Data Mesh Adoption Framework: Iterative adoption

Data Mesh can be seen as a sociotechnical transformation that aims to break down monolithic Data platforms into more distributed and domain-driven architecture. It aims to increase reusability, scalability, and autonomy within domains while reducing bottlenecks and circular dependencies.

In our previous article “Data Mash Adoption Framework : Introduction to Data Mesh”, we introduced the theoretical pillars of the Data Mesh approach as abstracted by Zhamak Dehghani and some criteria to assess your company readiness to adopt this approach and potentially have successful outcomes and advantages.

This article focuses on implementing a Data Mesh as a transformation initiative. The implementation process can be complex and challenging and requires a thoughtful and strategic methodology.

Bootstrap, Accelerate, and Scale

One way to navigate the different phases of implementing a Data Mesh architecture is through the Bootstrap, Accelerate, and Scale phases. These phases are common in digital transformation initiatives and provide a framework for organizations to follow as they adopt this architecture.

Evolutionary adoption of a Data Mesh architecture

The idea is to follow an iterative approach and to adapt your approach to solve the right problems at the right time. For Data Mesh, this is particularly true as the concept is still maturing, it is very dependent on the actual organization process and structure, and its value is yet to be universally proven.

The three phases can be seen as maturity phases:

• Bootstrap: is the initial phase of the transformation initiative. During this phase selected (high ROI) domains demonstrate the feasibility and value of the proposed Data Mesh architecture by delivering data-driven use cases. A minimal infrastructure is provided to allow the implementation of use cases. The main goal of this phase is to validate the concept and ensure senior management support.
• Accelerate: the Accelerate phase is the next step in the process, it should focus on delivering more value to business domains while building strong platforming capacities. During this phase, the team focuses on expanding the Data Mesh foundations, adding more features to the self-serve platform, and improving the governance processes.
• Scale: during this final phase of the Data Mesh adoption process, the Data Mesh architecture is generalized to all domains and teams, and the focus is on making the architecture sustainable in the long term by optimizing governance practices while improving the overall platforming capabilities.

Investment Levels

Different investment and effort levels are required during the tree phases of implementation due to the nature of the work and the goals of each phase.

Investment / effort levels during the three phases

During the Bootstrap phase, the focus is on proving value to business and senior management to ensure budget and long-term engagement. During this phase, the following targets should be considered:

• Onboard limited number of domains and help them achieve PoCs and MVPs (early wins)
• Provide opinionated minimal infrastructure and a limited self-serve capacity (e.g. ticketing or PRs)
• Produce small number of source-aligned data products while laying out minimal requirements for data products (e.g. metadata specifications)
• Setup a basic operating model for governance.

On the second phase, the effort of enabling domains to deliver value needs to continue while focusing on industrialized capabilities to give domains more autonomy and allow them to try more ideas and address a wide range of use cases.

Finally, on the Scale phase, the focus is shifted on data products and governance to allow reusability of data while ensuring strong governance practices.

Agile at heart

The transformation process should be agile in order to adapt to the needs and requirements of the company’s business. Data Mesh is maturing and will probably develop into a more solid methodology over the next few years.

One of the key principles to prioritize are flexibility and adaptability. This means that the adoption should be able to pivot and change course as new information, requirements, or obstacles arise.

Another important aspect of agile methodology is the focus on continuous improvement. This means that your Data Mesh adoption should be regularly reviewed and evaluated, and adjustments should be made as needed. This allows the domains to identify and address any issues that may arise and make sure that the solution is meeting the needs of the business

⚠️  To ensure success, it’s essential to involve the business stakeholders and domain teams in reviews. 

Conclusion about Data Mesh iterative adoption

Implementing a Data Mesh initiative requires a strategic and thoughtful approach, and the Bootstrap, Accelerate, and Scale phases provide a framework for navigating the different stages of implementation. Each phase has different investment and effort levels, reflecting the nature of the work and the goals of each phase.

Additionally, the adoption process should be agile, in order to adapt to the needs and requirements of the company’s business, this allows for flexibility, adaptability, continuous improvement, and collaboration.

In the next articles of this series, we will focus on the ways to measure and evaluate the adoption of Data mesh during the three phases.

You want to know more about Data Mesh? Read the other posts about this subject:

• Data Mesh Adoption Framework: Introduction to Data Mesh
• Data Mesh Adoption Framework: Measuring Progress