Data virtualisation has often been heralded as the answer to enterprises caught in a vicious circle in a world riddled with data, both online and offline. However, it is important to remember that no technical solution is a silver bullet and data virtualisation should not be thought of as a one stop solution for all an enterprise's needs.
Businesses want to act and improve their decision-making in real time whilst containing costs and supporting business-as-usual activities, which can leave CIOs struggling to navigate through an array of complex applications and systems. To get the most out of data virtualisation, and when deployed with the right capabilities and methodology to achieve the desired result, businesses can leverage existing investment to solve current and future analytic needs without compromising on quality, budget and time.
Don't get caught in the data maze
It seems like a Catch-22 situation where businesses need data to derive meaningful insight and improve decision-making. However, many large enterprises have evolved over years of operation and accumulated a variety of data resources along the way, which can make it difficult to access and utilise information across numerous business systems.
Businesses are increasingly implementing retention strategies, which means that the industry is witnessing a proliferation of structured and unstructured customer information. As a result, enterprises are feeling compelled to feed the analytical needs of the business with complex, enterprise data warehouses (EDWs) and business intelligence (Bl) solutions.
On the face of it, investing in Bl solutions may seem like the clear 'get-out-of-jail-free' card, however, these systems can create a whirlpool of data management challenges. From master data management, to data integration and data storage, Bl systems lack agility and flexibility.
Moreover, the complexity of the data landscape makes it difficult for BI systems to accommodate additional business needs with ease.
These analytic solutions combine multi-vendor product deployments and disciplines across complex integration patterns. Unsurprisingly, they are deployed at the cost of lengthy timeframes and excessive capital investments. While the solutions address several operational use cases of the business, they struggle to provide quick and actionable insights.
The Swiss Knife of the Data Tool Box
In such a disparate business and IT landscape, data virtualisation comes to the rescue...