From data aware to insight driven: How organisations can use data to get ahead post-pandemic

The case for data virtualisation

Over the last two years, businesses have had to be more resilient and more creative than ever before in order to survive. And, as we emerge into our new hybrid landscape, the challenge is far from over. Around the world, businesses must work out how to digitally connect to both employees and customers. At the same time, they need to be able to forecast what is happening in the market, and process the information almost instantly to make quick decisions.

Data could be the key to achieving all of this. But data is not worth having unless it is being analysed correctly and understood. Despite many organisations being ‘data aware', few are truly ‘insight-driven.' In many cases, traditional, outdated data management tools are getting in the way.

For businesses looking to get ahead in our new post-pandemic landscape, data and the way it is used has never been more important. Those who fail to realise this, and get a handle on data management now, risk being left behind.

The data hurdle

Every business has data and every business needs it. Yet, there are few organisations that are able to maximise on its scope and use it to its full potential.

This is because, more often than not, important information is dispersed across multiple data sources within an organisation. Governments, public sector organisations and private businesses alike are all guilty of storing data across a network of on-premise, multi-cloud, and third-party environments. Whilst storing data in these environments is not a problem in itself, without the appropriate tools in place, being able to actually access and utilise it can become a challenge.

In the past, the most solution has been to consolidate and curate the data that might most likely be needed to make access for business users easier. Moving and copying the data. The most common method for doing this has been to Extract, Transform and Load (ETL) into a new location or data warehouse. Through this, data files are extracted from an existing source, transformed into a common format, and loaded into a new data store - such as a database server, data store or data warehouse. Once complete, information can be made available to prescribed users, under pre-set access and security protocols. However, ETL has been a standard method of mass data integration since the 1970s. It's no surprise, therefore, that certain limitations are becoming increasingly apparent.

The reality is that ETL processes and legacy data storage techniques make detailed data analytics almost impossible. They lack any form of centralised access, which prevents businesses from utilising all of their desired data. To make matters worse, they create significant bottlenecks for engineers due to the time and effort required to produce data sets, run queries, and perform other requests from business users.

Whilst many businesses today are aware that their data holds significant value, outdated processes and tools are getting in the way of unlocking it. In order to inform decision-making and make a meaningful difference in our hybrid world, data needs to be delivered quickly and effectively.

Becoming insights driven

This is where data virtualisation comes in. By automatically integrating disparate data sources, optimising query requests and building a centralised governance architecture, data virtualisation enables businesses to access the data they need faster, boosting both the top and bottom line. In fact, this type of technology could decrease data delivery time by 65 per cent over ETL processes, saving $1.7 million.

Data virtualisation proves that connecting to data is far superior to collecting it. It provides a single source of truth for all data that flows through various business systems, enabling firms to access and combine data from multiple heterogeneous sources using business logic and to present information in single views. The benefits of being able to access data in this way are numerous. Data virtualisation can help them to improve their overall performance and efficiencies in a strategic manner, reducing costs and project cycle times and helping to enhance business decision making capabilities with real-time insights.

Businesses can take data virtualisation a step further with data fabric technology. This will automate data management functions using artificial intelligence and provide additional semantic capabilities through data catalogue, data preparation, and data modelling. It helps to reduce the burden in IT and data engineers, whilst enabling data scientists to quickly and intuitively get what they need to build models and develop insights. Put simply, data virtualisation helps businesses to monetise data whilst improving organisational flexibility and agility alongside boosting employee satisfaction.

In our data-intensive age, traditional integration methods are no longer fit for purpose. Being ‘data aware' isn't going to help businesses support their employees and deliver to their customers. Modern technologies, such as data virtualisation, could provide an answer for those looking to gain the upper hand and become truly ‘insights driven'.

Charles Southwood is Regional VP at Denodo