How can you integrate master data management into data architecture?

1 Define the scope and domains of MDM

The first step is to define the scope and domains of MDM, which are the categories of data that are critical for the business and need to be managed centrally. For example, common domains include customer, product, supplier, employee, and location. You need to identify the data entities, attributes, relationships, and rules that belong to each domain, as well as the sources, consumers, and owners of the data. You also need to assess the current state of the data quality, availability, and integration across the domains.

2 Choose the MDM architecture style

The next step is to choose the MDM architecture style, which is the way the master data is stored, accessed, and synchronized across the systems and domains. There are four main styles: registry, consolidated, coexistence, and hub-and-spoke. Each style has its own advantages and disadvantages, depending on the data volume, complexity, diversity, and latency requirements. You need to evaluate the trade-offs and select the style that best suits your business needs and data architecture goals.

3 Design the MDM data model and structure

The third step is to design the MDM data model and structure, which is the representation and organization of the master data in a logical and physical format. You need to define the data elements, types, formats, validations, hierarchies, and mappings that will form the master data schema. You also need to design the data storage and distribution mechanisms, such as databases, files, or APIs, that will enable the master data to be stored and accessed efficiently and securely.

4 Implement the MDM data flow and integration

The fourth step is to implement the MDM data flow and integration, which is the process of moving and transforming the master data between the sources, the MDM system, and the consumers. You need to establish the data ingestion, cleansing, matching, merging, enrichment, and publishing pipelines that will ensure the master data is accurate, complete, and consistent. You also need to implement the data synchronization, change management, and conflict resolution strategies that will maintain the master data integrity and currency.

5 Apply the MDM data governance and quality

The fifth step is to apply the MDM data governance and quality, which is the set of policies, standards, roles, and procedures that will govern the creation, maintenance, and usage of the master data. You need to define the data ownership, stewardship, security, privacy, and compliance rules that will assign the responsibilities and accountabilities for the master data. You also need to define the data quality metrics, indicators, thresholds, and actions that will measure and improve the master data quality and reliability.

6 Monitor and optimize the MDM performance and value

The final step is to monitor and optimize the MDM performance and value, which is the process of tracking and enhancing the efficiency and effectiveness of the MDM system and the master data. You need to collect and analyze the data on the MDM system performance, such as throughput, latency, availability, and scalability. You also need to collect and analyze the data on the master data value, such as usage, impact, and satisfaction. You need to use the insights and feedback to identify and implement the improvements and innovations that will increase the MDM value and benefits.