Five Ways That Data Virtualisation Can Enhance Your Approach to Data Driven Insights

Data virtualisation is the modern approach to putting all business data within reach of data analytics tools. Rather than build huge and expensive pools of storage to hold duplicate copies of data for the purposes of analytics, data virtualisation provides a layer of abstraction over siloed data sources allowing analytics tools and queries to “see” all that data as if it had been collated in a single pool.
The technology is clever; it can save time and resource for companies building an infrastructure for analytics, but that’s not the end game. Data virtualisation is ultimately about one thing and one thing only – driving data-driven business benefits. By accelerating time-to-value, data virtualisation technology is an accelerant to achieving these insights.
TIBCO is a leader in the field of data virtualisation and the company’s Senior Director for Data Management, Robert Eve, highlights what he believes are five popular ways in which Data Virtualisation can enhance your company’s approach to analytics. 

  1. Data Federation – Eve suggests that federating or combining data from multiple data sources is always a great starting point for data virtualisation. Very often, a limiting factor for any Business Intelligence (BI), dashboard or visualization tool is the limited data sources which these analytical tools can access. By expanding the analytical tools’ reach to a wider variety of data sources, organizations gain more complete and comprehensive insights.  This is even recognised by numerous analytical tools vendors who have built limited data virtualisation capabilities directly into their own products.  
  1. Data Warehouse Extension – This may seem quite surprising as data virtualisation is often touted as a replacement for data warehouses. However, according to Eve, many situations occur where the two technologies can co-exist, with data virtualisation truly enhancing the capabilities of a data warehouse. For example, adding new data sources into a data warehouse can be complex, time-consuming and even involve restructuring the whole system. Data virtualisation provides an easy way to combine existing data warehouse information with new data sources. Another great use case is using data virtualisation to combine the most current, updated data in live systems with the overnight batch data that resides in the data warehouse, ensuring analytics tools can work with the most up to date data sets possible. 
  1. Enterprise Data Virtualisation Layer – In essence, this means once a core data virtualisation technology has been implemented for more simple use cases such as BI federation, it can then swiftly be used to pull in data sources from all and every business application that an organisation runs. 
  1. Big Data Integration – Often, once Big Data silos like Hadoop are installed and being put to good use, line of business and data science teams realise that integrating this data with existing enterprise data can reap even more benefits. Data virtualisation enables this to be done quickly without the need to duplicate already large data sources. 
  1. Cloud Data Integration – As companies start to create increasing amounts of data in SaaS, IaaS and PaaS, the data becomes as valid as any other source for analytics-driven insights. Integrating those sources into existing data stores is a complex, if not impossible task, and the cost and time required of copying data down from cloud and SaaS providers are often prohibitive. Data virtualisation solves this problem, allowing analysts to get a view of their on-premise and cloud-based data from one source while keeping that data in situ. 

These are just some of the use cases, which at the current time, TIBCO’s Robert Eve suggests are amongst the most popular. More fundamentally, however, what these examples show is that the sources of data we create and use in our business are rapidly changing and adapting. Data is siloed because it is created and used in different places for different purposes. However, from an analytics point of view, we need to have sight of everything. In this dynamic environment, data virtualisation has emerged as the way to keep pace with change.
You can read more about Data Virtualisation here.

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