As businesses scale data and analytics capabilities, the importance of a self-service data infrastructure becomes increasingly critical.
This is especially true as data mesh operational models see increasing adoption by forward-thinking, data-driven organizations.
What is data mesh?
“Data mesh is a decentralized sociotechnical approach — concerned with organizational design and technical architecture — to share, access and manage analytical data in complex and large-scale environments – within or across organizations.”
– Zhamak Dehghani
(Source: Interview with Datanami, April 1, 2022.)
If you’re creating a mesh architecture, domain ownership must be a primary focus. (You can read more about domain-driven ownership in our recent blog.) And for your data mesh model to work, a self-service infrastructure is essential for managing the complexities of data products and implementing governance policies.
Understanding self-service data infrastructure in a data mesh environment
Self-service data infrastructures are a critical component of organizations operationalizing data mesh environments, enabling teams to access and manage data products without depending on centralized data teams.
With a range of stakeholders needing access to data for a range of reasons, a self-service infrastructure becomes the platform where data producers and data consumers can interact seamlessly.
Data producers need to build, manage, and share data products, while data consumers require access to these products to gather insights, make decisions, or generate interconnected data products.
Self-service infrastructure removes the barriers that slow down data product creation and usage, scaling analytical capabilities across the organization.
The goal is to build an infrastructure that caters to the majority of data producers and consumers, not just the specific needs of a small, highly technical team.
How self-service infrastructure differs from traditional approaches
The surge in interest in data mesh has pushed the need for self-service infrastructure to the fore.
While traditional monolithic infrastructures serve centralized teams, data mesh self-service infrastructure is designed to cater to decentralized data teams within domains.
It’s a big cultural and technological shift. And your company is at its unique stage in the journey.
From centralized data warehouse to distributed command and control to domain-driven ownership, today’s enterprises often have the flexibility to enable distributed control on a central data warehouse or data lake and allow domains to directly manage tech stacks as their skills and capacity develops.
Mesh organizations are looking to empower domains so they have control over the infrastructure from the beginning, allowing it to evolve naturally in the right direction.
Integrating policy implementation with self-service infrastructure ensures data is used responsibly and in compliance with relevant regulations, all while maintaining data quality.
This approach caters to a broader audience, enabling more data producers and consumers to benefit from the infrastructure.
In short, self-service infrastructure enables domain teams to manage data products independently, without relying on a central team.
Better together: Data mesh and Collibra
Adopting a self-service data infrastructure is essential for scaling data and analytics capabilities across an organization.
By decentralizing control and providing a platform for seamless interaction between data producers and consumers, self-service infrastructure plays a pivotal role in overcoming barriers and unlocking the full potential of data products.
Implementing this infrastructure with the help of solutions like Collibra can smooth implementation and drive adoption.
Ultimately, you’ll build a more efficient, data-driven organization with data every user can trust, across every source.
Implementing self-service infrastructure with Collibra
Collibra is an ideal solution to support self-service infrastructure, providing a system of record for everyone in the organization that uses data.
Collibra Data Catalog allows data teams to create and manage a comprehensive inventory of data assets, regardless of their location. The catalog also offers vital information about data, including data lineage, metadata, and data quality. The result: Data teams can truly discover, understand, and trust their data.