Revolutionary Data Management to reap exponential profitability!!!
Talking about “Data Mesh” in today’s world, the first thing enterprise decision makers and technocrats vouch for is high reliability of data management, AI and ML technologies. Google had reported earlier in 2021 that the Data Mesh trend has literally won-over the “Data Lake” systems and concepts that had been quite popular within the IT departments of global enterprises.
Most technocrats have gone to the extent of certifying Data Mesh implementations in enterprises. They serve a whole gamut of purposes right from vindicating risks pertaining to data management, facilitating precision peer-decision making several notches and ploughing out higher organizational profits.
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Data mesh – powerfully go domain agnostic
Data Mesh is not a mere data architecture, it is the ideal way for enterprises to organize every task around “data” as the core, including its ownership and activation!!!
IT and technology experts the world over, look at Data Mesh as the ultimate, robust Data Management concept. It can perform data management, “beyond the limits”, irrespective of data platform restrictions/protocols, or the challenges posed by singularly centralized data platforms.
Data Mesh provides enterprises the breezily perfect modern data stack technology:
Provide powerful insights from data warehouses or data lakes (popularly called “data swamps” considered to have unnoticed, useless and stagnated data), that could have previously gone unnoticed by stakeholders and decision makers, to aid them make positively impactful business choices.
Flawlessly balance enterprise data management, for both centralization as well as decentralization of meta data.
Bring about data design and architecture that is purely domain driven.
Super-specialist and Core-competent data engineers now have it easy: Flawlessly manage huge volumes of complicated data warehouse tasks in the form of ETL jobs, reporting, table structuring, which otherwise were seen.
One of the best ever visionary references on Data Mesh can be found in the 2-part series, “Toward a Data Mesh” (Part 1, Part 2) by, L’Oréal CIO Francois Nguyen.
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What exactly is the advantage of Data Mesh for enterprises?
Traditional Data Warehousing technologies work around single/monolithic data sources, the latest in the list being the Data Lakes.
On the contrary, Data Mesh is more of a paradigm Data Management concept, that works dynamically across disparate systems as they are. It includes another data capture architectural layer for analytics usage.
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Data Mesh Key takeaways:
Capturing huge value and ROIs is the bottom-line!!!
Data discoverability
Data addressability
Data reliability
Data self-description
Data Inter-operability
Data Security
Capturing real value out of Data Mesh
The best bet to achieve this would be to collaborate with ideal technology partners. We are here talking about Data technocrats who have been consistently working hard, putting to practice and being constantly updated with the theory of latest in Data Management technology.
At this juncture, experts are clear about one basic thing – the best way to approach Data Mesh implementation is to focus and work around just the end goal of perfectly streamlined, inter-operable, easily accessible, secure, reliable Data Management system.
The following figure illustrates the working of a typical Data Mesh from, martinflower.com:
Reference: https://martinfowler.com/articles/data-monolith-to-mesh.html
In traditional systems, the front-end/back-end applications also have the microservices concept. It also has containers with pseudo-microservices implementations.
However, after implementing Data Mesh, lack of the microservices hardly matters. With an entirely distributed architecture and data demarcation and at the same time, capture complete data insights by interacting with your data.
The best ever advantage of this concept flowers out while building systems such as DAAS (Data-As-A-Service) or DAAP (Data-As-A-Product)
Capturing real value from Data Mesh means to precisely hit its end results. Here is where the role of data products comes into being. It does not just stop at simple dashboard analytics implementations. Real Data Mesh value comes from how the enterprise is able to work around smart and data rich products that impact business bottom-lines. It includes an array of applications serving network anomaly detections, fraud prevention, prescription engines that can recommend for delightful, real-time customer and user experiences.
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Putting it more simply, if mammoth data oceans are required as base platforms to set-up enterprise Data Mesh, it is Data Mesh as such that forms the foundation for data product builds.
