How data fabrics weave a new advantage for insights

Data fabrics continue to gain popularity across businesses as Gartner identifies it as the "top strategic technology trend for 2022". This should be no surprise, given data fabrics have arisen to help enable modern data architectures to meet the growing demand for data-driven insights.

A data fabric framework makes it easier for organisations to manage data-driven insights across all data environments at all speeds in all data application scenarios. Organisations across Australia and the globe have seen marked increases in the number of user endpoints, machine-to-machine interfaces, and analytics capabilities, while the pressure for faster business intelligence and more worthwhile insights has not abated. This means organisations must seek more value from data sources not only located on-premises but across hybrid multi-cloud and poly-cloud environments.

To add to the mix, workloads often require historical data, real-time streamed data, or a combination of such, and this is where the 'weaving' of data fabric enters the scene. Data fabrics can support augmented data management and cross-platform orchestration through automation, reducing the amount of human input needed for data management and insights value. If done correctly, 'weaving' this data together speeds new insights and advantages for businesses and can be transformative.

Leading industry analysts are spotlighting data fabrics

As noted by major industry analysts, data fabrics are said to be on a Compound Annual Growth Rate (GACR) trajectory of around 10% from now to 2028, where these approaches will support a wide range of analytic, operational, transactional and governance use cases for a diverse set of applications across all industry verticals.

Leading analyst groups have defined this 'weaving' as a technology-enabled framework supporting many potential outputs, including data that supports insights workflows. A key value often cited is that a data fabric framework enables organisations to work across all data environments, at all speeds and in all data application scenarios. To further the maturity of an organisation's data administration and maintenance, data fabrics also can support automation intelligence, reducing the amount of human input required. In these ways, data fabrics promote enhanced data management and cross-platform orchestration.

Data Mesh versus Data Fabric

Industry analysts have recognised a difference between a data mesh and data fabric. Their view is that a data mesh acts as a solution architecture, enabling the building of business-focused data products without specifying technology. A data mesh is more about data pipes and lineage in a data engineering architecture, whereas a data fabric is a framework—an implementation design flexible enough to support multiple outputs and uses.

By adopting data fabric frameworks, organisations are given a chance to comprehensively manage the dispersed nature of modern data for the highest business value.

Given the amount of verification, deduplication, ratification, augmentation, and data retirement that needs to be executed inside modern data estates, the data fabric represents a crucial 'blanket' data management framework worth serious consideration by any organisation reliant on data for business advantage.

Four cornerstone advantages

By adopting data fabric frameworks, organisations can benefit from four cornerstone advantages.

These cornerstones are insights, innovation, information governance and inherent trustworthiness.

• Insights

The value of data fabric goes beyond governance and can fuel high-impact insights. In a world where data is now the lifeblood of all businesses, organisations need to think about their data management competency in the same way that they think about their installed base of operational equipment, the quality and standard of their procured services and levels of their staff skills base.

As organisations – both large and small – adopt data fabrics to automate and accelerate data management, they will be better able to use Artificial Intelligence (AI) and Machine Learning (ML) in data management workflows to augment their data approach and gain deeper business insights.

While insights use cases often are specific to a given industry — for example, answering the question of "what equipment might be in need of maintenance now to avoid failure?" for manufacturers — any industry with complex data estates will benefit from a data fabric approach for insights.

• Innovation

Disruptive innovation fuelled by data must also be considered. Developing and using a robust data fabric framework enables organisations to better orchestrate all their data estate. This breadth and scope support a prime mover advantage when seeking data-driven advantage, such as bringing disruptive new products and services to market.

An enterprise that leverages a data fabric approach will find greater agility in transforming its business through data-driven insights, yielding a new level of resilience through innovation. This agility will enable the organisation to better gain value from AI and surpass its competitors.

• Information governance

This cornerstone stems from a data fabric framework's ability to leverage and support compliance and governance.

The Australian and global marketplace is characterised by an increasingly complex array of regulatory and compliance needs – which are subject to change. These regulations demand enterprise data management that functions from a base of controlled access, auditability and traceability – a core requirement for any business that seeks to be in compliance. The unified view provided through data fabric frameworks can simplify and streamline this complexity.

• Inherent trustworthiness

A data fabric framework also helps ensure the inherent trustworthiness of data and its capabilities because a data fabric involves taking a unified approach to data management. This instantiates that only curated, trusted data is accessible for use.

Addressing the challenges of the explosion of data volumes and velocities is especially important in this modern age of cloud and hybrid data architectures. As data diversity continues to grow and data workloads become more interconnected and complex in their nature, data leaders will increasingly turn to data fabrics. Understanding what a data fabric framework can do to help manage these challenges and weave business value is a necessity.

The way leading organisations use data today is cut from a different cloth — one increasingly woven with data fabric frameworks.