What is Master Data Management

To navigate and develop omni-channel businesses, consistent insight into the data produced by the business is of strong importance

Insight in omnichannel trading data includes a persistent and sufficient abstraction of the operating model into the logical model of the data generated by the business 

In fashion retail, data is more complex than in general retailing since products are a lot more seasonal, unique to a brand or chain, support short-lived production, customers are typically fueled by desire, and multi-channel retailing requires both adaptation and consistency across various channels.

Traditionally ERP systems have addressed business data as static (master data) or dynamic (transactions). Therefore, master data is concerned with permanency and appropriate data representations are derived from these transactions. Business processes contribute to existing master data.

As these systems are loosely connected to the ERP, there is a many risk of loss of meaningfulness between systems, the quality and relevance of the MDM / PIM is therefore paramount to establish coherence between products, business and systems 

What is Master Data?

Master Data is a single version for a given attribute of a business entity. It is authoritative and accurate, clean, consistent, accessible, available, and integrated across the enterprise .

Master Data is all the data you need to know about your customers, products or services in order to run your business effectively. The problem is there are many kinds of Master Data — customer information; product specifications; order details — so it's very common for organizations to have multiple copies stored in different locations across their systems.

How is Master Data managed?

Master data management (MDM) softwares tools are used by businesses to manage master data. At its outset MDM Management & Governance includes 11 dimensions, namely 

  • Data Strategy
  • A policy level direction on why we are capturing data, how and at what control points it is being captured, at which point a data set is considered “Master” vs “Reference” or a “Shared Master” 
  • Data Architecture & Modeling
  • Defining a reusable data landscape and architecture components to optimally handle the MDM processes and technology touch points
  • Meta Data
  • Layering system generated and human defined registries of information facets being generated based on usage, audit logs, certifications, entity specific requirements. 
  • Data Quality
  • Definition what constitutes a golden record, automated rules, policies for identifying, isolating and deboarding a poor quality information at entity levels
  • Data Operations
  • Process statement of man and machine touch points, systemic and form driven data capture mechanisms, rule based enrichment, data ingestion frameworks and templates, data de-boarding frameworks and processes 
  • Content Management
  • Establishing version control, audit cycles, standard formats based on global agencies and legal frameworks, provisioning access controls, workflows etc
  • Data Interoperability
  • Derived from data consumption touchpoints, focused approach to making sure master data is accessible in formats required by human and systemic consumers for both data egress and ingress
  • Data Security
  • Ensuring stored data is secure in states of rest and consumption, by enforcing encrypted API’s, SSO, 2 factor authentication, volume monitoring, random and planned audit etc
  • Data Insights & Analytics
  • A solution centric approach of leveraging master data to derive meaningful, business insights, which is ultimately the purpose of MDM systems 

Why is MDM important?

Embarking on digital transformation of  MDM with cloud and data strategies can drive positive outcomes for data professionals and business users, including the ability to:

  • Understand customer needs and patterns in seconds, 
  • using a workflow-based processing engine to match and relate data to deliver a comprehensive view of customers, purchase patterns, seasonality of demand generation and other business-critical insights
  • Improve self-service empowerment for business leaders
  • By facilitating access to trustworthy and actionable data without reliance on IT, business leaders can be proactive and quick to react to market changes and consumer expectations
  • Maximize platform extensibility
  • By creating benchmarked MDM standards and systems, businesses can extend and scale operations leverage information from either a legacy databases (M&A) or bespoke and new applications (Digital Transformation Investments, New Market Launch etc) 

How do enterprises use MDM products and services?

Master data includes information shared among multiple applications and systems, including:

  • Customer data (e.g., Legal name, address, etc.)
  • Product data (e.g., product number, price)
  • Transaction-level data (e.g., order number)
  • Party Data (e.g., Resellers, Retailers, Suppliers, Partners etc.)
  • People Data (e.g., Employees, Customers, Stakeholders, Users, etc.)

Reference data is information an enterprise uses to look up values from other data—such as countries or currencies—to fill fields on forms or reports. Reference attributes are stored centrally and linked to other systems via standard identifiers such as country codes or currency symbols; however, references can be maintained locally for local use only within an application program interface (API).

Organizations that take their data management seriously typically adopt an MDM system.

In the current business environment, companies often don’t have a precise overview of customers, products, suppliers, inventory or employees. Whenever companies add new enterprise applications to “manage” data, they contribute to increased complexity. As a result, the concept of MDM – creating a single, unified view of key source data in an organization – is growing in importance.

This is because MDM can:

  • Be used to support data governance, which involves identifying and documenting core business processes within your organization, along with associated activities and metrics. Data governance also involves providing guidelines for accessing and sharing data throughout the enterprise.
  • Ensure your organization’s data is of high quality by ensuring each element has been properly defined, categorized and referenced correctly so as not to cause confusion when working with it (e.g., if you have two people working on a project looking at different sources of information—and neither knows which is correct).
  • Facilitate integration between business partners or departments by connecting previously siloed applications into one centralized source where they can share information seamlessly without repeating themselves in order to get what they need from another department's system.


​​MDM is a complex topic, and requires a combination of both strategic components (organization & governance) and highly Technology driven activities (rules for master data items on field level, control points to achieve completeness & uniqueness of MD). This requires the right mix in terms of MD expertise, tool capability and business-process knowledge. At Supplysail, we have team of 20+ people who have in depth experience and expertise in transforming MDM architecture from groundup 

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