The primary value of a data mesh is that it allows organizations to use their data with a specific business output in mind. A data mesh allows an organization to find data gathered from similar, multiple use cases from anywhere in the network and combine it to deliver specific insights or outcomes related to specific topics. These use-case focused combinations of data are often called “data products.”
In other words, a data mesh is designed to find all the data needed to address a specific use case. If a data lake is broad and deep and must be searched carefully, a data mesh is stretched out across all data and can quickly identify the data necessary to support a particular outcome.
With the growth of hybrid data environments, the challenge of data democratization — enabling equitable access to data while simultaneously keeping it secure and implementing access control — has grown significantly more complex. A data mesh provides an overall framework that allows for consistent data ownership, while ensuring scalability and allowing it to be available quickly when needed.
Some added benefits of a data mesh include:
- Greater autonomy to data stakeholders to make decisions on data mgmt and use
- Enforcement via policy, using shared data sets as opposed to replicated data sets
- Transparency and visibility of data use across departmental silos
Zhamak Dehghani, credited with creating the concept of data mesh in 2019, maintains that a data mesh is a data platform encompassing not just the concepts and protocols, but also the equipment used. Dehghani sees a data mesh architecture as the next iteration of data storage beyond a data lake, with the added advantages of speed, efficiency and specificity. In a May 2019 blog post, Dehghani wrote,
“Be open to the possibility of moving beyond the monolithic and centralized data lakes to an intentionally distributed data mesh architecture; Embrace the reality of ever present, ubiquitous and distributed nature of data.”