Implementation - Consolidated MDM
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Harmonize master data from multiple sources into a single view
Consolidated MDM, also known as Analytical MDM, focuses on integrating and harmonizing master data from multiple sources into a single, authoritative view. This style aims to create a unified and consistent representation of master data across the enterprise.
Data integration techniques such as data cleansing, data matching, and data merging are employed to ensure data quality and eliminate redundancies. Consolidated MDM provides a centralized data repository that serves as the single source of truth for master data.
This approach is particularly useful in organizations with diverse data sources and a need for accurate and consistent information across departments.
Benefits of consolidated MDM:
Data quality
Improved data quality and accuracy through data cleansing and standardization processes.
Data governance
Enhanced data governance and control with a single source of truth.
Data integration
Simplified data integration and interoperability across systems.
Decision making
Facilitates better decision-making and reporting with consistent data.
Challenges of consolidated MDM
Data modelling
Traditionally Consolidated MDM has necessitated that data teams model their data upfront in order to determine the data model the MDM system must follow. This data model is then applied rigidly without the flexibility to evolve or adapt as more data is ingested.
Data governance
Establishing robust data governance practices can be a significant challenge in consolidated MDM. It requires defining data standards, policies, and ownership roles across the organization. Ensuring compliance with data governance principles and maintaining data quality over time can be complex.
Data integration complexity
Consolidated MDM requires integrating data from various disparate sources, which can be challenging due to differences in data formats, structures, and quality. Data integration efforts may involve complex ETL (Extract, Transform, Load) processes and require data cleansing and harmonization.
Reliance on technical teams
Consolidated MDM centralizes control within a single master data hub, and it is usually the responsibility of architects, data engineers, and stewards to manage and prepare the data for use. This places MDM squarely in the IT sphere, making it less accessible to domain experts and business users and forcing IT to support the system and user requests.
Use case example...
A multinational corporation with multiple subsidiaries and disparate data sources aims to create a unified customer view across its business units. By implementing consolidated MDM, they can integrate and cleanse customer data from various systems, ensuring accurate and up-to-date information across the organization.
How CluedIn solves consolidated MDM challenges
CluedIn addresses common Consolidated MDM challenges in the following ways:
Easy Ingestion
By easily ingesting data from potentially thousands of different sources directly into the platform.
No up-front modelling
By eradicating the need to model your data up front. As a Graph-based system, CluedIn allows the relationships between the data to emerge as a natural data model. This model will evolve over time as more data is ingested.
Automated data enhancement
Automated matching, merging, cleaning, and enrichment of the data.
Automated rule-building
Automated rule-building, including AI-based rules to minimize repetitive manual data fixing. Poor quality data is readily accepted by the system and will either be automatically fixed using existing rules, or in the case of a new issue it will be flagged as requiring a manual fix which will then be used to create a new rule.
The end result is that CluedIn becomes the central location for trusted data, and is able to accurately report on that data either within the CluedIn platform or stream it out in near real-time to Business Intelligence platforms such as Power BI.
View our MDM Implementation styles white paper for the complete overview
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Inbuilt and high compression for low storage footprint
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Inbuilt and high compression for low storage footprint
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