Business leaders are investing in data virtualisation to enable self-service BI, data science, hybrid/multi-cloud data integration, and empower teams to pursue enterprise-wide data services.
On-premises data marts, data lakes and data warehouses are things of the past. With the dramatic growth of data across companies, storage needs are also increasing at the same rate. These on-premises systems are difficult to scale as they typically require scaling compute and storage together, which is not practical. Modern cloud variations of these systems are very popular as they simplify scaling and can handle tremendous volumes of data.
However, organisations still run into the same challenges they did with the traditional on-premises versions. They follow the same fundamental design of centrally collecting data, using the traditional Extract Transform Load (ETL) style of getting everything into a central repository in a consistent format. While the modern cloud-based versions of these platforms remove the constraints of traditional on-premises deployments, simplify scaling, and are key components of a modern data architecture, the same reasons that made it impractical to consolidate everything to a single platform in the past still hold true today. To successfully modernise a data architecture, organisations must add logical data management to their data strategies.
A modern data architecture must balance the physical collection of data, as occurs in a centralised architecture, with the logical connection to data, which occurs in a decentralised architecture. Logical data management provides a unified data access layer across all enterprise data assets that enables immediate access to any dataset without needing to first copy or replicate it.
The foundation of a logical data architecture is data virtualisation. It enables users to access data from multiple, disparate sources through a unified, logical view, hiding the underlying complexity of the data sources and providing a simplified, standardised view of the data to users. This enables users to query and analyse the data as if it were in a single, integrated data source, even if the data is stored in different formats, structures, or locations.
Globally, the data virtualisation market is projected to reach $22.2 billion by 2031, a considerable 593.75% increase from 2021. With its increasing popularity and trend, experts believe the market will exponentially grow in the next few years and be comparable to the overall data analytics market.
Powered by Denodo, Datatechvibe recently conducted an exclusive C-Suite boardroom in Bahrain to talk about Data Virtualisation – Democratising Data On The Go. In the age of building peer-exchange resources, this was a part of the series of boardrooms with Denodo, and it aims to form a community of like-minded business leaders to explore insights, ideas and best practices.
It had Denodo’s business leaders, Seema Alidily and Ahmed Alzamzam Sales Directors Middle East and Alexey Sidorov – Data Management Director & Chief Evangelist Middle East discuss how enterprises are revolutionising their data management through logical data management powered by data virtualisation by delivering a single, real-time view of data from multiple sources. This opens various windows for democratising data, granting more comprehensive access to more people and leading to better decision-making, increased efficiency, and significant cost savings.
Connecting data complements data warehouses and data lakes
Logical data architecture and management enable access to multiple, diverse data sources while appearing as one “logical” data source to users. It is about unifying data that are stored and managed across multiple data management systems, including cloud-based databases, enterprise data warehouses, data lakes, and other data sources like applications, big data files, web services, and the cloud to meet every analytics use case.
A logical data layer that integrates and manages all enterprise data siloed across disparate systems; data virtualisation delivers data to business users in real-time.
Reinforcing the idea that having direct access to data is more beneficial than just storing it, this real-time data access and analysis allows companies to dive deeper into reports and access on-demand information. It enables business leaders to uncover hidden insights and patterns, make discoveries, find opportunities, and take informed decisions based on real-time updates.
Opportunities across industries
Consider the oil and energy landscape. Due to the growing popularity of smart cars, smart homes, and other smart products, oil and energy companies depend on advanced analytics for metering, billing, oil exploration, outage management, and other activities. As a result, the industry is significantly impacted by the enormous amount of data generated by such machines.
Research reveals that the global energy and utilities analytics market size is expected to cross $4 billion by 2025 at a CAGR of 16.3%. Can data virtualisation help with the onslaught of data and make good business decisions? Yes.
Data virtualisation solutions can offload large data volumes to inexpensive Hadoop repositories or combine multiple primary data stores. It enables energy companies to decrease Total Cost of Ownership (TCO) while increasing ROI. Because the data technology can combine data in real-time, even across structured or semi-structured, it can support real-time analytics based on streaming, social media, or even sensor-based data.
Analysing such machine-generated data shows immense potential for generating business insights and increasing revenue. However, data governance becomes critical.
Better governance, better decisions
Recent research reveals that the data governance market will grow at a CAGR of 22.30% by 2029. In the age of increasing complexity in all aspects of data technology and architecture, IT departments need to simplify their approach to data governance to succeed.
By leveraging data virtualisation technology, compliance teams can take advantage of the centralised data access layer to provide a consistent application of security and governance policies across distributed data sources by leveraging advanced mechanisms to manage authentication and access to specific data.
For instance, if the column is marked as personally identifiable information (PII), permissions can be set not to share that information or only to share it with a subset of authorised users. This centralised data access layer supports advanced features, such as an attribute-based access control (ABAC) system to enforce security policies for data access. The policy engine defines and enforces security policies, which can be configured based on various factors, including data sensitivity, user roles, and business requirements. It will also integrate with your organisation’s existing security and governance tools.
Data virtualisation will remain a top priority for decades to come. And accessing all the company data at the source without moving them is an added advantage. It is critical that CIOs and CTOs leverage data virtualisation to address data-related challenges, translate rich, real-time data into meaningful insights and make informed business decisions.