Effective data management strategies are key to unlocking the organizational value of data and are drivers for digital transformation.

These data management architectures are the backbone for identifying business insights, efficiently operating enterprises, and enabling advanced analytics. As the strategic focus has somewhat shifted from siloed data warehouses to centralized data lakes, organizations are still trying to solve similar goals with data. While data lakes are effective at storing data in a central repository, without proper governance and evolution, they may become monolithic bottlenecks that are too rigid. Think of scenarios where volatile source data must be constantly manicured, there are time-sensitive requests coming from business teams for data pulls as well as new curation and alignment of data, and all the while advanced analytic teams are developing models which requires constant monitoring of data pipeline health.

A burgeoning approach, that is a natural evolution of data management, is called data mesh which can be thought of as an encompassing technical architecture and data strategy that is domain-oriented and tailored for self-serve as a response to the growing capabilities of users and growing appetite for vast amounts of data and flexibility to work with it.

Data mesh is comprised of four fundamental concepts: domain ownership, data-as-a-product (or data-as-a-service), self-serve data infrastructure platform, and federated governance. This architecture decentralizes data and allows teams to perform cross domain analysis at scale. In this recorded webcast Thorogood Data and Analytics Consultant Ben Dunmire discusses where we see aspects of data mesh being introduced and how we perceive its potential impact.

What We Cover:

• Overview of data mesh architecture and its strategic vision
• Potential opportunities and challenges of data mesh
• Demystifying data mesh in relation to other data management approaches

Is it for you?

• Are you interested about data mesh’s capabilities and want to learn where it can lead your organization?
• Are you interested in modern data platform techniques and want to stay abreast of new concepts?
• Are you looking to transition into a more decentralized data architecture where domain teams can make business decisions?