Data Virtualisation a Catalyst for Agile Business Decision Making

One of the problems with the term “Data Virtualisation” is that it sounds like a complex technology and the concepts behind what it does technically can be a bit difficult to grasp.
As is the case with many “software-defined” technologies, they do indeed mask incredibly clever, detailed and complex technical wizardry under their covers. More importantly, they simplify this complexity for users. 
That is exactly the case with Data Virtualisation. There is some very clever stuff going on beneath the surface, but for users and consumers of data, all it does it make life easy and very efficiently put more relevant data in the hands of the people who need it.
In truth, the most important thing that anyone and everyone needs to understand about Data Virtualisation is that is a technology that facilitates rapid access to data in order to quickly and efficiently obtain deep insights to aid agile decision making.
Business agility is a critical component of readying your company for the app and data-driven age. Analytics, or rather, analytics done well is a differentiator in achieving that agility. According to a study carried in “Competing On Analytics: The New Science of Winning”, there is a strong link between the use of analytics and business performance. High performers were 50 percent more likely to use analytics strategically and five times more likely to embrace analytics than low performers.
Simply put, analytics matter a lot in business.
So where does Data Virtualisation come in? How does it aid agile business decision making? Like all technology, analytics of today is not the same as a decade ago. It requires access to massive amounts of data often analysed in real time. As an example, the idea of an overnight batch job to load yesterday’s data into a data warehouse often no longer cuts it. By creating a virtual pool of all your data and allowing analytics tools to access that data “in situ”, insight can be updated based on data current to the second.
TIBCO is a pioneer in data virtualisation technology and in a book written by TIBCO’s Senior Director for Data Management, he highlights three key traditional issues which impede a company’s ability to take a modern approach to analytics. These are:
Data Complexity – Analytics-driven decision making needs to access data in different formats from multiple sources. In most cases, the number of sources is not static. It is growing quickly. Traditional approaches to collating this data are complex and time consuming. It isn’t unusual for organisations to have specific implementation projects just to integrate a single new data source.
Query Performance – Not only is it vital to be able to quickly analyse large volumes of data, but it is also becoming critical to have access to the most up to the minute data possible. This is something that has been very challenging in traditional analytic infrastructures.

IT Responsiveness – The need to bring on new data sources quickly and with minimal planning is being driven by the business. In the past, the business used to accept IT being the blocker or reason for long timelines to integrate new sources of data. That no longer holds true. IT is now expected to respond at pace.
It should come as no surprise that Data Virtualisation solves these issues and more. For businesses that need better insight for decision making, data on which to build new business models, and even businesses that are built on data itself, they need to execute agile business decisions. Data Virtualisation is the key to making this happen.
You can read more about Data Virtualisation here.

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