NashTech Insights

Data Mesh – What it bring to business ?

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Data Mesh is not a framework but is a paradigm for designing and operating data architectures. This paradigm helps in better manageability and scalability of the data with in your enterprise. Looking at the data solutions maturity in large organisations they all have centralised data already in place. But still, if there is need for new analysis, enriching the existing reports or a new AI/ML model the turn around time is very high. This is because most of the time the centralised Data team have no idea about what kind of data is needed and its quality as they are not the one who are sourcing it.

The common problems associated with the centralized data approach are:

  1. Missing Business context: The foremost problem is that the centralized team does not have the business context of the Data they are managing, so in case of new requirement mostly they do not have any idea about the data that will serve the need and how to procure it.
  2. Bottlenecks and Delays: In a centralized data architecture, a single team or department is responsible for managing and processing all data requests causing bottlenecks and delays in data access and processing, especially as the volume and complexity of data increase. Teams will have to wait for the centralized team to fulfill their data needs, which leads to slower decision-making and reduced agility.
  3. Lack of Agility and Innovation: Centralized data solutions often struggle to keep up with the rapidly evolving data needs of different business units or teams. Changes and customizations to the centralized system can be slow and cumbersome, limiting the ability of teams to innovate and experiment with new data-driven initiatives. This can hinder the organization’s agility and competitiveness in the market.
  4. Data Ownership and Accountability: In a centralized data model, it can be challenging to assign clear ownership and accountability for specific data domains. When a single team manages all data, it becomes difficult to establish responsibility for data quality, governance, and compliance. This lack of ownership can result in lower data quality, limited transparency, and challenges in enforcing data governance practices.
  5. Scalability and Performance Limitations: Centralized data architectures may face scalability and performance limitations as the volume and complexity of data increase. Scaling a centralized system to handle large amounts of data can be expensive and technically challenging. This can lead to slower data processing, increased latency, and reduced system performance.
  6. Single Point of Failure: Centralized data solutions are susceptible to a single point of failure. If the centralized system experiences an outage or a technical issue, it can disrupt data access and impact the entire organization. This vulnerability can have severe consequences, particularly when real-time data is crucial for critical business operations or decision-making.
  7. Data Security and Compliance Risks: Concentrating all data in a single system increases the potential impact of a data breach or security incident. Centralized data solutions can become attractive targets for malicious actors, posing significant security risks. Additionally, compliance with data protection regulations, such as GDPR or CCPA, can be more challenging in centralized architectures due to the need for centralized controls and data access management.
  8. Limited Customization and Flexibility: Centralized data solutions often have limited customization options to cater to the unique needs and preferences of different teams or business units. This lack of flexibility can hinder the adoption of specialized tools, technologies, or data processing frameworks that might be more suitable for specific domains. It may also limit the ability to integrate with external systems or leverage cloud-native capabilities.

Data Mesh tries to resolve the above problems by defining the below principles

Principles Explained

  1. Principle of Domain Ownership; Domain team has the most understanding of their data so they should own it – The principle of domain ownership in Data Mesh emphasizes distributing data ownership among different domains or business units within an organization. Each domain takes responsibility for its own data, including quality, governance, and lifecycle management.
  2. Principle of Data as a Product : Data is a valuable Asset and should be treated and managed as a product – Instead of viewing data as a byproduct or a technical artifact, considers data as a product with its own lifecycle, stakeholders, and value proposition. This shift in mindset encourages organizations to invest in data quality, usability, and accessibility. Data should be managed and delivered in a way that aligns with product management practices like defining clear requirements, setting goals, establishing metrics, and understanding the needs of data consumers.
  3. Principle of Self Service Data Platform : An able Platform which can be accessed and used by all – Data Platform should empower users within an organization to independently access, manage, and analyze data without heavy reliance on centralized data teams. It should provide user-friendly tools and interfaces that enable users to query, transform, and analyze data without requiring extensive technical expertise.
  4. Principle of Federated Computational Governance : Domain team owns the governance : Instead of relying on a centralized governance team, federated computational governance delegates decision-making authority to individual domain teams. Each domain team has the autonomy to define and enforce their own governance policies and practices, tailored to their specific data domain and requirements.

Benefits of Data Mesh

  1. Data Democratization: Data Mesh promotes the idea of decentralized data ownership and access. It enables different business units or teams to have control over their own data domains, allowing them to make independent decisions about how to structure, manage, and share their data. This decentralization encourages data democratization, empowering teams to use and analyze data more effectively.
  2. Scalability and Agility: By distributing data ownership and management, Data Mesh enables organizations to scale their data infrastructure more effectively. Instead of relying on a centralized data team to handle all data-related tasks, individual teams can take ownership of their data and develop specialized data products. This decentralization allows for faster decision-making, increased agility, and improved time-to-market for new data-driven initiatives.
  3. Data Quality and Reliability: With Data Mesh, teams are responsible for the quality and reliability of their own data domains. This encourages a culture of data ownership and accountability. By having dedicated data product owners who understand the specific needs of their domain, organizations can ensure better data quality, improved data governance, and more reliable insights.
  4. Improved Collaboration and Interoperability: Data Mesh promotes the use of well-defined domain boundaries and standardized interfaces between data domains. This approach facilitates collaboration between teams and encourages the development of interoperable data products. Teams can easily discover, access, and consume data from other domains, fostering cross-functional collaboration and enabling the creation of new data-driven applications and services.
  5. Flexibility and Innovation: Data Mesh allows teams to choose the most suitable tools, technologies, and data storage solutions for their specific needs. This flexibility enables teams to experiment with new technologies, adopt innovative data processing frameworks, and leverage cloud-native architectures. It promotes a culture of innovation and exploration, helping businesses stay competitive and adapt to evolving data requirements.
  6. Cost Optimization: By decentralizing data ownership, Data Mesh can lead to cost optimization in terms of infrastructure and human resources. Instead of maintaining a large centralized data infrastructure, teams can leverage cloud services, distributed systems, and scalable storage solutions tailored to their needs. This approach allows businesses to allocate resources more efficiently, reduce duplication of efforts, and optimize costs associated with data management.

Overall, Data Mesh brings a paradigm shift in how organizations approach data management, promoting autonomy, collaboration, and scalability. It empowers teams to be more data-driven, fosters innovation, and helps businesses unlock the full potential of their data assets.



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