Unlocking Agility: Embracing Data Mesh for Decentralized Data Ownership
As with the evolution of data so too has the modern #datateam evolved. There is no one single solution to how to solve the problem. Recently I read Data Mesh by O’Reilly, to identify how to implement Data Mesh and modernize data architecture. The theory is that by decoupling parts of the data pipeline and putting the responsibility on the team or department, it creates an atmosphere or trust and flexibility.
The Data Engineering (DE) Team often does not understand the business enough to implement the BI layer or perform analytics and end up being dependent on an analyst and the analyst being dependent on Engineers to bring data. The Data Analytics team wants to perform Analytics quickly instead of waiting on the DE to do the engineering which ends up causing dependency and delays.
So, How do we solve this?
Using Data Mesh as a data architecture that promotes decentralized data ownership and aims to align technology and business through a product-centric approach. It is designed to address the challenges of traditional data architecture models, which often result in data silos, lack of data governance, and slow decision-making due to a centralized data ownership structure.
In a Data Mesh architecture, data ownership is decentralized, with each product team having ownership over its own data. This allows for faster decision-making and more efficient data management, as well as improved data quality and easier data access for the teams that need it. The architecture is based on the principles of domain-driven design, event-driven architecture, and microservices, and it encourages collaboration and communication between teams.
Pillars of Data Mesh Architecture:
Data Ownership: Decentralize the ownership of analytical data to business domains closest to the data.
Data as a Product: Domain-oriented data is shared as a product directly with data users. It would adhere to some guidelines to be shareable and reusable.
Self-serve data platform : Data core team empowers domain’s cross functional teams to share data and consume data without causing dependency by creating a self-serve data platform.
Federated governance : This model works on federated decision-making and accountability structure, with a team composed of domain representatives, data platform and SMEs.
Data Mesh :
Data mesh is a decentralized approach to share, access, and manage analytical data in complex and large scale environments. It enables domain teams to perform cross-platform data analytics without being dependent on Data engineers. The domain team ingests operational data and builds analytical data models as data products to perform their own analysis. It may also choose to publish data products with data contracts to serve other domains’ data needs.
The domain team agrees with others on global policies, such as interoperability, security, and documentation standards in a federated governance group, so that domain teams know how to discover, understand and use data products available in the data mesh. The self-serve domain-agnostic data platform, provided by the data platform team, enables domain teams to easily build their own data products and do their own analysis effectively. An enabling team guides domain teams on how to model analytical data, use the data platform, and build and maintain interoperable data products.
Adopting a Data Mesh architecture offers a promising solution to the challenges faced by centralized data teams. By decentralizing data ownership, treating data as a product, implementing self-serve data platforms, and embracing federated governance, organizations can empower domain teams to efficiently manage and utilize their data assets. This approach fosters a culture of collaboration and agility, ultimately enabling faster decision-making and more effective data-driven strategies across the organization. To embark on this transformative journey towards a Data Mesh architecture, organizations are encouraged to assess their current data practices, identify domain-specific needs, and start implementing decentralized data ownership frameworks within their teams. Embracing this paradigm shift will not only enhance data accessibility and quality but also pave the way for a more agile and innovative data-driven culture within the organization. Start your Data Mesh journey today and unlock the true potential of your data assets!
