the decision-making process. Ensuring that value is extracted from this data by analytical and business intelligence systems is the other. Organizations need these two parts to work together seamlessly and efficiently to ensure that they grow into a very data-driven and responsive business for all customers. Innovations like high-end multi-source analytics and artificial intelligence capabilities can be achieved only when their respective services can seamlessly access and use data from different sources on demand. Data virtualization has proven to be the missing piece of the puzzle in this scenario.
The Rise of Data Virtualization
Connecting cloud analytical services with the organization’s data spread across channels and systems, can be done efficiently by data virtualization. It is essentially a virtual middle layer that sits between user services and data sources. By acting as a bridge, data virtualization facilitates the easy exchange and management of data insights on the virtual layer rather than having to move data from its original location. Summing up, it centralizes all data within the organization into a single virtual location which in turn unifies the data access and management experience for different applications that consume the data.
How Does Data Virtualization Simplify Multi-Source Analytics?
For organizations that leverage analytics to drive decision-making, there is a major mandate to provide a simplified experience for users performing data operations like analytics. This is where data virtualization proves its mettle. Let us explore four ways in which data virtualization helps in simplifying multi-source data analytics:
- Creates an Abstraction Layer for Analytical Services
Analytics services often crunch data from multiple sources in different degrees to extract insights. Data virtualization creates an abstract layer that masks the source and can be configured to provide the required data from the specific storage on demand. This way, analytics engineers can focus on refining and improving their computing and analytical logic rather than having to spend time on data management. Data management approaches are often complex and hence providing relief through data virtualization is something that users will appreciate a lot.
- Enables Faster Development Cycles
Taking a step from the abstraction layer mentioned above, engineers can focus on building more logical workflows for their analytics infrastructure without the need to spend time on data architecture. This speeds up the development life cycle and businesses can get their new analytics-powered services to the market faster.
- Breaks Down Data Silos
One of the biggest challenges that analytics services face is dealing with siloed data from different sources within the enterprise. By bringing data virtualization into the picture, this challenge can be tackled easily. Data residing in different locations can be seamlessly brought into a unified virtual platform that makes it easier for analytics services to comprehend and discover the deep contextual understanding of value hidden within data. Business users can also play with different data sources by mixing and matching them to create new data views that analytics services can consume readily. Such an approach will be impossible if the analytics services are to directly acquire and gather data from multiple sources as the preparation process of data will be highly cumbersome.
- Optimizes Scalability
Thanks to data virtualization, data from different sources are readily available on the cloud for consumption by analytics services on demand. There is a high degree of elasticity provided by the abstract virtualization layer that helps engineers combine multiple data sources at scale whenever needed for analytical use cases. There is no need to invest in scaling the entire data landscape of the enterprise and data operations can be easily scaled without disrupting the original storage characteristics.
Powering Decision-making on the Cloud
With its ability to build a disruption-free data management framework on the cloud, data virtualization will undoubtedly be the centerpiece of modern cloud transformation initiatives powered by analytics. It can facilitate large-scale collaboration by analytical services from different regions in parallel without disrupting any business workflows or data stores. Every department within the business gets its version of the current data snapshots necessary to run its decision systems and power operations.
While the concept of data virtualization provides several opportunities for businesses, the road to building a sustainable one is not without its share of challenges. From data architecture to the configuration of infrastructure for managing granular data intricacies, the abstract virtualization layer requires a more strategic approach. Enterprises need an expert technology partner to understand their unique data flows, business needs, and objectives to design and build the most tailored data virtualization engine. Contact us to learn how Wissen can be the trusted partner leading this change forward.
