Data Mesh Anti-Patterns
Data Mesh is an emerging socio-technical approach for data-driven organizations that enables them to build, deploy and manage data products. It has been gaining traction in recent years due to its ability to provide a more efficient way of managing large volumes of data in the context of complex organizations.
However, while Data Mesh can be incredibly beneficial when implemented correctly, it also presents some challenges that need to be addressed if you want your implementation process to be successful. In this post we will discuss 11 common antipatterns related to Data Mesh implementations so you can identify and avoid them as soon as possible during the development process:
- Let’s do a data product (DP). A common data mesh anti-pattern involves neglecting the data product lifecycle. To counter this, it’s crucial to address coherent data product creation, consistent evolution through versioning, and the establishment of governance and feedback loops. Moreover, we have to enforce the usage of standards in the definition of the DP, e.g. agile-lab-dev/Data-Product-Specification .
- The more DP the better. The second data mesh anti-pattern spins around the misconception that having more data products are always better, or even a DP for each use case. In reality, the optimal number of data products should align with the organization’s ability to consistently consume and utilize them. Overloading the system with unnecessary data products can lead to inefficiency and confusion.
- Do one, no feedback loop. This data mesh anti-pattern occurs when the feedback loop is absent, hindering the improvement and evolution of data products. Engaging with users and monitoring real usage are essential for maintaining a data product’s relevance, effectiveness, and internal market fit. Implementing a feedback loop through data product monitoring is fundamental to the success of the data mesh effort.
- Governance is something that happens outside. The fourth data mesh anti-pattern assumes that governance is separate from the process of building and maintaining a DP. In fact, governance should be embedded within data product development, with teams building data products in accordance with policies set by the governance support team. This integration ensures a more effective and compliant data mesh system. And don’t forget data quality, access management, data lineage, and other aspects of Data Governance, that are fundamental for Trust.
- The tool is going to solve everything. This data mesh anti-pattern arises the socio-technical nature of data mesh implementation. A successful data mesh journey requires transforming organizational culture, work processes, and incentive models, emphasizing the importance of the social aspect. Relying solely on a single tool to achieve data mesh success is a misconception that should be avoided. Don’t trust in magical solutions that materialize the architecture of Data Mesh for you
- Data products for everything (analytical and operational). The sixth data mesh anti-pattern involves using data products indiscriminately for both analytical and operational purposes. Data products should provide value through abstraction and be adaptable to different analytical use cases. Recognizing the distinct requirements of analytical and operational systems helps ensure that data products are effectively consumed and utilized.
- Change happens effortlessly. :D This anti-pattern assumes that change occurs effortlessly. In reality, adopting a data mesh approach requires significant effort from both the organization and individuals. Change is hard. However, the benefits, such as seamless, secure, consistent, and governed data consumption throughout the organization, make the investment worthwhile.
- Too popular too quickly. The hype drags us to perdition. This anti-pattern is the belief that data mesh suits every organization and that having multiple meshes is better. In truth, factors such as data size, organizational complexity, existing technology, and culture should be considered before embarking on a data mesh journey. It’s important to recognize that adopting certain principles, like data platforms, can be done independently and may be more suitable for some organizations. Maybe, Data Mesh is not for your organization.
- Paralysis by Analysis. This occurs when organizations or teams spend excessive time analyzing and planning their data mesh strategy, resulting in delayed action or implementation. Overthinking and overanalyzing can hinder progress and prevent teams from learning through experience. To avoid this pitfall, it’s important to strike a balance between thorough planning and timely execution, allowing for iterative improvements based on real-world feedback and experiences. Start as early as possible with PoC, MVP, adopting Data Mesh principles in a domain, or part of it, of your company.
- No one is the owner. The tenth data mesh anti-pattern is data neglect, no individual or team is responsible for the management, quality, or governance of a specific dataset. This can lead to poor data quality, inconsistencies, and reduced trust in the data, as there is no clear accountability for its accuracy and reliability. In a data mesh context, data neglect hinders the ability to create valuable and reliable data products, ultimately impacting the overall success of the data mesh implementation.
- Over or under-engineering solutions. The eleventh data mesh anti-pattern involves over or under-engineered solutions, which can affect the data mesh’s effectiveness by making it either too complex or limited in capabilities. To avoid this, a data mesh platform should offer a user-friendly, self-serve approach with the right balance of abstraction and extensibility, reducing the data developers' cognitive load. This empowers data product teams to work independently while allowing the platform to adapt and grow to meet evolving requirements, resulting in a more successful data mesh implementation.
I’ve gathered these insights from various sources, such as the Thoughtworks Technology Podcast, and my personal experience. These anti-patterns can be valuable learning points for individuals and organizations looking to adopt a data mesh approach. By sharing this information, I’d like to help others avoid common pitfalls and encourage discussion about best practices in implementing a data mesh.
Thanks to my colleagues in Globant standing all day with my conversation about Data Mesh.