In this article, we’re delving into the core of Data Mesh transformation, exploring the concept of Data Mesh Readiness. Before you take the plunge, there’s a pivotal question that needs answering: Is your organization, your domain, truly ready for Data Mesh?
As an expert inception facilitator, I’ve been helping large organizations navigate the complexities of Data Mesh transformation. These experiences have shaped a unique approach, a blend of Lean principles, a few workshop templates and Data Mesh concepts. But let’s not get ahead of ourselves.
In my previous article, Data Mesh Accelerated, Steve Upton and I — Paulo Caroli — shared a workshop that helps teams and organisations accelerate their Data Mesh transformation. Now, it’s time to roll up our sleeves and tackle a fundamental question that has to be answered before accelerating your Data Mesh transformation: Is your domain prepared for the Data Mesh journey?
Together, we’ll assess your organization’s readiness and explore the fascinating world of Data Mesh transformation. Are you ready for it? Let’s find out!
How to download and use the Data Mesh Readiness Assessment shared excel
Here is the step by step to download and use the Data Mesh Readiness Assessment shared excel
- Click on the Data Mesh Readiness Assessment shared excel link. This will open a read-only version on Google Sheets.
- Make a copy of the document or download it as an Excel file to your device.
- Review each criterion listed in the Data Mesh Readiness Assessment tabs.
- Record the results for each criterion in the ‘Result’ tab, which is the main tab of the document.
- Explore the result table and spider chart graph available on the ‘Result’ tab.
In the chart, a score of 1 represents low readiness, 2 indicates medium readiness, and 3 signifies high readiness.
The Data Mesh Readiness Assessment
The Data Mesh readiness assessment is based on Zhamak Dehghani’s Data Mesh book. It is a spider chart with eight aspects that score from low to high. Below, you’ll find each aspect explained, along with the low, medium, and high scores.
The criteria are: Organizational Complexity, Data-Oriented Strategy, Executive Support, Data Technology at Core, Early Adopter, Modern Engineering, Domain-Oriented Organization and Long-Term Commitment.
Belos is a sample result from the assessment.
Data Mesh Readiness: Organizational Complexity
Data mesh is beneficial for organizations facing significant complexity due to a multitude of data sources and diverse use cases. Entities like large tech services, banking, insurance, and retail, dealing with various data domains and relying heavily on ML and analytics, are ideal candidates for data mesh. For example, in healthcare, diverse data categories from different sources (hospitals, clinics, labs) make data mesh valuable for optimizing care models and insurance processes.
- Low: Limited proliferation of data sources and use cases, minimal complexity.
- Medium: Moderate proliferation of data sources and use cases, moderate complexity.
- High: Large proliferation of data sources and diverse use cases, significant complexity.
Data Mesh Readiness: Data-Oriented Strategy
Data mesh requires a strategic focus on deriving value from data at scale. It necessitates a commitment from product teams and business units to integrate data-driven decision-making into their applications. Without a strategic vision emphasizing data’s role in ML and analytics, motivating teams to share data becomes challenging.
- Low: Lack of strategic vision for data-driven decision-making.
- Medium: Partial commitment to data-driven strategies and applications.
- High: Strong strategic focus on deriving value from data, commitment to data-driven applications and services.
Data Mesh Readiness: Executive Support
Implementing data mesh brings about significant change, often met with resistance. Executive support and top-down engagement are vital. Successful implementations have backing from C-level executives to navigate resistance and make decisions favoring the platform’s progress over short-term solutions.
- Low: Lack of executive backing, resistance to change.
- Medium: Some executive support, moderate resistance to change.
- High: Strong executive support, proactive in managing resistance to change.
Data Mesh Readiness: Data Technology at Core
Data mesh relies on organizations that consider data and AI as core competitive advantages. Entities with data technology ingrained in their business functions are poised for data mesh adoption. In contrast, organizations treating technology as a support function and relying on external vendors are not ready for data mesh.
- Low: Technology viewed as a supporter, reliance on external solutions.
- Medium: Partial integration of data technology, some in-house capabilities.
- High: Data and AI integrated into core business functions, competitive advantage.
Data Mesh Readiness: Early Adopter
Data mesh is in its early adoption phase, appealing to venturesome leaders willing to experiment and adapt. Early adopters embrace emerging technologies, making them suitable candidates for data mesh. Late adopters, preferring established solutions, might need to wait for data mesh to mature further.
- Low: Resistance to adopting new, untested technologies.
- Medium: Openness to emerging technologies, cautious experimentation.
- High: Willingness to adopt novel, multidimensional technologies, eager experimentation.
Data Mesh Readiness: Modern Engineering
Data mesh builds on modern software engineering practices, requiring continuous delivery, DevOps, distributed architecture, and access to modern data storage and processing stacks. Successful implementation demands technology stacks that allow easy integration and empower smaller, distributed teams.
- Low: Outdated engineering practices, lack of modern technology stacks.
- Medium: Some modern practices, partial adoption of modern tech stacks.
- High: Continuous and automated delivery, strong foundation in modern software engineering practices.
Data Mesh Readiness: Domain-Oriented Organization
Data mesh assumes a modern business setup with domain-specific technical teams aligned closely with business needs. Organizations with centralized IT units, lacking continuous domain-oriented ownership, aren’t suited for data mesh. Domain-oriented data sharing relies on a close alignment between technology, technical teams, and business domains.
- Low: Centralized IT unit, lack of domain-oriented alignment.
- Medium: Partial domain-specific alignment, limited collaboration between tech and business teams.
- High: Domain-oriented technical teams, close alignment between technology and business domains.
Data Mesh Readiness: Long-Term Commitment
Data mesh implementation is transformative and requires long-term commitment. It cannot be executed as isolated projects but should be seen as a journey. Organizations need to commit to the vision and be prepared for a continuous transformation process to reap the full benefits of data mesh.
- Low: Short-term, isolated projects without a long-term vision.
- Medium: Medium-term commitment, limited scope projects.
- High: Long-term commitment, organizational vision and transformation journey.
Data Mesh Readiness: Should You Adopt Data Mesh Today?
Having assessed your organization against these aspects, the answer becomes clear:
- If your readiness scores are low, the answer is no. Focus on improving these aspects before considering Data Mesh.
- If your scores are medium or high and you have the capacity, the answer is yes!
- If your scores are medium or high but capacity is lacking, the answer is a qualified yes. Start small and build incrementally.
Start small and build incrementally: Domain, Use Case, Thin Slice, and Increment (DUTI)
Domain: Embarking on the Data Mesh journey begins with a careful domain selection. Choose a domain within your organization that exhibits high readiness scores. This strategic choice acts as the cornerstone for your entire transformation endeavor. By starting with a domain primed for Data Mesh integration, you set the stage for a smooth and effective initiation.
Use Case: Within your selected domain, it’s crucial to identify a specific use case that promises substantial value with minimal effort. This concentrated approach ensures a focused and efficient strategy, enabling your team to direct their efforts and resources effectively. Starting with a well-chosen use case provides the clarity needed to align your organization’s objectives with practical applications, ultimately magnifying the impact of your Data Mesh implementation. (Tip: Avoid commencing with intricate use cases; reserve these for later stages, once your organization has built up more Data Mesh expertise.)
Thin Slice: After pinpointing your use case, the subsequent step revolves around thin slicing it. This important step represents a strategic decision to deliver something small, akin to an MVP (Minimum Viable Product) to start with. Understanding these thin slices in detail is pivotal, as it forms the basis for incremental development, validating that you are progressing in the right direction effectively and efficiently.
Increment: With your thin slice strategy defined, it’s time to add increments iteratively. Each addition builds upon the last, ensuring a continuous cycle of growth and improvement within your Data Mesh journey. This iterative approach not only facilitates seamless integration but also allows your organization to adapt to evolving requirements and challenges. The incremental strategy ensures that your Data Mesh implementation remains agile, responsive, and capable of accommodating changes as your organization progresses on its transformative journey.
In the forthcoming articles of this series, I will go deeper into the intricacies of thin slicing, exploring techniques and best practices going from the discovery to lean inception for the use case. By following the DUTI approach, your organization can navigate the complexities of Data Mesh transformation with confidence, embracing a methodical path to success.
Preparing for the DUTI Transformation: Identifying Your Domains
Before embarking on the DUTI transformation, it’s essential to have a clear list of domains within your organization interested in implementing Data Mesh. This groundwork is crucial for a successful Data Mesh Readiness assessment. If this step hasn’t been completed yet, it’s vital to map out the domains in your organization that are considering Data Mesh adoption.
Choosing the initial domain is a critical decision. Ideally, you want to begin with a domain that exhibits the highest scores on the Data Mesh Readiness assessment, particularly focusing on domains with more mediums and highs. These domains serve as the optimal starting point for your Data Mesh journey, providing a strong foundation for the transformation process.
Conclusion: It’s journey is not a sprint
The Data Mesh journey is not a sprint; it’s a marathon of innovation and transformation. With the insights gleaned from the Data Mesh Readiness Assessment and the strategic guidance of the DUTI Lean Transformation Path, you possess a well-defined roadmap for your journey.
Attempting to transform entire organizations in one fell swoop can feel overwhelming, especially in the beginning. That’s why adopting a domain-by-domain approach provides a manageable and pragmatic route forward. By concentrating efforts on individual domains, one use case at a time, thin slicing and delivering incrementally, organizations can elegantly navigate the intricacies of the transformation journey.
Exciting Opportunity Alert! 🚀 Join us for our upcoming workshop: Guiding Data Mesh Lean Transformations: Facilitator’s Workshop. 🌟 Unlock the secrets of data transformation and become a skilled facilitator!