Data Mesh Adoption Framework: Measuring Progress

In the previous articles (Introduction to Data Mesh and Data Mesh: iterative adoption)  we introduced the theoretical pillars of the Data Mesh approach as abstracted by Zhamak Dehghani and provided an iterative implementation framework.

This article is an addition to the adoption framework focusing on evaluating the Data Mesh implementation.

Objectives and Metrics

Objectives and metrics are essential components of any successful transformation initiative. They provide clear, measurable goals and a way to track progress towards those goals, helping organizations to align efforts and resources, ensure accountability, and facilitate data-driven decision-making.

Example objectives and metrics for social media improvement initiative (source: Agility PR Solutions)

Why Objectives are Important

Tangible objectives are critical to the success of any transformation initiative. They offer precise, measurable targets that help to:

1. Align efforts and resources towards a common purpose.
2. Track progress and measure success.
3. Ensure accountability and ownership among stakeholders.
4. Facilitate decision-making and prioritize actions.
5. Communicate the vision and benefits of the initiative to stakeholders.

Having well-defined, tangible objectives helps to focus and guide the transformation effort, allowing businesses to accomplish their desired results and adjust their course as necessary.

Why Metrics are Essential

Good metrics are crucial for evaluation and assessment, as they:

1. Measure progress towards goals: Metrics provide a way to track progresses towards specific objectives and determine whether the initiative is on track to meet its goals.
2. Facilitate decision-making: Metrics provide data that can be used to inform decisions about what is working well and what needs to be improved.
3. Support continuous improvement: Metrics help organizations identify areas for improvement and continuously refine and optimize their processes and practices.
4. Provide a basis for comparison: Metrics can be used to compare the performance of different initiatives, teams, or processes, helping to identify best practices and areas for improvement.

In conclusion, well-designed metrics are a crucial tool for evaluating the success of a transformation initiative and continuously improving its outcomes. By setting clear objectives and tracking progress through meaningful metrics, organizations can ensure that their transformation initiatives are on track to achieve their desired results.

Defining objectives

Defining objectives for Data Mesh initiatives can seem hard at first but we can make the task way easier by projecting the objectives over the implementation phases and Data Mesh pillars.

In addition, best practices in objectives definition such as SMART (Specific, Measurable, Achievable, Relevant, and Time-bound) could help define good objectives.

Let’s take the example of Data as a Product pillar, possible “Increase data products production” objective could be defined as follows:

Example Data as a Product objective over the three implementation phases

Following this example, we can generate objectives in a progressive way. Those objectives could be broken down into sub-objectives with even smaller targets. For example, if the bootstrap phase will span over a year, the global objective could be broken into the following sub-objectives:

Example of an objective breakdown over several quarters

Depending on how your organization defines goals and objectives, you may need to adapt the above to match frameworks like OKRs, BSC(Balanced Scorecard), Six Sigma, etc.

Defining Metrics

The most important aspects of metrics definition are relevance, simplicity, and freshness:

• Ensure that stakeholders, including domain owners, are involved in the process of defining metrics.
• Avoid using too many metrics as this can lead to information overload and make it difficult to focus on important parts.
• Regularly review and update metrics to ensure that they are still relevant and that progress is being tracked accurately.

To continue with the Data as a product pillar, here are examples of metrics we could track (by objective):

Example metrics for Data as a Product pillar objectives

By following these principles, organizations can ensure that they are on the right path. Good metrics will help them make informed decisions.

Adopting the organization’s culture

When you start adopting a new initiative such as Data Mesh, it is important to not innovate on every level, if your company already has standards and ways of defining and measuring the success of projects and initiatives.

It is important to stick to what is already proven and to try to bring expertise and experts that worked on other projects to help and adapt the initiative to the local enterprise environment.

Comic source: https://xkcd.com/

There are no “secret sauces” and every context is different. Objectives and metrics for Data Mesh should not differ 360 degrees from how other initiatives are being managed inside your organization.

Conclusion about measuring progress of Data Mesh

In conclusion, setting clear and tangible objectives and defining relevant and simple metrics are crucial components of a successful Data Mesh transformation initiative.

It is also very important to adopt and align with the existing culture and standards of the organization in defining objectives and metrics. By doing so, organizations can ensure the success of their Data Mesh initiative and make data-driven decisions.

In the next and final article, we will discuss the challenges of implementing Data Mesh and will answer some frequently asked questions about the concept.

You want to read more about Data Mesh, please consult our other posts:

• Data Mesh adoption framework: introduction to Data Mesh
• Data Mesh adoption framework: iterative adoption