Implementation of Data Catalog and Data Mesh
By Troy Wyatt & James Buschkamp / May 05,2023
A leading financial institution, providing a wide range of services to its clients, such as investment banking, asset management, and consumer banking. As the organization grew, they faced challenges in managing and making sense of the vast amounts of data generated from its various departments and applications. To address this issue, they decided to implement a data catalog and data mesh to improve its data management and accessibility.
The main objective of this case study is to showcase the successfully implemented data catalog and data mesh to enhance data discovery, governance, and collaboration, ultimately enabling the organization to make data-driven decisions more effectively.
Assessment and Planning
Before implementing the data catalog and data mesh, Intuitive conducted a thorough assessment of the current data landscape to understand the existing pain points and identify opportunities for improvement. The assessment involved:
- Inventorying data sources and repositories
- Identifying data owners and stakeholders
- Defining key business use cases that require data access
Based on this assessment, the Intuitive team developed a roadmap for implementing the data catalog and data mesh, which involved the following steps:
Data Catalog Implementation
- Selecting a data catalog platform: Evaluation of several data catalog platforms based on their features, scalability, and ease of integration with existing systems. After a rigorous selection process, they chose a platform that best met their requirements.
- Data catalog setup and integration: The Project team worked closely with the data catalog vendor to set up the platform, integrate it with existing data sources, and establish a comprehensive data ingestion process.
- Metadata management: Created a metadata schema to define and categorize data assets, ensuring consistent and accurate descriptions for better data discoverability.
- Data governance and security: Implemented strict data governance policies and access controls to ensure that only authorized users could access sensitive data assets.
Data Mesh Implementation
- Establishing domain-driven data ownership: Divided the data landscape into domains based on business functions, such as investment banking, asset management, and consumer banking. Each domain had a designated data product owner responsible for managing and maintaining the domain's data assets.
- Defining data product APIs: Intuitive developed standard APIs for each data product, enabling seamless and secure data access across domains.
- Implementing a data mesh infrastructure: Built a distributed data infrastructure that connected all data products, allowing for efficient data sharing and collaboration among domains.
- Monitoring and observability: Established monitoring and observability tools to track data usage, identify data quality issues, and ensure the ongoing health of the data mesh.
Following the successful implementation of the data catalog and data mesh, the project achieved the following results:
- Improved data discovery: Users across the organization can now easily find and access relevant data assets using the data catalog's search and metadata capabilities.
- Enhanced data governance: The data catalog and data mesh enable and enforce consistent data governance policies and maintain compliance with industry regulations.
- Increased collaboration: The data mesh has facilitated collaboration between teams and departments by providing a seamless way to share and access data.
- Better decision-making: With improved data accessibility and quality, make more informed, data-driven decisions, driving better business outcomes.
The implementation of a data catalog and data mesh has allowed our customer to streamline their data management processes, leading to improved data discovery, governance, and collaboration. This case study demonstrates how large financial institutions can benefit from investing in modern data infrastructure to enable data-driven decision-making and foster a data-centric culture.