Data silos are a persistent issue for organisations of all sizes and sectors.

In fact, a recent IDC study revealed that data silos cost the global economy an astonishing $3.1 trillion each year.

Put simply, data silos occur when multiple departments and individuals within a company use different applications, governance, databases and solutions to manage their data.

This type of data management poses a significant challenge to organisations. Apart from hindering collaboration, it makes it more difficult for your employees to conduct proper analyses for business decisions and slows down innovation.

This situation is common nowadays, given the variety of solutions and architectures for hosting data within organisations, such as multiple data sources and hybrid cloud hosting. Two data architectures stand out as a way to avoid data silos: a data mesh and a data fabric. This article explains why these can address these issues.

The Foundations of Data Fabric Architecture

Firstly, a data fabric is an architectural approach that uses metadata and automation and integration tools to create a centralised data layer across disparate sources. The following features and principles are associated with it:

The metadata used to describe the context of the data is created and stored within a centralised metadata management system. This enables employees to access consistent data descriptions, including information about compliance and sensitivity.

Business teams can also benefit from AI tools for exploring datasets, as these tools can be used to load and transform raw data when ETL solutions are deployed.

Finally, a data fabric architecture provides seamless data access and makes it easier to build a common data governance framework thanks to a centralised governance infrastructure.

In short, the primary objective of a data fabric is to connect and manage data consistently across different environments. This is particularly relevant for companies with multiple data sources from cloud services and on-premises infrastructures.

The Decentralised Nature of Data Mesh

On the contrary, the data mesh approach is characterised by decentralisation.

Rather than having a single centralised data team manage all of an organisation's data, ownership of data is distributed across business domains (e.g. marketing, finance, supply chain, HR).

These domains are responsible for collecting, cleaning, storing and sharing their data, just as they own their operational systems.

This puts data in the hands of those who understand it best, thereby reducing the bottlenecks created by centralised data teams and fostering accountability for data quality, accuracy, and accessibility.

Data is also treated with the same care as software products, with updates and documentation detailing the data's structure, as well as quality indicators.

Each domain is independently responsible. They have the freedom to decide how they share and manage their own data. The organisation provides a set of self-service tools for all domains to manage their data.

In terms of governance, a data mesh architecture operates as a federated infrastructure, meaning it is shared across domains rather than being controlled by a central authority. While each domain adheres to global governance standards (e.g. data privacy, security, and metadata requirements), it has flexibility in how it manages its own data internally.

The Hybrid Data Strategy: Fabric + Mesh

A data fabric establishes a centralised, connected layer through which data can flow seamlessly across silos. It reduces friction by using metadata and automation, while ensuring consistent governance and access.

In contrast, a data mesh breaks down silos by decentralising ownership. It promotes data sharing through standardised, discoverable data products. It also encourages a data-driven culture across domains.

Depending on your business's needs and the maturity of your infrastructure, either approach could be relevant.

You may even wish to consider using both approaches, as they can coexist within a data ecosystem that addresses potential challenges relating to individuals and technology alike.

However, both approaches come with significant prerequisites. A Data Fabric requires robust metadata management, advanced data integration capabilities and AI-driven tools to automate discovery, governance and data movement.

In contrast, a Data Mesh requires a high level of organisational and domain maturity. This means business units must clearly understand their data responsibilities and collaborate effectively across boundaries.

To ensure consistency, trust and interoperability across all domains, experts must design clear ownership models, establish data product standards and implement federated governance frameworks.

In conclusion, there is no one-size-fits-all approach; the most effective strategy would be to combine the automation of a data fabric with the decentralisation of a data mesh. Both aim to achieve the same goal of breaking down data silos and enabling a unified, data-driven enterprise.