Master Data Management

What is master and reference data management solutions

Today, enterprise data is distributed across versatile applications and systems (such as ERPs and CRMs). As a result, there is a high probability that data across different departments can easily become fragmented, redundant, and often out of date. Your company may find it very difficult to answer even the most rudimentary, but important inquiries about any performance indicators in such a scenario. For example, master data management might answer basic business questions, such as “What is the profit margin on products/services?” and “What is the most profitable business unit?”

Reference data management

It is the data used to classify or classify other data that is usually static or changes slowly over time. Examples of reference data include: (Units of measurement, country codes, company codes, calendar structure and restrictions) The organization and management of reference data is fundamental to ensuring its quality and therefore fitness for purpose. All aspects of an organization, operational and analytical, depend heavily on the quality of the organization’s reference data. Without consistency across a business process or applications, for example, similar things can be described in very different ways

The benefits of Master Data Management System

Unified and consistent information across many networks

Better understand your customers

Integrated view of business data assets

Improved data reliability and confidence

The methodology of master data management

Assessment and Planning

• Evaluate existing data sources, structures, and quality
  • Identify key stakeholders and their requirements
  • Define business goals and objectives for MDM implementation

Data Profiling and Analysis

• Analyze the quality, completeness, and consistency of existing data • Identify duplicates, inconsistencies, and other data quality issues

Data Standardization and Cleansing

• Standardize data formats, units, and values to ensure consistency
  • Cleanse data by removing duplicates, errors, and inconsistencies

Master Data Modeling

• Implement data governance policies to manage access, data ownership, and stewardship
  • Ensure compliance with regulatory requirements and industry standards

Integration and Data Consolidation

• Integrate master data from various sources such as databases, applications, and external systems
  • Consolidate data to create a unified and centralized view of master data

Data Quality Management

• Establish data quality metrics and KPIs
  • Implement data validation, enrichment, and monitoring processes

Data Synchronization

• Implement mechanisms for real-time or batch data synchronization across systems
  • Ensure that changes made in one system are reflected in others

Metadata Management

• Manage metadata to provide context and meaning to master data elements
  • Document data lineage, relationships, and business rules

Implementation and Deployment

• Select and deploy appropriate MDM tools and technologies
  • Customize the MDM solution based on organizational requirements