Is your organization planning on scaling up (or down)? Are you planning on going global? Are you seeking proper use, segregating and accessing data without hassle? Do you like the idea of a decentralized, technologically-agnostic system? Data mesh might just be the thing for you. Once you know its use-cases, you can decide if your organization can be molded into it.
Working on four major principles, a data mesh is an architecture that makes for a cleaner and safer experience with analytics. The principles are as follows:
Think of domains like inventories for data pipelines. This makes it relevant in two ways. First, the principle of domain ownership provides a lot of automation power to teams that manage this data from one end to another.
The next thing is that the aforementioned automation makes for a smooth flow from the operational as well as the analytical end. This would strive to provide instant or quick responses from both ends.
What does this translate to in day-to-day terms? Better customer support. In a world where customer service being prompt is an absolute necessity, the principle of domain ownership would aid in the same.
This principle enables greater movement of data from one domain to another. Basically, data as a product implies that there are entities beyond the said domain, into other domains. The concerned domain teams communicate with each other in providing the necessary data.
This would not only deflower the existence of a centralized system, but in general provide high-quality data. As an organization, this would come in handy if you have global offices—more on this in a while.
Despite the existence of such domains, a self-serving data platform proves to be domain-agnostic. This way, teams can ensure access to the same data, despite coming from different domains. Think of it like the type-C innovation. Self-serving data proves to be useful in scaling data that is now being decentralized.
Before you bring forth your rebuttals, let us clear it out. Governance does not mean centralization here. In fact, the principles of governance implies smoother operability through standardization. The primary aim here is to create an ecosystem that complies with set standards by the industry; ensuring that the perils of centralization are avoided.
Before you decide if the data mesh architecture is suited better for your organization, you need to steer clear of a few questions. We have provided the most pertinent questions of them all—if you have affirmation on these fronts, you should avail data mesh.Also read: Top 3 Lessons I Learned from Growing a $100K+ Business
In the modern era, being tech-agnostic is almost a prerequisite to doing good. One of the reasons why some industries are rapidly expanding with the help of technology is the fact that they are adopting tech-agnostic measures.
An organization that is tech-agnostic does not comply by set standards of hardware, software, or their integration. If anything, it means that the architecture is so compliant that it can be interoperable using various languages, without any hardware or software bonds. If your organization falls under this, you should consider data mesh.
Can you add more computers on the system to make the operation run smoother? Can you scale in higher numbers so that operations are dealt with less burden? If these are some pertinent questions, then you should consider data mesh.
This comes in handy if your organization manages a large data size. If data mesh is implemented properly, it can be scalable.
Decentralization is an immaculate way in which no one entity can have complete control over domains or the data it contains. Decentralization involves teams having control over bite-sized data.
This enables a scope for greater representation of data, especially if microservices are involved in your organization—like how the big fishes like Amazon use the same for shipping.
If your organization plans on going global, having the same access to the same data through a data mesh is important. Say your office in Japan needs to get hold of data regarding certain shipments to India, data mesh would be a smart way to save time.
Data architectures generally run without any discipline. It is all messy and lacks rigor. But data mesh actually solves a lot of issues on this front. In fact, data mesh could be important to make this more uniform. Data pipelines can now be prevented from getting messy—and ensure easy flow of data within domains.Also read: Top 10 Programming Languages for Kids to learn
The benefits of data mesh outweigh the cons. Irrespective of the scale of operations, this architecture seems to be the future of data. While it still seems to be in its early stages, the potential of data mesh is truly endless.
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