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Master Data Management

Overview​

Master data management (MDM) is the practice of collecting, organizing and maintaining accurate and reliable data that is used by an organization to identify and manage its core business entities, such as customers, products, and suppliers. MDM provides a unified master data service that delivers accurate, consistent, and complete master data across the enterprise and to business partners.

Turn data into insights through effective MDM​

With Skypoint AI, you can manage, centralize, organize, categorize, localize, synchronize, and enrich master data according to the business rules of the sales, marketing, and operational strategies of your company. This can help to improve the quality and accuracy of business decisions, reduce the risk of errors and inconsistencies, and support regulatory compliance. It can also help to improve efficiency and reduce the costs associated with managing multiple, disparate sources of data.

There are four types of data classifications as mentioned below:

  • Master data: Refers to the core, critical data that is essential to an organization's operations and is used as the basis for other business transactions and processes.
  • Relationship data: Refers to the data that describes the relationships between different tables in an organization's data.
  • Transaction and interaction data: Transaction data refers to the data that is generated by business transactions, such as sales or purchases. Interaction data refers to the data that is generated by interactions between an organization and its customers or other stakeholders.
  • Reference data: Refers to the data that is used to classify or categorize other data.

For MDM, it is important to first define master data; master data is the critical business data of an organization that includes the product, supplier, and customer information.

Resolve​

After the successful import of data in Dataflow, the Resolve (map and unify) step enables you to develop a unified view of your data. It involves breaking down data silos by linking tables across data sources. To define your data model, you work with tables and attributes. This section explains how to create, add, edit, and test those elements.

Skypoint AI Resolve identity resolution provides a quick and easy way to deduplicate, manage, and improve identity data for your business requirements. It is an essential part of Master Data Management (MDM) and is critical for organizations that rely on accurate and reliable data for their operations.

The importance of identity resolution is as follows:

  • Fraud and risk monitoring: Detect and prevent fraud through visible data patterns that identify real profiles and flag fake profiles.
  • Universal identifier: Map, match, deduplicate, merge, and optimize profile identity markers to create a universal identifier (SkypointID).
  • Visualize by group: Leverage AI/ML to map identities to households, companies, or groups, and automatically remove previous associations.
  • Quality and consistency: Use a persistent identifier (SkypointID) to ensure data quality and consistent operations across downstream applications.

The Graph-based entity resolution algorithms, including the utilization of Apache Spark and GraphFrames, are the most effective approach to unifying the data. This technique exhibits to unify the data based on a simple process. At the same time, the identity resolution engine maps the data to a machine-learning with a powerful knowledge graph at its center.

After the Resolve step, you can configure master data (profiles, enrichment, timelines) to gain more insights about your customers.