Data Virtualization – Bigger, Broader, and Deeper

Denodo-Paul-Moxon-headshotby Paul Moxon

In the latest Forrester Wave for Enterprise Data Virtualization (Q1, 2015), the authors recognize Informatica, IBM, Denodo, Cisco, SAP, and Oracle as the leading data virtualization vendors. However, the most important message to come out of the report – and that of other research by Forrester – is not who is first, whether Cisco is an outlier, or any other dissection of the relative positions of the vendors. The key message is that data virtualization adoption is growing rapidly as organizations find broader uses for the technology. Forrester actually describes the data virtualization market as ‘hot’ and highlights four trends to support this finding:

  1. Data virtualization is more than data federation

The Forrester Wave identified a number of use cases for data virtualization that go beyond simple data federation, including:

  • A 360-degree view of the business, customer, product, and employee (‘single view’ use cases).
  • Real-time data sharing across lines of business, partners, and the enterprise.
  • Self-service data platform for technology management and business users.
  • Securing critical data.
  • Delivering higher performance and scalability.

Other examples of data virtualization use cases include the hybrid data warehouse or ‘data warehouse offloading’, integrating big data with more traditional corporate data stores to provide context for analytics, and self-service data architectures so that business users can find the data that they need more easily, and so on.

  1. Large system integrators are getting involved

Large system integrators are now getting involved in data virtualization projects and Service Providers are all offering a range of consulting services in support of data virtualization projects. The providers include, Accenture, BearingPoint Consulting, Computer Sciences Corp (CSC), Deloitte, Goldman-Sachs, HCL Technologies, HP Enterprise Services, IBM Global Business Services, Infosys, SAP, Tata Consultancy Services (TCS), Tech Mahindra, and Wipro. .

However, these firms do not get involved in technologies unless there is demand for skills and expertise in large projects with big budgets. This is a clear sign that data virtualization projects have moved beyond single point use cases and are now used for enterprise-wide deployments within large organizations.

  1. The industry/vertical scope is growing

Similarly, data virtualization projects are now spanning many industries as the technology gains broader acceptance. The projects focus on financial services, telecom, and government sectors. However, Forrester reports significant adoption in other verticals, such as insurance, retail, healthcare, manufacturing, and eCommerce.

This should come as no surprise, as financial services and telecom companies are typically the first to exploit new technologies as they look for a slight edge on their competition. Companies in the other industries tend to be more cautious and, even, conservative in their adoption of new technologies and then usually only after a rigorous selection process. The significant increase in data virtualization adoption in these additional industries again demonstrates that the technology has reached a broader audience.

  1. Products are ‘enterprise hardened’

Data virtualization solutions are mature products that support enterprise-wide deployments with very large and complex deployments. Leading vendors offer innovative, proven platforms that support large enterprise data virtualization needs, some of which run from hundreds of terabytes to petabytes. The leading data virtualization products have improved in terms of performance, scalability, and security to meet the most demanding requirements.

In addition to these four points, other Forrester research highlights the increasingly important role that data virtualization plays in the modern hybrid data ecosystem. In fact, Forrester has defined data virtualization as a critical component in the following architectures:

Self-Service Data Platform – The business is demanding faster access to data and without the constraints that are usually imposed by IT. Often, they are asking for access to the raw data so that they can discover relationships and insights that were previously unknown. Self-service data access is becoming a hot topic and data virtualization makes data discovery, navigation, and consumption much easier and quicker. As Forrester notes in the report Create a Road Map for a Real-Time, Agile, Self-Service Data Platform, February 26th, 2015, “Enterprise architects must revise their data architecture to meet the demand for fast data. In-memory data grids, data virtualization, and NoSQL data sources are must-have technologies to effect the transition from data museums to a world of contextual data services.”

Next Generation Data Warehouse – This has also been called the hybrid data warehouse or logical data warehouse and recognizes that the plethora of different data stores within an organization are unlikely to be consolidated into a single data store or data lake. The next generation data warehouse architecture (described in ‘The Forrester Wave™: Enterprise Data Warehouse, Q4 2013’) uses data virtualization as an abstraction layer to make the underlying data stores look and act like a single logical data warehouse. The formats, location, and access technologies of the data stores are hidden from the consumer who sees a single data store.

Big Data Integration – As ‘big data’ deployments – whether they consist of Hadoop, NoSQL, or simply massive volumes of other data – gain momentum, there is always the question of how to integrate these new data sources with traditional data stores, such as a data warehouse or CRM data. The traditional data stores provide context for analytics, such as customer information, that make the results of the analysis meaningful to the business. Alternatively, the insights from the analytics needs to be integrated with the data from the business decision systems so that actions can based on these insights (e.g. offers to move customers from ‘thinking about’ to ‘purchasing’). Forrester notes in the report ‘Market Overview: Big Data Integration’, December 5th, 2014 that, “[Data Virtualization’s] key strengths are delivering unified and centralized data services with security and real-time integration across multiple traditional and big data sources including Hadoop, NoSQL, cloud, and SaaS.”

Application Process Platform – As Forrester describes in Increase Flexibility By Embracing Future Business And Technology Trends’, September 23rd, 2013 applications have been moving towards a service-oriented architecture for ten or more years and architects are beginning to realize that access to data should also be service-enabled. Organizations are using data virtualization to build a data services layer on top of their data sources and provide a consistent and common view of data for all applications – internal, web, mobile, and so on.

These are all telling proof points that data virtualization has expanded beyond simple projects into a truly agile and flexible data services layer that can seamlessly scale from a single project all the way up to the largest enterprise – and, in doing so, delivers on a bigger scale, with broader use cases, and deeper functionality.

Paul Moxon is senior director of product management at Denodo Technologies.


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