Due to the increasing popularity of data mesh, in this post, data and analytics leaders highlight some implications of shifting towards this operating model and key strategies regulated industries should consider adopting. 

Data Leaders members can read the full post and connect with peers via the Data Leaders Hub.

Ensure a Clear Definition of Data Products

Data and analytics leaders advocate by starting with a clear definition of a data product. Use templates to organise data into smaller chunks, for example access policies and naming standard. Keep this definition separate from technical implementation to secure adaptability.

End-to-end Data Governance 

Embed data governance during implementation and throughout the entire data product development lifecycle. Collaborate with domain owners to define policies, distribute accountability across domain teams, and enforce compliance. Data leaders can automate this approach by setting up self-service templates for each data product. Creating a marketplace with many specific templates to check that requirements are met can help manage the data ops lifecycle of data products.

Tiered systems for data product standards 

Implement different quality standards (e.g., gold, silver and bronze) for varying levels of quality and effort on data products within a data mesh framework, aligning them with the purpose and intended use of the data. This approach provides flexibility and momentum while still maintaining a focus on governance and value.

From centralised to a hybrid approach: data literacy

Data literacy plays a significant role in the transition from a centralised to a more distributed organisation due to the challenges around business skill sets and the need for an ongoing commitment to take on the work. Investment in data literacy is therefore crucial.

From decentralised to a hybrid approach: common vocabularies

When creating data mesh in decentralised organisations, establish a common vocabulary or create mappings between different vocabularies to facilitate communication and collaboration and ensure ROI.

Data domain owners and role definitions

When appointing data domain owners, prioritise those with high business understanding and the context of data within their domain over technical aspects which will be handled by IT experts.

Pay-to-play model: shift from consumers to data owners

Consider implementing a pay-to-play model for data access, encouraging business engagement with data governance by requiring participation in data product creation: i.e. to access a set of data products created by other domains across the organisation, data consumers have to become data owners, joining the community that creates data products.

Evolutionary architecture

In fragmented IT environments, build an evolutionary architecture based on open standards and technologies to bridge the gap between legacy systems and new platforms. This will enable future evolution towards multi-cloud and multi-technology adoption for data domains.

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