The Power of Domain-Oriented, Federated Data Ownership

In today’s hyper-competitive environment, companies are increasingly opting for data lakes that enable real-time stream and batch data availability. Organizations are approaching this with the intent to ingest, enrich, transform, and serve data via a centralized platform.

However, for most organizations, this architecture contains several shortcomings:

  • Data is siloed, restricted to those teams and use cases, limiting the ability to share.

  • Lack of a holistic view, and the ability to discover data across the organization, limiting the ability to experiment and innovate.

  • A centralized repository is limited to the personas and associated use cases for which it can serve.

  • Such architectures are challenged with the ability to secure and govern data due to the central team’s limited understanding of the incoming data sources.

  • Inevitably, these architectures are costly, inflexible, and slow, limiting their ability to support an ever-changing business.


Chaotic lakes of data result in impatient and disconnected data consumers in addition to backlogged data teams. This results in data drift and the further creation of data silos, as users look to gain access to data and insights through alternative means .

Our data mesh services offer a paradigm shift for domains and business areas, with the capability to perform enhanced analytics without the need of a centralized data architecture. This system puts control in the hands of your business, enabling it to be responsible to manage its own data quality, governance, security, and availability.

Trianz Data Mesh Maturity Model

Trianz created the Data Mesh Maturity Model to assist clients in assessing and planning their data mesh deployment. This provides a clear pathway to think big but start small, allowing the organization to incrementally adopt and learn at its own pace, based on its maturity, culture, and capability.

What is Data Mesh?

Data mesh is a domain-oriented capability that enables you to have a conversation with your data—all your data—in a secure and governed manner. This enables you to ask questions and, via exploration, gain knowledge that uncovers unknown or unforeseen opportunities.


Experimentation. Utilizing data federation, business personas have immediate access to data to explore, understand, and gain insight. This stage connects multiple separate data siloes together to create a single panel of glass to said data.

Stage 1 is characterized by data utilization, but a lack of data exploitation. The business uses data as-is within its silo but lacks the ability to identify insights across domains to extract additional insight and value. Analytics is ad-hoc due to siloed data architecture, creating barriers to insight generation. Visibility of data meaning (single source of truth) due to limited data governance capabilities leads to data trust challenges.

Typically, the data strategy for each silo is owned and developed by IT with input from business stakeholders. It is more difficult for a true business-focused data approach, as each silo has different governance rules and access methods aligned with their needs. This results in organizational and cultural barriers to data access and insight generation.

At Stage 2, businesses must start to experiment with the elements of data ownership, where the silo is responsible or “owns” that data. In other words, it is responsible for the quality, security, governance, consistency, and availability of data within its domain.

At this stage, the business begins to understand that the data strategy is critical to its business strategy, and therefore starts to take more ownership of that strategy. To support such strategies, data agility becomes a critical focus. To support such agility, data connection and sharing across domains becomes imperative. However, this stage still involves centralized control, with centralized teams performing a majority of the activities around data operations.

The business has formed the general guardrails for data governance and access. The next step is to work towards federating this governance and access control framework so the business can mature to federated ownership and the development of data products.

Domains are actively taking ownership and stewardship of the development and sharing of data products. The concepts and practices of federated data ownership start to mature. As data products are established, these products are shared across domains and are used to create new customer experiences, generate new insights, explore new opportunities, and increase operational efficiency.

Stage 4 results in lower time-to-market for analytics as well as lower costs, in addition to enabling self-provisioning capabilities and reducing data team workloads while increasing output.

Governance is baked in and automated in the development of data products and data access, with flexible governance and security frameworks enabling data-as-a-Service models, as well as DataOps. The user experience is greatly improved and IT becomes a true enabler for the organization to be data driven.

This stage is where businesses reach full data mesh maturity. All data is treated as a product, with governance and ownership federated and managed by the originating data owner—with the flexibility of centralized guardrails. Data and Analytics (D&A) is a central pillar of the wider business strategy, with data guiding the majority of important business decisions. Insights are everyday decision making and touch points, leading to greater awareness and agility.

The organization is able to rapidly access new data sources at will, integrate, and create or enhance data products to accelerate the data advantage. Automated governance and security capabilities, coupled with absolute transparency greatly reduces risk. Domains are truly accountable and responsible for the data products within their domain. This reduces ambiguity by establishing a data trust through increased data quality, while also providing clear ownership to rapidly resolve issues.


Effective, Efficient Delivery of Impactful Data

Data is a simple yet complex aspect of businesses today. Businesses are often challenged with issues such as real-time access to data across their enterprise (some on-prem and some in a multi-cloud environment), application services not able to query the right data for analytical needs, the high costs of creating a centralized data lake, and the dilution in ownership of data.

Trianz’s data mesh services will assist you in overcoming these pain points effectively and efficiently. Our data mesh services include:

  • Building a data community by connecting those who wish to share data with those who wish to use data—in a secure and governed manner.

  • Increasing the ability to future-proof your data capabilities. Utilizing what you have now to bank the data dividend immediately, while taking advantage of future capabilities without major disruption or transformation.

  • Supporting digital transformation and digital experiences by ensuring your data capability has the required agility, elasticity, and flexibility.

  • Delivering a business-led, self-service capability that services multiple data personas, both human and machine.

  • Greatly reducing the cost of delivering capabilities and maintaining existing platforms, while also greatly reducing the speed-to-market for data products and insights.


Our services help in:

  • Enabling analytics-led digital transformation.

  • The adoption of a federated, computational data governance capability.

  • Ensuring interoperability of domain-based data products via a marketspace.

  • Facilitating the development of domains to create and share data products, increasing the data governance, security, and quality of data and analytics.

  • Reducing analytics program and initiative costs and overall burden.

  • Enabling IT to become the facilitator of business insights rather than data delivery.

  • The development of a componentized future-proof architecture.

Best fit

We assess that use cases for the utilization of a data mesh are grouped into five broad categories:

  • Experimentation. Business personas require immediate access to data to explore, understand, and gain insight.

  • Ease of Access. A simplified access point for data access and analytics, for all personas.

  • Self-service. Enabling business users to access, catalog, transform, prepare, and share in a secure and governed manner.

  • Agility. Diverse requirements for analytics to manipulate, process, and share, resulting in data across an ecosystem.

  • Regulation. When regulations prohibit the transfer of data between departments, companies, and geographies, limiting the ability to share.

Introducing Trianz Extrica: Expressway to Analytics and AI

Data mesh is not just a concept, but a catalyst for enterprise transformation. It shapes the future of both enterprise architecture and organizational operating models with an iterative execution model. Extrica harnesses the power of this data mesh concept, crafting tailor-made analytical solutions for enterprises across the globe.

Extrica, sculpted on the data mesh concept, equipped with a vast array of connectors, advanced analytics, and Gen AI capabilities, aims to revolutionize the way we manage data to insights to AI journey.

Extrica is the Netflix® of Data – a powerful, zero-code, self-serve platform, that reaches sources anywhere and creates reusable data products to deliver purposeful insights & AI in a fraction of time, efforts and cost.

® the NETFLIX trademark is owned by Netflix, Inc. and Trianz and its services are not affiliated with nor endorsed by Netflix, Inc.


A core tenet of the solution is federated data ownership, where Extrica supports the creation of a data community. Here, technical and non-technical users alike can create and share data products to uncover hidden opportunities and insights contained within the myriad data points across the enterprise. This involves data producers who own and share data, as well as data consumers who use this data in their day-to-day workflows.

Extrica sits right in the middle, governing ownership and data sharing for producers, as well as access to data for consumers using a standardized framework.


Considering a Data Mesh? Try Our Free Data Mesh Lab!

Trianz and AWS have partnered to demonstrate the value of a fresh perspective on how we perceive and interact with data leveraging data mesh architecture coupled with number of pre-built connectors to reach to data sources and SaaS application and Gen AI capabilities and advance analytics to enterprises across the globe.

Here, Trianz will conduct a proof-of-value, offering a zero-risk view into the benefits of the new paradigm and the new capabilities it can enable for your business.

By being directly involved, you will learn from data experts, accelerating the rate of adoption for these types of architectural approaches across your organization.

Data Mesh Lab comes in two flavors:

  • Data Federation: Guidance on how to design and build a data federation solution across multi-cloud and hybrid architectures. The deliverable is a working prototype that connects a series of data sources via the mesh, commonly supporting a business intelligence use case.

  • Federated Data Governance: Experience the methods of computational federated data governance in preparation for establishing a data community. The deliverable is a working prototype that demonstrates the ability to federate data governance via a direct query architecture.

Get in touch with the Trianz team for a free crash course on data mesh architecture.


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