One of the biggest challenges facing IT leaders of global organizations is how to extend the use of business intelligence across the enterprise to a user community that demands self-service. Technology research firm Gartner writes that, without appropriate governance, self-service capabilities can “increase errors in reporting and leave companies exposed to inconsistent information1.” CIOs and heads of BI and analytics departments are under increased pressure to satisfy the business need for greater agility without compromising consistency and trust in the data.

virtualization_BITraditionally, supporting multiple territories and departments using legacy, on-premises BI solutions has involved time consuming and expensive efforts that rely on physical replication of BI infrastructure – not just hardware but also data, business models, user profiles, system configurations, etc. Locally dedicated environments are required for development, testing and production, often with backup instances for mission critical applications. Administrators are tasked with managing constant data loads and metadata updates to maintain synchronization across the different environments. The result is restricted access to data, long wait times for the business and, ultimately, a barrier to end user self-service.

Today, the emergence and large-scale adoption of technologies like cloud computing enable innovative alternatives to traditional BI that introduce exciting opportunities. One of these alternatives is the ability to virtualize the BI ecosystem. In the same way that virtualization reshaped our approach to computer servers and data storage, modern multi-tenant cloud architectures have the potential to completely transform the way we deliver and consume analytics, from application development lifecycles to end-user generated content.

The possibilities are fascinating. For example, the use of virtual – not physical – BI instances, allows organizations to extend analytical capabilities across multiple territories, departments and customers at a dramatically accelerated pace. Companies can unify global and local data without any physical replication. They can deliver federated data access across the globe with local and aggregate views. Software vendors can scale out multi-tenant BI environments to onboard new customers faster than before. What used to take quarters will now happen in a few weeks, maybe days. This means significantly reduced costs and risk.

But the virtualization of BI is not just about speed. By virtualizing analytical business models (i.e. metadata) – not just hardware or data – companies can build a network of interwoven BI instances that share a common analytical fabric. Decentralized teams and individual users, empowered with self-service data preparation capabilities, will be able to augment the global analytical fabric with their own local data and business definitions. Think of it as an organically grown – “crowdsourced”, if you will – network of common business semantics.

A Sales Operations Manager, for instance, can analyze opportunities by salesperson across different regions. Through virtualization and smart discovery, an automatically generated business model can be shared with the Marketing team. A Campaign Manager may then augment this model with her own data and expand the analysis to include market segments, without impacting the work of the Sales Ops manager. This networked model can be shared with the entire company for trusted collaboration. End users have autonomy to work with data on their own, while governance is maintained transparently.

The virtualization of BI will redefine our approach to enterprise analytics. It will enable IT leaders to extend the adoption of BI across the enterprise with confidence. By building networks of virtual instances, businesses can deliver governance that moves at business speed, eliminating data silos once and for all and giving people freedom to work with data on their own terms.

1. Gartner, “Embrace Self-Service Data Preparation Tools for Agility, but Govern to Avoid Data Chaos”, March 2015