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In the era of this big data, organisations are striving to become more data-centric and gain better insights from their data to boost efficiency and profitability. With the desire to extract more value from data, data virtualisation is emerging as a solution that helps organisations achieve this vision.

In this blog post, we will explore how data virtualisation addresses the challenges posed by disparate data systems, the growing number of data sources, and the need for a secure and central view of data. Integrating this concept with the need for real-time data integration, we will address how data virtualisation can empower organisations to react quickly to events, make informed decisions, and unlock the true potential of their data assets.

The limitations of traditional data integration

Before delving into the transformative potential of data virtualisation, let’s understand the challenges faced by traditional data integration. The reliance on data warehouses for data integration poses several challenges:

  • Data sprawl: This approach increases the likelihood of data sprawl, where data becomes scattered across multiple repositories, making it challenging to manage and access efficiently.
  • Data replication: The process of moving and replicating data is time-consuming, often requires significant effort and increases costs.
  • Security risk: Ensuring data security and compliance becomes more complex when data is distributed across multiple locations.
  • Fragmented view: Drawing real-time data from various locations, formats and source types can be difficult to configure and execute.

As businesses seek to extract greater value from their data, achieving a scalable and adaptable central data view becomes increasingly essential. Unfortunately, traditional approaches such as data warehousing can struggle to deliver these real-time insights due to the delay in uploading new data.

In a recent engagement, we encountered a scenario where an organisation was pulling data from multiple disparate SaaS applications into a data warehouse in the public cloud. This allowed them to perform data science and analytics post extraction. However, the client faced a significant drawback with this approach. The data was only brought into the data warehouse at the end of each day, resulting in decisions being based on outdated information that didn’t meet their use case requirements. The client desired the ability to access all the data collectively in near real-time to capitalise on potential efficiencies and reduce overspending on resources. Unfortunately, the current method fell short of fulfilling these objectives.

Benefits of real-time data integration with data virtualisation

With data virtualisation, a virtual abstraction layer connects directly to each data source, presenting a unified view of the data that business users and applications can access and query in real-time, regardless of the protocol and format required. By accessing data directly from each source, data virtualisation eliminates the need to relocate or replicate the data into a separate data warehouse. This not only saves on storage costs but also ensures that the organisation has a single, accurate, and current version of the data.

Effectively, by utilising data virtualisation to achieve real time data integration, organisations can achieve:

  • Enhanced responsiveness: Real-time access to data enables organisations to respond rapidly to evolving events and make data-driven decisions without delays.
  • Cost savings: Data virtualisation eliminates the need for additional storage and maintenance costs associated with traditional data warehouses.
  • Data integrity and security: By accessing data directly from its source, data virtualisation maintains a single copy of the data, ensuring accuracy, integrity, and efficient governance.
  • Scalability and flexibility: Data virtualisation allows organisations to scale and adapt their data integration efforts as the business evolves and new data sources emerge.

Applications, alternatives and moving forward

In the real-world context, data virtualisation can have diverse applications across industries. From empowering personalised customer experiences and enabling agile responses to changing market trends in the retail and e-commerce industry, to integrating real-time data from sensors, machinery, and supply chain systems to optimise production and productivity in manufacturing – data virtualisation presents a compelling solution for organisations seeking real-time access to disparate data sources.

By securely connecting directly to each data source and providing a unified view of the data, organisations can leverage real-time insights without the need for data duplication or reliance on traditional data warehouses. Data virtualisation enables organisations to react quickly to events, make informed decisions, and achieve their data-centric vision.

Is there an alternative approach to achieve this? Increasing the frequency of data ingestion into the data warehouse is an option, but it would significantly increase the volume of data stored. Considering that the data already resides in each of the SaaS applications, duplicating and separately storing it becomes redundant and costly. Whichever way you look at it, this method falls short in terms of efficient data security and governance compared to a data virtualisation solution.

Whilst organisations should consider embracing data virtualisation as a forward-looking approach to real-time data integration, it is essential to have the right governance and management support in place. By prioritising data virtualisation as a key technology, organisations can enhance time-to-data, strengthen data security and governance, and unlock valuable opportunities that arise from placing data at the heart of their operations.


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Dan Irven

With over a decade of expertise in data infrastructure and storage architecture technologies, Dan offers invaluable knowledge at the forefront of the industry discourse. His thought leadership content goes beyond merely simplifying the complexities of data infrastructure. It provides practical advice for refining storage architecture, improving both performance and scalability.

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