Data seems to be the oil of future industries as most organizations are beginning to use data to analyze their business and integrate their goods and services through structured data sources. Denodo is a company that specializes in handling data with data virtualization, data integration, and real-time data services. Analytics Insight has engaged in an exclusive interview with Ravi Shankar, the Senior Vice President and Chief Marketing Officer of Denodo.

Kindly brief us about the company, its specialization, and the services that your company offers.

Denodo is the leader in data virtualization providing agile, high-performance data integration, data abstraction, and real-time data services across the broadest range of enterprise, cloud, big data, and unstructured data sources at half the cost of traditional approaches. Denodo's customers across every major industry have gained significant business agility and ROI by enabling faster and easier access to unified business information for agile BI, big data analytics, web, cloud integration, single-view applications, and enterprise data services.

With what mission and objectives, the company was set up? In short, tell us about your journey since the inception of the company?

Denodo transforms the way organizations innovate and operate their businesses by unifying their data assets in real-time and making data ubiquitous and secure to all users and business applications. Denodo imagines a world in which organizations can focus on their business goals without having to stop and figure out how to access, integrate, govern, and provision data. We imagine a world in which business users can immediately and easily access the data they need without worrying about where it is housed, how it is formatted, or how quickly it changes. Every day, we work to make this vision a reality, with a commitment to constant innovation in the fields of data integration and data management.

Brief us about yourself and your contributions to the company.

What began in 1999 as a research project at the University of ACoruña was brought to life in the commercial sector by me. I was then a full-time professor at the university. I worked tirelessly with some of my students and research fellows, many of them still with the company, to solve a problem that I believed would grow ever more pressing as technology advanced: How can we better integrate, and make sense out of, the ever-increasing data volumes? Ultimately, how can we set data free? Denodo Technologies was thus born.

Denodo launched the first version of its product – the Denodo Platform – in 2002. In 2006, I moved Denodo's headquarters to Silicon Valley, USA. In 2015, Denodo made its debut in the 2015 Gartner Magic Quadrant for data integration tools. The same year, the Denodo Platform was recognized as the winner of the 2015 Ventana Research Technology Innovation Award for Information Management. In 2016, Denodo was the first company to introduce data virtualization in the cloud and dynamic query optimization in its version 6.0 of the Denodo Platform. This innovation earned Denodo as a Visionary in the 2016 Gartner's Magic Quadrant for data integration. In 2017, HGGC, a private equity firm, made an investment in Denodo to further accelerate its growth. In 2018, Denodo released the industry's first fully-integrated data catalog and introduced massive parallel processing (MPP) capabilities in its version 7.0 of the Denodo Platform. This innovation earned

Denodo as a Challenger in the 2018 Gartner's Magic Quadrant for data integration and as a Leader in Forrester's big data fabric wave. In 2019, Denodo was recognized in the 2019 Gartner Peer Insights report with a 100% willingness to recommend it by its customers. In 2020, Denodo launched its COVID data portal to help its customers gain real-time insights for faster time-to-response. Also, it announced the Denodo platform 8.0 to accelerate hybrid/multi-cloud integration, automate data management with AI/ ML and boost performance. Based on its vision and execution, Denodo was recognized as a Leader in both the 2020 Gartner's Magic Quadrant for data integration tools and Forrester's enterprise data fabric wave.

Tell us how your company is contributing to the IoT/AI/big data analytics/robotics/self-driving vehicles/cloud computing industry of the nation and how the company is benefiting the clients.

Today's organizations claim to be data-driven, but they spend too much time in manual efforts to prepare the data for business use. Automation through AI/ML is key to delivering value quickly. Data virtualization uses AI/ML to understand user behavior and provides recommendations to automate mundane tasks, thus accelerating the delivery and use of data through self-service by business users at the point of their business transactions.

The volume, variety, and velocity of data are pushing the envelope in big data analytics. It's no longer practically possible to collect all of the enterprise data into a physical repository such as a data lake. Rather, data virtualization enables a logical integration of data by connecting to the data wherever it resides, combining them in real-time, and delivering the data at the point of consumption to the business users.

The pandemic is driving CIOs to invest more in the cloud moving their capex to opex. Many of them are choosing different cloud service providers (CSP) for different workloads based on business requirements and geographic necessities. To help the migration of data to the various clouds, and unify them across the different CSPs without incurring egress charges, data virtualization provides a cost-effective approach to not only accelerate the transition to the cloud, but also unify the data across the different clouds to provide a unified of the relevant information for business users and executives.

Kindly share your point of view on the current scenario of big data analytics and its future.

Data virtualization will use AI/ML to automatically infer data changes at the sources, where the data is continuously created through business transactions. It will then help integrate the data irrespective of its location – in the cloud or on-premises – its format – structured or unstructured –, and its latency – data at rest or data in motion. All of this enterprise data will be combined into related business information and delivered to business users and executives within their day-to-day applications, enabling inferring of insights into the business operations in real-time and powering effective operations to stay ahead of the competition.