Traditional methods of data management, while common, have become increasingly inefficient, insecure and costly. The exponential growth in the world's data over the last few years has resulted in a paradigm shift in how this data is managed, and data mesh is fast becoming the go to approach.
What is Data Mesh?
The term data mesh refers to a new analytical data management architecture that is based on a contemporary, distributed approach. It allows end users to access and query data without first transporting it to a data lake or warehouse, allowing them to do so quickly. The data mesh's decentralized approach spreads data ownership across domain-specific teams who manage, own, and deliver the data as a product.
The goal of data mesh is to address the problems of data availability and accessibility at scale. Data mesh enables individuals to access, analyze, and operationalize business insights from virtually any data source without the need for expert data teams.
Simply defined, data mesh allows data to be accessed, available, discoverable, secure, and interoperable. Faster access to query data directly translates into a quicker time to value without the need for data transportation.
The data mesh connects separate data silos to aid organizations in achieving automated analytics at scale. It allows firms to break free from the consumptive trap of monolithic data architectures and avoid massive operational and storage expenses. This new distributed approach attempts to eliminate data access bottlenecks in centralized data ownership by putting data management and control in the hands of domain-specific business teams.
The three challenges that a decentralized data lake/warehouse attempts to address are the following.
Lack Of Ownership: who owns the data, the data source or infrastructure team?
Organizational Scaling: The central team becomes a chokepoint, as is the case with an enterprise data lake/warehouse.
Lack Of Quality: Although the infrastructure team is in charge of quality, it does not have a solid grasp on the data.
The increasing adoption of Data Mesh
Many organizations are now implementing this concept to overcome common challenges and inhibitors pertaining to their data management. They're recognizing that domain-specific data products, enabled by common support functions, can be assured with flexible access to data and critical benefit in terms of decreased time-to-market. This is a new architectural approach that focuses on data management instead of connectivity or orchestration.
A recent example, Microsoft is now offering a solution to this problem that will start rolling out in 2022, mesh for Microsoft Teams. The function combines the mixed-reality capabilities of Microsoft Mesh, which allows people in different real-world locations to collaborate and share virtual experiences, with the productivity features of Microsoft Teams.
Microsoft says this solution is also focused on enterprise collaboration, where remote workers can meet in virtual rooms to discuss projects and ideas and work collaboratively in meaningful ways.
What are the advantages?
The main goal of implementing a data mesh is to enable business users, analysts, and developers to have rapid access to all the data they need. In this way, the organization can break free from traditional operational constraints. In addition, adopting a data mesh yields immediate benefits such as the following.
Agility: The data mesh architecture seeks to create a more agile environment, in which data is decentralized to specific business domains. This approach achieves flexibility by establishing roles for information-ownership and delivery, independent of the prevailing organizational hierarchy. In this model, data owners are no longer reliant on IT for access or querying, creating an evolution away from centralized processes to distributed ones.
Faster Delivery: Setting up data infrastructure (such as data processing, data storage, logging, monitoring, and identity management) is frequently a challenge for data management operations. The structure of the Data Mesh is such that it hides the underlying complexity while enabling faster data delivery via a self-service, governable and centralized infrastructure.
Strong Central Governance: Traditional data architecture, which relies on a centralized data lake to reconcile the meanings and quantity of ingested data, is no longer effective as more sources continue to proliferate. To improve the quality of data delivery and ease of access, data operations should be decentralized to a domain and global data governance standards should be implemented. As a result, there will be no more mass data dumps into data lakes.
Cross-Functional Teams: In comparison to traditional data architecture techniques that encourage the segregation of skill teams with lengthy backlogs, Data Mesh promotes a solution in which domain experts and owners are in command. This is made possible by a stronger domain understanding, closer business and IT teams, as well as agile virtual teams.
What are some challenges associated with a Data Mesh?
Any new technological advancement will have a large learning curve involved, and data mesh is no different. This requires training the business users to take charge of their own data so that they can have a stronger understanding of what's going on with it. They should be able to easily identify whether or not the data they request is in compliance with the guidelines of their organization.
Although a Data Mesh allows for almost limitless access and autonomous management, it means that there will be increased data flow and more queries. This requires an enhanced IT infrastructure and support team to guarantee quality and integrity in large datasets.
The data mesh is a new architectural approach that emphasizes management of data, rather than the convergence of technologies. It enables you to break free from traditional operational constraints and scale ownership of your data across your organization. You can support business goals and lead agility and quality efforts within your IT team and users alike.
The data mesh promises to address many of the challenges common with traditional approaches and should be considered as a solution for organizations who are looking to embrace initiatives such as AI. It is an especially advantageous option for companies that want to take advantage of big data and digital transformation, even if their business model or use cases don't fit within the confines of a standard data stack.
Data mesh's ability to provide an interface that allows users to access, analyze, and operationalize business insights from virtually any data source without needing expert data teams is one of its most prevalent assets, and one of the many reasons it can shoot organizations far ahead in their data management processes.
If you would like to find out more about how you can make your data accessible, available, discoverable, secure, and interoperable, email us at Salesforce@coforge.com.
Other useful links:
Data visualisation 101
Tableau Visual Analytics
Data & Analytics capabilities
