A data mesh and data fabric can work together to create an advanced solution to a normally overwhelmingly complex challenge.
Data mesh and data fabric are often seen as two opposing approaches that oppose each other. While data fabric focuses on the technologies needed to support metadata-driven use cases in hybrid and multi-cloud environments, data mesh focuses less on technology and instead takes a people- and process-centric view. However, it is also true that data mesh and data fabric are two equally effective, yet different architectures that complement each other in what we call a meshy fabric approach.
Data as a powerful product
It helps to see data as a product. Data is a powerful asset and can also be packaged as a product with the right classifications, descriptions and quality. Think of your domain experts or data producers as retailers. They will be the ones to welcome people into the world of data and guide them to the best use in their class.
Viewed through this lens, data can deliver real ROI, with data producers acting as retailers. Data leaders can then understand how to make data more accessible and understandable for their consumers, and consider other useful contexts for consumers to use their data. To be successful, companies must fully embrace a culture of decision-making that puts their data at the center.
Data mesh inverts the common model of a centralized team that manages and transforms data for wider use. A data mesh architecture requires responsibilities to be shared among the people closest to the data. A data substance, on the other hand, focuses on the technologies needed to support metadata-driven use cases in hybrid and multi-cloud environments. Once data producers and consumers recognize their new symbiotic relationship, an organization can set up a data catalog to do the heavy lifting of creating a meshy tissue. By combining data mesh and data fabric, organizations can overcome bottlenecks and disconnects typical of data lakes and data warehouses, eliminating the need for data engineers to act as an intermediary between data producers and consumers.
How data catalogs support a meshy fabric
Data leaders can use the data catalog to establish governance principles that clarify best practices for data usage across the enterprise. Leaders can catalog policies related to data, either from an internal perspective or an external compliance perspective. Through a data management program, companies can create a list of terms that is standardized across the company to make it easier for users to find definitions. As a result, the data catalog acts as an important foundation of a painless data governance program that can support the entire organization and increase efficiency.
A best-in-class data catalog automatically ingests metadata using metadata connectors and applies behavioral intelligence to augment technical metadata with usage, popularity, origin, classification, naming, and other contextual information. This helps an organization prepare and present its most valuable data products for wider consumption.
For example, a Fortune 500 financial services company leveraged a data catalog to build a strong data culture and put data in the hands of teams across the company. This financial services company embarked on an ambitious digital transformation project using best-in-class cloud-native technology. With this technology, they built data mesh domains that provided data consistency across the business, decentralized data management to create a leaner organization and transform the customer experience across all channels. This included decommissioning their legacy data lake system and moving to the cloud as the front door of their data, enabling data governance and self-service.
This whole process shows how the marriage of data mesh and data fabric in a meshy fabric combines the best of decentralized autonomy. This allows a company to move at maximum speed with proper supervision and risk mitigation. In addition, data consumers now have a reliable source of well-defined data products with owners directly responsible.
For many companies, it is not necessarily a choice of one or the other, but a combination of both. Data mesh and data fabric work together to create an advanced solution to a normally overwhelmingly complex challenge. Data mesh and data fabric can provide a solution for enhanced decision-making to increase overall success by giving companies control and autonomy over their data. These are investments worth making, and companies that commit to these tools can realize exorbitant profits for their business.