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What this blog covers:

  • Challenges of centralized data management
  • Advantages of data decentralization
  • Comparison between centralized and decentralized architectures
  • Implementation of data mesh principles with Kyvos

Today, we live in a world where data fuels everything we do. Organizations are flooded with information, capturing data from every click, activity and customer interaction. However, managing this ever-growing data is not a walk in the park. The traditional centralized data architectures find it challenging to keep pace with mountains of data generated every day. Let’s find out how.

Challenges of Centralized Data Management

Traditional centralized data architectures store and manage an organization’s data from diverse sources in a single central repository, commonly known as a data warehouse. Sure, centralizing enterprise-wide data in common data source makes organizing, updating and retaining easy, but is it worth it if it comes with a downside of data silos, accessibility and scalability issues and security concerns?

Ironically, the process that brings all the data under one roof is responsible for data silos. In centralized architectures, multiple departments muster and store data in one single repository, but they might define data in different formats, structures and terms, creating different silos of information. This causes inconsistency and makes it challenging to analyze data across various departments. Over time, these departments develop a sense of ownership, restricting access to data and collaboration between teams. Limited data accessibility can lead to limited cross-functional insights and missed opportunities, hindering strategic decision-making.

In centralized data architectures, the system gets overloaded and can’t keep up as data volume grows. When business users from different departments access the central repository for their analytical needs, the system can run into computational and storage limitations. Growing data volumes also come with increasing processing demands, which can lead to slowdowns and performance issues. Some organizations add more storage systems, thinking it might solve the scalability problem. Instead, this creates siloed data and requires additional management overhead hence increasing complexity and costs.

Another issue with a centralized data repository is a single point of failure. If everything is stored in a single location, the cyber criminals only have to breach the security of one location. Also, if all the data is lost due to some technical glitch or malicious attack, organizations can collapse under scrutiny and might not be able to recover all the data.

Apart from the issues mentioned above, a centralized repository also brings a lack of ownership and accountability that impacts data governance in an organization. If ownership is not clearly defined in a company, it will create chaos and delays. For instance, imagine a sales team creating a list of all the prospects from a marketing campaign that happened a week ago. Later, when they analyzed the campaign’s data to find out how successful the campaign was, they witnessed some missing information, redundancies and inconsistencies. The campaign data was handled by three teams, where sales team entered the data, analysts used it to analyze how successful the campaign was, and IT folks handled this data in a database. Without defined ownership, it becomes difficult to find out who’s responsible and fixing these mistakes takes more time and incurs additional cost.

Therefore, organizations must opt for an alternative approach, such as data decentralization that offers distributed data ownership, accessibility and accountability to enterprise-wide users. Strategically implemented data decentralization can promote efficient data management, improve data quality, democratized data access and accelerate insights for timely decision making.

What is Data Decentralization?

Data decentralization is a modern approach that decouples the operational plane of data storage from the analytical plane. This approach makes data easily accessible to enterprise-wide users and helps them reduce development pipelines and burdens. A decentralized model democratizes data access to domain users and makes them responsible for managing and revealing their data to other teams. This prevents the creation of data silos that impedes the flow of information.

Data decentralization empowers businesses to augment operations without limiting themselves by scalability challenges. Since in this approach data is distributed across various nodes so is the workload. If needed, additional nodes can also be added to the network, allowing the system to scale horizontally. This way, the system can endure massive amounts of data without degrading performance.

Another advantage of decentralized architecture is that there is no single point of failure. Even if one node is compromised, other nodes can continue to work the same way, ensuring data availability and no delays in processes.

Implementation of a Data Decentralization Framework with Data Mesh Principles

Data mesh is a decentralized framework for data management that addresses the scaling complexities of managing a massive amount of data. In this approach, ownership and accountability are distributed among various departments of an organization.

This disruptive innovation empowers the domain team to independently manage and oversee their data assets by controlling the creation and updating of data models and analysis while contributing to a connected, cohesive ecosystem. Unlike the centralized data approach, there is no sole custodian of data.

Data mesh implements decentralization by using four fundamental principles:

Domain-oriented ownership: According to this principle, every domain is responsible for collecting, processing and availability of its own data. The principle accelerates innovation and reduces time-to-market by encouraging users to align their domain-specific needs with data management. It enables users to perform quick iterations and more relevant data analysis, fostering fast decision-making. To implement this principle, organizations need to follow three strategies: Firstly, they need to establish clear roles and responsibilities among domain users. Secondly, organizations should provide continued education and support and thirdly, they need to enforce robust data integration tools in their architecture.

Data as a product: This principle encourages domain-specific data consumers to think of their data as a product and consider all the questions that describe a robust product, such as accessibility, security, interoperability and comprehensibility. Since domain users are responsible for their data pipelines, they should apply these characteristics to data code, policies, metadata and so on. This principle prioritizes valuable information to facilitate faster access to insights.

Self-serve infrastructure: With the sole responsibility of owning the data, domain teams build, manage and deploy their data pipelines and APIs while maintaining full sovereignty in handling their data products. They need a self-service infrastructure without any underlying complexities of data product lifecycle management. This helps establish decentralized data governance by enforcing pre-defined rules to maintain data quality, security and compliance across the mesh. It also encourages users to collaborate with cross-functional teams effectively and share relevant information.

Federated governance: Implementation of federated governance within data mesh environment is of utmost importance to avoid interoperability issues and duplication of effort. The model enforces common data language on all the data products. Standardizing business definitions, metadata, standards and policies among all the business domains is the key to ensuring data is effectively integrated and analyzed. It empowers enterprise-wide users with democratized data access, coherence and compliance with organizational objectives.

Unlock the Power of Data Mesh with Kyvos

Kyvos is an optimal choice for organizations implementing data mesh as it masterfully blends the three important factors necessary to promote data democratization: accessibility, performance and robust governance. With its ability to create data models, Kyvos’ distributed scale-out architecture combines domain-level data products into an AI-powered semantic layer while enabling seamless query execution on raw and aggregated data. It empowers organizations with limitless scalability to store as much data as needed and deliver consistently high-speed querying without compromising on performance even for higher concurrency, data volumes and complexities.

The platform supports parallel processing to scale up and down on demand and eliminate the need to adjust for over- or under-provisioning. Additionally, Kyvos’ three-tiered security model with granular-level role-based access control prevents the information from falling into the wrong hands. The security of the row and column levels further protects and keeps information safe.

Kyvos’ GenAI-powered semantic layer plays the role of the data product layer in an organization’s data architecture. The platform can give the best of both worlds, it can combine domain-level data products to provide a single representation of enterprise-wide data without actually storing data in one central repository. It allows the blending of data products from different departments. Therefore, organizations can perform inter-domain analysis without worrying about scalability, accessibility or a single point of failure.

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