Data Mesh Future Of The Data Architecture & Management
Aniket Mhala is a Strategic & Achievement oriented passionate Leader and he offered 27 years of rich & multi functional experience with strong technology & business development skills and broad multinational experience in delivering IT solutions across the enterprise by leveraging deep Technology, Innovation, IT advisory and hands on experience.
As you know that change is constant and Data is changing and growing constantly in terms of volume, variety and its velocity. We all know the famous line, 'Data is Oil'. It means that data is the most important aspect of every business. However, organizations are finding it difficult to manage the huge volume of enterprise data using centralized approaches such as centralized Data Lake, Data Warehouse or Data Lake House with centralized Data Management teams.
CXO has typical challenges such as data silos, lack of data autonomy, data security, data governance & data latecy related to the enterprise data using centralized data management and centralized data teams. Data Mesh is one of the options to address the problems of centralized enterprise data management.
Data Mesh is a way of organizing and managing data in large, complex organizations. It is an architectural pattern. It is based on the idea of breaking down monolithic data systems into smaller, independent services, each with its own data domain and ownership. This allows for greater scalability, flexibility, and autonomy for teams working with data, and can lead to faster innovation and decision-making. Business people can benefit from Data Mesh by having more control over their own data, faster access to the data they need, and the ability to make data-driven decisions more quickly.
"Business people can benefit from Data Mesh by having more control over their own data, faster access to the data they need, and the ability to make data-driven decisions more quickly"
The concept of Data Mesh was first introduced in a 2018 blog post by Sam Newman, a software architect and author. In the post, Newman argued that traditional data management approaches, which often involve centralized data systems and a monolithic architecture, were becoming increasingly difficult to manage and scale in large, complex organizations. He proposed the idea of Data Mesh as a new approach that would break down monolithic data systems into smaller, independent services, each with its own data domain and ownership.
Since then, the concept of Data Mesh has gained popularity in the software development community, with many organizations and companies experimenting with the approach. In December 2020, Zhamak Dehghani published an article on the Data Mesh Principle and its logical architecture. Some of the key ideas and principles behind Data Mesh include decentralizing data ownership and management, breaking down monolithic data systems, and creating smaller, independent data services that can be managed by a single team.
The Data Mesh concept is heavily influenced by the principles of Microservices architecture, in which a monolithic application is broken down into smaller, independently deployable services. Data Mesh is an extension of this concept to data management.
As Data Mesh is a relatively new concept, it's still evolving, and there is ongoing discussion and experimentation in the community around best practices and how to implement it effectively. As more organizations adopt Data Mesh, the history of it will continue to develop.
Implementing Data Mesh in an organization can be a complex process, but there are a few key steps that can help CxO and Data Strategy Team to operationalize the Data Mesh:
•Define data domains: The first step in implementing Data Mesh is to identify and define the different data domains within the organization. These domains should be based on the business functions and teams that use the data and should be small enough to be managed by a single team.
•Decentralize data ownership: Once the data domains have been defined, teams should be given ownership of their own data domains. This means that they are responsible for managing and using the data within their domains and making decisions about how to use it.
•Create data products:Each team should be responsible for creating data products that they can use to access and work with their data. These products should be designed to be simple, easy to use, and independent of other data products.
•Establish data governance: While Data Mesh is focused on decentralizing data ownership, it's important to establish data governance to ensure that data is being used correctly and comply with regulations.
•Establish communication and collaboration: With multiple teams owning their own data domains, it's important to establish clear communication and collaboration processes to ensure that teams can work together effectively.
•Continual improvement:Finally, its important to continuously monitor and improve the Data Mesh implementation. This can be done by monitoring data usage, performance, and feedback from teams and continuously making adjustments to the implementation accordingly.
Data Mesh can provide several benefits for large organizations, including:
•Scalability: By breaking down monolithic data systems into smaller independent services, Data Mesh allows for greater scalability and flexibility as the organization grows and evolves.
•Autonomy: Data Mesh allows teams to take ownership of their own data domains and make decisions about how to manage and use that data,leading to faster innovation and decision-making.
•Improved data access:By decentralizing data ownership and management, Data Mesh can make it easier and faster for teams to access the data they need to do their jobs.
•Reduced risk:With Data Mesh, teams are able to work with and manage their own data domains, reducing the risk of data breaches and other security issues.
•Better alignment:Data Mesh can help align business objectives with data objectives, allowing teams to make data driven decisions that support the overall goals of the organization.
•Faster innovation:By decentralizing data ownership and management, Data Mesh can make it easier for teams to experiment with new data technologies and approaches, leading to faster innovation and improved performance.
As a reminder, implementing Data Mesh requires a cultural shift in the organization, as it's not just a technical change, but also a change in the way of working. This change will take time and resources, but the benefits of Data Mesh can be significant for the organization to address the current and future needs of large enterprise data management.
As you know that change is constant and Data is changing and growing constantly in terms of volume, variety and its velocity. We all know the famous line, 'Data is Oil'. It means that data is the most important aspect of every business. However, organizations are finding it difficult to manage the huge volume of enterprise data using centralized approaches such as centralized Data Lake, Data Warehouse or Data Lake House with centralized Data Management teams.
CXO has typical challenges such as data silos, lack of data autonomy, data security, data governance & data latecy related to the enterprise data using centralized data management and centralized data teams. Data Mesh is one of the options to address the problems of centralized enterprise data management.
Data Mesh is a way of organizing and managing data in large, complex organizations. It is an architectural pattern. It is based on the idea of breaking down monolithic data systems into smaller, independent services, each with its own data domain and ownership. This allows for greater scalability, flexibility, and autonomy for teams working with data, and can lead to faster innovation and decision-making. Business people can benefit from Data Mesh by having more control over their own data, faster access to the data they need, and the ability to make data-driven decisions more quickly.
"Business people can benefit from Data Mesh by having more control over their own data, faster access to the data they need, and the ability to make data-driven decisions more quickly"
The concept of Data Mesh was first introduced in a 2018 blog post by Sam Newman, a software architect and author. In the post, Newman argued that traditional data management approaches, which often involve centralized data systems and a monolithic architecture, were becoming increasingly difficult to manage and scale in large, complex organizations. He proposed the idea of Data Mesh as a new approach that would break down monolithic data systems into smaller, independent services, each with its own data domain and ownership.
Since then, the concept of Data Mesh has gained popularity in the software development community, with many organizations and companies experimenting with the approach. In December 2020, Zhamak Dehghani published an article on the Data Mesh Principle and its logical architecture. Some of the key ideas and principles behind Data Mesh include decentralizing data ownership and management, breaking down monolithic data systems, and creating smaller, independent data services that can be managed by a single team.
The Data Mesh concept is heavily influenced by the principles of Microservices architecture, in which a monolithic application is broken down into smaller, independently deployable services. Data Mesh is an extension of this concept to data management.
As Data Mesh is a relatively new concept, it's still evolving, and there is ongoing discussion and experimentation in the community around best practices and how to implement it effectively. As more organizations adopt Data Mesh, the history of it will continue to develop.
Implementing Data Mesh in an organization can be a complex process, but there are a few key steps that can help CxO and Data Strategy Team to operationalize the Data Mesh:
•Define data domains: The first step in implementing Data Mesh is to identify and define the different data domains within the organization. These domains should be based on the business functions and teams that use the data and should be small enough to be managed by a single team.
•Decentralize data ownership: Once the data domains have been defined, teams should be given ownership of their own data domains. This means that they are responsible for managing and using the data within their domains and making decisions about how to use it.
•Create data products:Each team should be responsible for creating data products that they can use to access and work with their data. These products should be designed to be simple, easy to use, and independent of other data products.
•Establish data governance: While Data Mesh is focused on decentralizing data ownership, it's important to establish data governance to ensure that data is being used correctly and comply with regulations.
•Establish communication and collaboration: With multiple teams owning their own data domains, it's important to establish clear communication and collaboration processes to ensure that teams can work together effectively.
•Continual improvement:Finally, its important to continuously monitor and improve the Data Mesh implementation. This can be done by monitoring data usage, performance, and feedback from teams and continuously making adjustments to the implementation accordingly.
The Data Mesh concept is heavily influenced by the principles of Micro services architecture, in which a monolithic application is broken down into smaller, independently deployable services
Data Mesh can provide several benefits for large organizations, including:
•Scalability: By breaking down monolithic data systems into smaller independent services, Data Mesh allows for greater scalability and flexibility as the organization grows and evolves.
•Autonomy: Data Mesh allows teams to take ownership of their own data domains and make decisions about how to manage and use that data,leading to faster innovation and decision-making.
•Improved data access:By decentralizing data ownership and management, Data Mesh can make it easier and faster for teams to access the data they need to do their jobs.
•Reduced risk:With Data Mesh, teams are able to work with and manage their own data domains, reducing the risk of data breaches and other security issues.
•Better alignment:Data Mesh can help align business objectives with data objectives, allowing teams to make data driven decisions that support the overall goals of the organization.
•Faster innovation:By decentralizing data ownership and management, Data Mesh can make it easier for teams to experiment with new data technologies and approaches, leading to faster innovation and improved performance.
As a reminder, implementing Data Mesh requires a cultural shift in the organization, as it's not just a technical change, but also a change in the way of working. This change will take time and resources, but the benefits of Data Mesh can be significant for the organization to address the current and future needs of large enterprise data management.
