The modern cyber battlefield doesn't respect organisational boundaries. Across defence networks, critical structured, unstructured, and semi-structured data sits distributed and siloed in specialised environments — from classified intelligence systems to operational command platforms and tactical edge devices to headquarters. In the public sector, for example, 65% of leaders struggle to use data continuously in real time and at scale, according to a recent Elastic study.

The defence establishment faces just such challenges, and the growth in the volume of security data generated across multi-domain operations isn’t slowing. When threats move at machine speed across networks, human analysts need to collaborate effectively across interoperable, if disparate, systems. The need is to improve visibility into individual domains and enable genuine collaboration across them, without compromising security or operational control.

Rather than centralising data — and wrestling with all the challenges of that approach — a data mesh instead embraces a distributed model built on four principles:

• Domain ownership ensures that the teams most familiar with the data maintain responsibility for it.

• Data as a product means information is well documented and accessible to authorised users.

• Self-service platforms enable teams to discover and use data without IT bottlenecks.

• Federated governance ensures security and compliance across the entire ecosystem.

Cross-cluster search is a key feature in Elastic’s data mesh approach, allowing teams to search across distributed environments without moving data. Analysts can execute a single query that securely retrieves results from multiple sources while respecting data access controls. This approach eliminates expensive data duplication across systems and offers up to 90% productivity improvements in IT operations. Unlike traditional approaches that simply forward queries to disparate systems, cross-cluster search provides a unified indexing layer: Data is indexed once and then available to any authorised user. This eliminates performance bottlenecks and inconsistent security models that plague other approaches, creating faster collaboration with stronger security. Data owners maintain control of their assets.

For organisations like the MOD, a global data mesh approach offers significant advantages, allowing data to remain at its source while being searchable. Cross-cluster search excels in these challenging environments. It enables interoperability between previously disconnected systems, making it a technical enabler of the broader interoperability goal. 

Queries can span geographical and organisational boundaries so that when an analyst needs to correlate threat intelligence across multiple domains, they can run a single search that returns unified results. This dramatically reduces response times during critical incidents. The data itself never moves, limiting or removing the requirement for duplication. Only the query and its matching results traverse the network, significantly reducing bandwidth requirements and maintaining data sovereignty. 

For defence teams facing constrained network environments, this efficient approach to data management delivers both operational and cost benefits through a unified platform approach instead of multiple disconnected tools.