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Master Data Management (MDM)

Master data management (MDM) involves creating a single master record for each person, place, or thing in a business, from across internal and external data sources and applications. This information has been de-duplicated, reconciled and enriched, becoming a consistent, reliable source. Once created, this master data serves as a trusted view of business-critical data that can be managed and shared across the business to promote accurate reporting, reduce data errors, remove redundancy, and help workers make better-informed business decisions.

What is Master Data Management (MDM)? | Informatica India

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General Questions​

What is Master Data Management (MDM)?​

MDM is a comprehensive method used to manage an organization's critical data to ensure a single point of reference for the data. It involves the governance, policies, standards, and tools to manage and ensure the accuracy, consistency, and accountability of enterprise-wide data.

Why is MDM important for an organization?​

MDM ensures data consistency, accuracy, and integrity across the organization. It helps in making informed business decisions, improves operational efficiency, enhances customer experiences, and ensures compliance with regulations.

What are the key components of MDM?​

The key components of MDM include data integration, data quality management, data governance, data modeling, data storage, data security, and workflow management.

Explain the different types of master data​

The primary types of master data include customer data, product data, supplier data, and location data. These represent the critical entities within an organization and are often shared across various departments and systems.

Technical Questions​

What is a master data hub, and how does it function?​

A master data hub is a centralized repository where master data is stored and managed. It functions as a single source of truth, ensuring that all systems within an organization use consistent and accurate master data.

What are the common challenges faced in MDM implementations?​

Common challenges include data silos, data quality issues, lack of data governance, resistance to change, integration complexities, and ensuring data privacy and security.

What is data governance in the context of MDM?​

Data governance refers to the processes, policies, standards, and roles that ensure the effective and efficient use of information. In MDM, it involves defining ownership, accountability, and responsibilities for data assets, ensuring data quality, and maintaining data security and compliance.

How do you ensure data quality in MDM?​

Ensuring data quality in MDM involves implementing data profiling, data cleansing, data standardization, data enrichment, and continuous monitoring processes. It also requires setting up data quality metrics and governance policies.

Scenario-Based Questions​

Describe a situation where you had to implement MDM in an organization. What steps did you take?​

In implementing MDM, the steps typically include assessing the current state of data, defining MDM strategy and objectives, selecting the right MDM tools, designing data governance frameworks, integrating data from various sources, ensuring data quality, and continuous monitoring and improvement.

How would you handle resistance from stakeholders during an MDM implementation?​

Handling resistance involves clear communication about the benefits of MDM, involving stakeholders early in the process, providing training and support, demonstrating quick wins, and addressing concerns through regular feedback and adjustments.

What methods do you use to integrate data from multiple sources into the MDM system?​

Methods for integrating data include ETL (Extract, Transform, Load) processes, data mapping, data synchronization, API integration, and using middleware tools to ensure seamless data flow between systems.

How do you measure the success of an MDM initiative?​

The success of an MDM initiative can be measured using key performance indicators (KPIs) such as data accuracy rates, reduction in data duplication, improved decision-making processes, enhanced customer satisfaction, compliance with regulations, and overall return on investment (ROI).

Tools and Technologies​

What MDM tools and technologies are you familiar with?​

Common MDM tools include Informatica MDM, IBM InfoSphere MDM, SAP Master Data Governance, Oracle MDM, Microsoft Master Data Services, and Talend MDM.

How do you select the right MDM tool for an organization?​

Selecting the right MDM tool involves assessing the organization's specific needs, evaluating the tool's features and capabilities, considering scalability and flexibility, checking for compatibility with existing systems, and ensuring vendor support and cost-effectiveness.

Explain the role of machine learning and AI in MDM​

Machine learning and AI can enhance MDM by automating data cleansing, matching, and deduplication processes. They can also help in predictive analytics, anomaly detection, and improving data quality through intelligent recommendations and insights.

Tools​

  • SAP
  • Microsoft 365 (Dynamics 365)
  • Informatica