At the recent Gartner BI Summit in Las Vegas, there was a lot of discussion about the paradigm shift underway in business intelligence (BI) and analytics. Business’s need for agile data access and self-service, combined with IT’s inability to satisfy this need, is causing disruption to traditional models and shifting the balance of power from IT to the business.
While that is certainly true, it is not a new phenomenon. In fact, it has been building for more than a decade.
The Genesis Of Data Virtualization
More than a decade ago it was clear that the traditional BI solutions were not meeting the needs of larger businesses. Large enterprises and service providers often had more than one warehouse, and many silos of data were excluded from the warehouse. Building reports that required data from multiple warehouses and silos led to very expensive data integration efforts.
In 2002, we created data virtualization to fill the gap between business’s need for more diverse data and faster time to solution, and IT’s need to provide this data in a governed, industrial-strength way. In the years to follow, data virtualization products gained significant traction as a way for IT to quickly provide business with one “virtual” place to go for secure business-oriented views of data.
From weeks and months, IT reduced time-to-delivery for new data sets to hours and days, while maintaining industrial-scale and security. And the business quickly leveraged this data to drive significant top and bottom-line benefits. Numerous successes were well cataloged on our video site as well as in the book Data Virtualization: Going Beyond Traditional Data Integration to Achieve Agility.
From Warehouses To Lakes
What no one saw coming, back in 2002, was the rate at which data volume, velocity and, especially, variety would grow. The rise of e-commerce, mobile, social, and video has created more data than traditional IT can handle. It’s a tremendous challenge, but also a tremendous opportunity.
The market has responded with the concept of the data lake, a single, elastic repository for consolidation and processing of all business data for BI and analytics. But does that fully solve the problem of data silos?
Most large enterprises will have more than one data lake due to their business structure, regulatory issues, and data sovereignty laws. They will also need data from Software as a Service (SaaS) applications, partners, data market places, public data, and of course, from their traditional warehouses, cubes, and marts. For most enterprises it won’t practical, let alone legal, to consolidate all data into a single data lake. It’s not possible at Cisco, and if you work for a Global 2000 company, chances are, it’s not possible at your company either.
So while the data lake solves problems of scale and cost, it does not solve the core problem data virtualization was created to solve: connecting all of the data. Therefore, data virtualization is even more relevant today then it was back in 2002.
The Rise Of Self Service
With so much more data at their fingertips, today’s business users are demanding self-service BI and analytics. No longer willing to wait for IT, they want direct access to data and a set of tools designed around their skillset and needs.
Cisco Data Virtualization offerings have enabled this leap ahead with new capabilities to meet the needs of business users. Data discovery features help business users find relationships between disparate data. Business directories connect business analysts with the data they need, regardless of where it may reside. Modern data virtualization platforms provide a powerful complement to self-service BI and analytic tools to empower the business.
The Future is Bright
The journey, so far has been incredible as our customers and team joined together to deliver data-virtualization based solutions with lasting value. It’s full speed ahead as we continue this evolution.
Do you see a bright future as well?
Share your thoughts and concerns with me as these will help inform my business view and drive our strategy.
Join the Conversation
Join our Data and Analytics Webinar
Learn More from My Colleagues