Data mesh is an architectural approach that allows for decentralized data management. Its goal is to support efforts to define, deliver, maintain and govern data products in a way that makes them easy to find and use by data consumers. Data mesh architecture is based on the concept of decentralizing and distributing data responsibility to people who are closest to the data and to share that data as a service.
The most common drivers for data mesh are: More data autonomy for lines of business (LOBs), less dependency on central IT and leveraging decentralization of data to break down silos (though some data centralization within a mesh architecture may be warranted). Despite its obvious appeal, be aware of the following prerequisites and challenges.
Data mesh architecture is not yet an established best practice.
The term is associated with varied approaches that differ by organizational model, management of the data and technology implementation. The organizational drivers also vary. They include removing IT as a bottleneck and rationalizing siloed datasets resulting from LOB-led data pipeline creation, or triggered by a cloud-modernization data-management initiative.
Data analytics leaders should not adopt data mesh architecture as a seemingly easy solution to their data management challenges. Although it formalizes common practices, it abdicates data accountability to LOB experts, which risks proliferating siloed data uses.
Data mesh success depends on the organizational model and data skills in LOBs.
If data literacy, autonomy and data skills vary greatly across departments, and if organizations lack the ability to operationalize data management activities, central IT will need to provide more support — at least at first. LOBs can evolve toward greater autonomy within a data mesh environment by creating new roles, such as data product owners, to manage the definition, creation and governance of data products. Organizations that lack commitment to building distributed data skills, however, should avoid data mesh.