Master data management (MDM) is a comprehensive method of enabling an enterprise to link all of its critical data to one file, called a master file, that provides a common point of reference. When properly done, master data management streamlines data sharing among personnel and departments. This increases the data’s overall quality, consistency, and use.
There are four main types of master data management:
1. Customer Data Integration
Customer data integration (CDI) consolidates all of an organization’s customer data into one, overarching master file. This provides sales, marketing, and service groups with accurate, timely information on prospects and customers. CDI enables different departments to access a uniform view of customer interactions across all service channels.
The master customer file contains essential information like name, address, phone number, order history, credit status, and more. CDI systems pull this data from multiple source systems across the enterprise, link records that relate to the same customer, and create a golden record for each individual. This provides a holistic view of every customer and fuels personalized, relevant messaging.
CDI delivers a number of important benefits, including:
- More satisfied customers due to consistent, unified messaging and service
- Higher sales conversions as a result of targeted cross-sell/upsell campaigns
- Lower marketing costs from reduced mail and email duplication
- Higher productivity since personnel spend less time hunting for information
- Lower IT expenses thanks to retired legacy systems
CDI is a mature area of MDM with proven solutions available from leading vendors like IBM, Oracle, and more. It is a foundational MDM practice. Most enterprises that want to connect siloed data using MDM start with customer data.
Key Capabilities of Customer Data Integration
CDI solutions provide a wide range of functionality to support master data management for customer information. Here are some of the key capabilities:
- Data standardization – Normalizes data from different systems into consistent formats
- Probabilistic matching – Identifies records that relate to the same customer entity using advanced algorithms
- Hierarchy management – Maintains relationships between customer entities like households
- Golden record creation – Merges disparate records into unified customer profiles
- Data enrichment – Appends additional demographic info to flesh out profiles
- Survivorship – Establishes rules for which data values prevail when multiple values exist
- Security – Controls user access and permissions
With these functions, CDI solutions provide the holistic, 360-degree views of customers that organizations need to provide consistent interactions and meet rising CX expectations.
2. Product Information Management
Product information management (PIM) focuses on centralizing, cleaning, and sharing product data across an enterprise. This includes item attributes like descriptions, specs, pricing, inventory levels, manufacturing data, and more.
PIM creates a “single source of truth” for product information. This master data is distributed to downstream systems like ecommerce platforms, sales portals, supply chain applications, and print catalogs. PIM eliminates inconsistencies and errors that result when product data is scattered across multiple databases.
Key benefits provided by product information management include:
- Accelerated time-to-market for new products
- Improved supply chain efficiency
- Reduced product returns and recalls
- Higher online product findability
- Cross-sell recommendations based on master product data
Due to its clear ROI, PIM adoption is growing steadily. It helps companies that sell direct-to-consumer and through distribution channels optimize product content for use by multiple groups.
Core Capabilities of Product Information Management
Robust PIM solutions come equipped with an array of features to fully manage product data, such as:
- Centralized repository – Stores all product data in one master database
- Data syndication – Shares product data to downstream systems in optimal formats
- Workflow management – Automates product data processes for efficiency
- Enrichment – Pulls in supplemental data like images to enhance records
- Publishing – Generates print catalogs, web pages, etc. from master data
- Lifecycle management – Manages data for products throughout their lifespans
- Supplier portals – Enables external supplier collaboration
PIM systems with these features provide an automated approach to managing product information on an ongoing basis across global product lines.
3. Supplier Data Integration
Supplier data integration compiles master supplier records from across procurement systems, ERP platforms, and other sources. This provides a holistic view of supplier performance, capabilities, transactions, compliance, and risk.
Master supplier data fuels supply chain optimization by enabling sourcing teams to make decisions based on the total supplier relationship – not just specific contracts. Additional benefits include:
- New revenue opportunities from improved supplier discovery
- Better supplier lifecycle management
- Lower supply risk through increased supplier transparency
- Higher procurement productivity and efficiency
While still an emerging discipline, supplier MDM is growing in popularity as firms recognize its potential to generate measurable cost savings and supply chain improvements.
Key Supplier MDM Capabilities
Supplier MDM solutions come with functionality needed to gain control over supplier master data, including:
- Multidomain MDM – Manages supplier data across all domains like SAP, Oracle, Salesforce etc.
- Identity matching – Links records from disparate systems referring to the same supplier
- Relationship mapping – Identifies corporate hierarchies and relationships
- Risk profiling – Assesses supplier risk levels based on master records
- Data validation – Improves data quality through input validation
- Workflow automation – Streamlines processes like supplier onboarding
With tight control over all supplier data, organizations gain the visibility needed to identify opportunities for supplier consolidation, rationalization, and more.
4. Asset Information Management
Asset information management (AIM) centralizes master data about equipment, vehicles, facilities, infrastructure, and other physical assets. This could include data like cost, deployment location, maintenance records, warranty status, and so on.
By consolidating asset data into one version of truth, AIM delivers benefits such as:
- Improved asset availability and uptime
- Better capital planning and cost control
- Lower asset procurement costs
- Reduced risk of accidents and failures
- Increased asset utilization and ROI
Industries like manufacturing, healthcare, and oil and gas use AIM to control critical data about complex physical assets that require intensive maintenance and generate massive amounts of data over long lifecycles.
Key AIM Functions
AIM solutions come equipped with a standard set of capabilities for optimal physical asset data management:
- Asset register – Central repository for all asset records
- preventative maintenance – Enables scheduled, preventative maintenance
- Location tracking – Identifies asset deployment sites
- Repair history – Stores all maintenance and repair records
- Linked documents – Connects maintenance manuals, warranties, etc.
- Asset lifecycle management – Manages data across asset lifecycles
- Mobile access – Enables updates from the field
With integrated, complete asset data, organizations can implement predictive maintenance programs and cut costs by eliminating unnecessary repairs and extending asset lifetimes.
Conclusion
Master data management focuses on aggregating critical business data from across the enterprise into “golden records” that providesingle points of reference. The four main types of MDM are:
- Customer data integration
- Product information management
- Supplier data integration
- Asset information management
Each MDM discipline consolidates data from a different business domain into complete, consistent master datasets. While philosophically similar, MDM types leverage different techniques tailored to their data types.
By eliminating data silos and discrepancies, MDM generates tangible business value like improved operational efficiency, lower costs, easy reporting and analytics, and data-driven decision making. As data volumes and diversity continue growing, MDM will become increasingly critical for day-to-day business operations and strategic optimization.
