What Is Master Data Management? How It Declutters Data

Data is a competitive advantage – but only when you can access it.

Imagine your company generates product, customer, and supplier data every day. When this data is incomplete, inaccurate, or inconsistent, you have compartmental data silos and can’t make data-driven decisions. Failing to turn data into decisions means your relationship with competitive advantage and growth will fade.

Master data comes from modern companies that generate constant data streams for business entities such as employees, customers, products, and cost centers. These data streams often include business information crucial for a contextual understanding of transactional and analytical operations. Unfortunately, you can’t leverage this data for fact-based decision-making when it’s scattered across spreadsheets, physical media, and applications.

So, how do you manage master data for competitive advantage? Enter master data management (MDM) software.

MDM is both a discipline and an infrastructure. It uses data governance models to create a trusted view of data as a discipline. As an infrastructure, MDM focuses on automating how organizations share, govern, and manage critical data across business lines. The end goal is to support business decisions with an authoritative data source.

Master data management vs. metadata management vs. product information management

Master data management is a technology-enabled discipline. It brings together business and information technology (IT) teams to ensure uniformity, accuracy, and critical data consistency. The goal of master data management is to create accurate master data assets for core entities, including suppliers, customers, prospects, and hierarchies.

Metadata (structured reference for data attributes) management refers to processes, systems, and rules that manage metadata for improving information accessibility. Metadata management plays a vital role in increasing the ease of data discovery, reducing data management costs, and facilitating faster data integration.

Product information management (PIM) is a business-led solution. It refers to the process of collecting, managing, and enriching product information. PIM is usually a subset of MDM and creates an SSOT for product information. Marketers, e-commerce managers, and data governance teams frequently use PIM.

Importance of master data management

Multiple information systems create different data views. The lack of a standard format across systems prevents operational employees, business executives, and data analysts from having a reliable data view. MDM addresses multiple data inaccuracy challenges by creating a trusted source of unified data.

MDM systems also enable organizations to solve issues like:

• Data replication
• Duplicate data entries
• Different name usage
• Data not updated across systems
• Distributed data across multiple systems
• Disjointed customer experiences because of segmented data
• Manual data entry errors, including incomplete data fields, miskeyed entries, and transposing characters

Master data management framework

A master data management framework is at the heart of an effective master data management strategy. This framework outlines disciplines and values that guide master data management. These disciplines ensure the accuracy and consistency of the shared master data. The elements of an MDM framework are as follows.

Governance

Master data governance involves creating rules, executing them, and resolving violations of these rules. The purpose of master data governance is to outline a set of core attributes for defining master data and maintaining data consistency. Cross-functional teams use governance rules to specify operational processes throughout the data lifecycle, from master data creation to data disposal.

Suppose your organization buys materials from suppliers, builds products, and sells them to customers. Any discrepancy in your master data will have a ripple effect on many business areas. Inaccurate data may impact order-to-cash or record-to-report processes in such cases. That’s why enabling data governance is key to process efficiency and data accuracy.

There is no one-size-fits-all framework because every organization has varying data governance needs. However, adding these critical elements ensures a smoother data governance journey: transparency, data ownership, change management, compliance, maintenance, authority, auditability, accountability, standardization, data stewardship, and education.

• Policies: Refer to internal and external regulations to manage data. These policies usually cover data governance aspects such as risk management, quality management, retention, deletion, privacy, and data protection.
  
  For example, a policy may separate duties related to uploading and approving customer data.
• Rules: Define how to execute data governance policies. A policy fulfillment may require you to follow one or more mandates.
  
  For example, a customer data policy may require you to follow multiple rules:
  
  
  • Getting customer consent
  • Creating customer data entry
  • Approving customer data
  • Obtaining marketing consent for using data for marketing automation
• Catalog: This involves discovery and documentation of master data domains across applications, data warehouses, and data lakes. Cataloging is crucial for verifying and ensuring the accuracy and consistency of master data across sources.
  
  For example, a customer master data catalog is crucial for gaining insights, finding areas of improvement, and simplifying compliance.
• Process mapping: Show how the data flows between different sources. Process mapping helps you understand how data is used, the need for embedding rules, and compliance risk exposure.
• People: Identify the individuals involved in MDM activities throughout the organization. People critical to the MDM success include:
   
  • Subject matter experts: Define master data standards, levels, and types of quality thresholds
  • Data stewards: Prevent data quality issues from affecting master data domains
  • IT team: Manages database architecture, business processes, and applications
  • Legal and security team: Ensures data privacy and protection
• Workflow: Enable and encourage key people to collaborate. Master data governance workflows usually include steps for data creation requests, routing requests to data stewards, approval, go-live activation, and distribution.
• Metrics: Create guidelines for measuring and managing master data. Common examples include the accuracy of master data, duplicate records, and encrypted personal data attributes. These metrics help businesses to manage technical data, mitigate risks, and improve business performance.

Measurement

Master data measurement involves setting metrics and key performance indicators (KPIs) for measuring data quality and continuous improvement goals. These metrics and KPIs are essential for ensuring customer satisfaction and reducing operating costs.

Common examples of KPIs are:

• Data record error rate
• Cycle time
• Percentage of duplicate data
• The total volume of account setups
• Total MDM expense
• The number of MDM employees per thousand data records

People

People are at the heart of any massive transformation. Having the right people makes it easier for you to implement and support the MDM initiative.

Here’s a list of key people who ensure MDM project success:

• Executive champion: Stewards the initiative and is completely bought in on it
• Stakeholders: Represent different business units
• Data governance team: Has clearly defined roles and tasks
• MDM consultants: Helps the team strategize, implement, and manage changes

Process

Processes act as guidelines for master data management and help teams focus on areas of improvement. Key strategies that make an MDM project successful are:

• Data consolidation refers to the process of data acquisition from multiple sources, including enterprise resource planning (ERP), product lifecycle management (PLM), and customer relationship management (CRM) systems.
  
  Data consolidation is key to centralizing the master data. Migration of data usually depends on different techniques, such as simple object access protocol (SOAP), representational state transfer (REST), Java message service (JMS), global data synchronization network (GDSN), and manual import. Data is validated, normalized, and classified during the extract, transform, and load (ETL) process.
• Data federation and enhancement is a software process that collects data from multiple sources across business functions and creates a single view of master data. The enhancement function uses reference data to verify the validity of the collected data and create a complete entity. This entire process may require cross-departmental collaboration workflows, depending on the information source.
  
  Suppose the master data contains product records created by multiple teams. There must be an appropriate governance model or approval workflow to ensure proper data enhancement in such a scenario.
• Data propagation refers to consolidated and enhanced master data distribution among systems that might need it. This distribution ensures that every business system has the same accurate information.

Technology

Using the right technology is key to efficient record linking, data model creation, and master data view synchronization. Some key technology components essential for MDM success are:

• MDM hub: Aggregates source system data, stores it in database, and synchronizes this data with different transactional systems.

• Data integration tool: Synchronizes data into the MDM hub and across the system landscape
• Data quality tool: Validates and maintains data accuracy. An efficient data quality tool helps you find and fight bad data.

Master data management software capabilities

MDM helps businesses make better decisions with integrous, accurate, and visible data. An MDM solution’s core capabilities that enable the best decision-making possible are:

Data profiling

Data profiling refers to the analysis of data sources for discovering data quality issues and risks. The data profiling process involves six activities that deal with:

1. Collection of dataset characteristics: Involves collecting descriptive statistics such as mean, minimum, maximum, percentile, and frequency
2. Collection of data types: Involves collecting data length and recurring patterns
3. Data tagging: Refers to data organization with non-hierarchical keywords or terms
4. Data quality evaluation: Measures data features against defined standards
5. Metadata collection: The collection and assessment of metadata accuracy
6. Distribution identification: Identifies key candidates, embedded value dependencies, functional dependencies, and foreign key candidates

Data matching and linking

Data matching and linking are crucial for identifying and resolving duplicate data records and variations into a single and accurate record. Having an identical record can skew analytical results, decreasing the chance of gaining accurate insights.

Data matching and linking ensure the creation of single and correct data by:

• Eliminating duplicate data entries
• Monitoring source system integrity
• Enriching data records with third-party data
• Automating resource-intensive data creation and verification tasks 

Data business rules

Data business rules specify actions and constraints to follow while creating, updating, deleting, or distributing data. These rules are usually centralized, meaning every system will reflect once the changes are applied. Data business rules help organizations to minimize risk and introduce governance strategies by:

• Defining data lifecycle for relevant object types
• Outlining decision and approval process workflows
• Specifying laws that enforce and ensure data integrity

Data localization management

Countries with strict data localization laws require organizations to store customer data locally. The General Data Protection Regulation (GDPR) ensures data transfer outside the EU only when there’s adequate protection. MDM solutions’ data localization management ability is crucial for data location standardization, integration, and centralized data connection with other domains.

Data privacy and security

Organizations with an increasing amount of data must establish efficient policies for protecting data privacy. MDM solutions come with role-based security policies that define access rights to sensitive data and restrict specific actions. Organizations can also protect their data from third-party access with MDM systems’ ability to encrypt data attributes with cryptographic keys.

Data enrichment

Data enrichment involves data quality enhancement with different tools and processes. MDM tools cleanse and streamline incorrect or incomplete data and collaborate with external data sources for data insights. This enriched data is key to identifying trends, understanding emerging patterns, and reducing risks.

Consent management

Data protection legislation has made it crucial for organizations to have a clear framework for storing and handling personal information. You must have proof of consent to obtain and keep data.

MDM solutions help you enforce such governance rules and adhere to consumers’ right to access and object. A centralized data repository allows you to manage consent, have a single view of data, and reduce personal data exposure risks.

Drivers for master data management

Organizations with multiple copies of business entity data suffer from operational data inefficiency, data quality issues, inconsistent data, and data classification issues. MDM software extracts data from disparate sources and loads them into the centralized master data hub to ensure a single view of data across the organization. 

Below are two scenarios that leave organizations with master data issues:

1. Business unit and product line segmentation: Organizations splitting products and operations into different segments often create redundant data. The front-to-back-office life cycle further compounds data redundancy. MDM helps you create an authoritative source of account and product data under such circumstances.
2. Mergers and acquisitions (M&As): Organizations going through mergers or acquisitions often experience issues with duplicate master data. Data deduplication is a standard solution in such cases. The challenge begins when the number of master databases increases, resulting in complex data reconciliation processes. MDM eases data integration from multiple sources and ensures post-M&A efficiency.

Benefits of master data management

Adopting a master data management process is crucial for data-driven organizations. Connected and accurate data helps them ensure business process agility and remain competitive.

Here are the main benefits of MDM platforms:

• Redundancy elimination: Teams usually collect and maintain data during their interaction with different business entities. As a result, there is data repetition and even unused data. MDM tools with data redundancy elimination (DRE) capabilities identify and eliminate duplicate intra-object and inter-object data elements.
• Easy data updates: Data updates happening in isolation causes data discrepancy because the changed data doesn’t reflect across the board. These updates affect business decision making and may even cause serious consequences. With MDM, you can edit the master data and ensure the updated data reflects across business units.
• Authoritative data source (ADS): Establishing an ADS is key to operational efficiency. MDM systems coordinate with different data sources, verify their origins, and merge them into one key node (a node with a unique key). ADS supports business processes by creating cohesive data assets that eliminate mismatched data issues.
• Data integrity: Organizations use data to analyze market trends, optimize supply chain performance, and improve product mix. Accomplishing these business intelligence applications is challenging when data flowing from different business functions is inaccurate or erroneous. MDM software ensures data integrity by maintaining data consistency and accuracy throughout the lifecycle.
• Data availability: Rapid digital adoption has enabled organizations to expand business overseas. As a result, business interactions happen virtually without people meeting in person. Multi-domain MDM ensures smooth business operations by making data available across the cloud, physical space, and cyberspace.
• Easy backup: Organizational data repositories often become subject to external threats and system crashes. Such incidents can lead to vital data corruption or data loss. MDM tools enable you to address these vulnerabilities with a centralized backup of resources.
• Improved regulatory compliance: Organizations today collect large volumes of data about their customers, partners, and products. All of these data must comply with business-specific or geographical regulations. MDM makes regulatory compliance easier by managing compliance levels and regulatory standards in a centralized data pool.

Challenges of master data management

Despite having advanced capabilities, MDM is not free from challenges. Here are some of the common challenges organizations face during MDM implementation:

• Data standards: Establishing proper data standards is crucial for accommodating different data types within your organization. That’s why you should define data standards that can handle cross-departmental data types and requirements.
• Data governance: Successful MDM implementation requires more than finding a suitable data model or setting up data standards. Consider having a well-defined data governance framework for addressing master data complexity effectively. This framework is crucial for having a clear data operations overview and identifying data quality issues.
• Master data management tools: It’s necessary to have the right set of tools to handle master data management requirements. Consider analyzing business requirements and understanding business goals to identify the best-suited tools for your organization.
• Data transfer: MDM tools make integrating data from different enterprise software and business channels easier. But the process of data transferring is highly error-prone and time consuming. That’s why it’s crucial to define policies for managing data integration with internal, external, and cloud applications.
• Data stewardship: Inaccurate data collection prevents master data consolidation and creates management problems. You can avoid these issues by defining data stewardship for managing tasks and authoring master data.

MDM architecture models

Finding a suitable implementation model is key to improving master data quality and data consistency. The implementation model also determines your ability to build service-oriented architecture (SOA) fabric, support the operational environment, and push clean data into existing systems.

The four standard MDM implementation models are:

1. Registry style

Registry style MDM implementation relies on cleansing and matching algorithms for identifying duplicate data across sources. It’s suitable for organizations with multiple data sources and rapid data integration needs.

This MDM model ensures a reliable golden record by assigning globally unique identifiers (GUID) to matched records and creating a real-time 360-degree view of each reference system.

Since registry style doesn’t send data back to source systems, it stores corresponding record links for data matching. This implementation style changes data on existing source systems, matches cross-referenced information, and assumes that source systems can manage their data quality.

2. Consolidation style

Consolidation style MDM creates an SSOT by consolidating data from multiple sources. A centrally managed MDM hub stores the golden record and applies updates to sources. This model is suitable for organizations with enterprise-wide reporting and data analysis needs.

After pulling data from existing systems, this implementation model cleanses, matches, and integrates a single record for one or more data domains. It’s also inexpensive and quick to set up.

3. Coexistence style

Coexistence style MDM implementation works similarly to consolidation models, but stores data in a central MDM system. It’s ideal for organizations looking to upgrade traditional consolidation style MDMs.

This model offers a single version of the truth by synchronizing master data across source systems and the hub. It ensures improved master data quality and faster access to data, but can be expensive to deploy.

4. Centralized style

Centralized or transaction style MDM implementation relies on cleansing, linking, matching, and enriching algorithms for storing, maintaining, and publishing master data attributes to respective source systems. It’s suitable for organizations with existing consolidation or coexistence style MDMs.

The transaction style hub supports security and visibility policies at a data attribute level. Besides merging master records, this system allows source systems to subscribe for updates.

MDM helps businesses accelerate growth by eliminating sub-optimal decision-making and data misalignment. Choosing a suitable implementation model and adopting best practices are equally important.

Master data management software

Finding the right MDM software is critical for enabling seamless master data processing. Let MDM software do the heavy lifting if you’re looking for robust features that make data consolidation, organization, deduplication, and storage more manageable.

To be included in this category, the software must:

• Track data from multiple sources related to an organization, specifically department performance metrics
• Consolidate, organize, and store master data, filter duplicate information, flag inconsistencies, and present findings in a clean format
• Provide administrators with tools or initiatives related to master data
  Export MDM data as necessary to other software tools

*Below are the top five leading master data management software solutions from G2’s Winter 2022 Grid® Report. Some reviews may be edited for clarity.

1. SAP Master Data Governance (MDG)

The SAP Master Data Governance (MDG) application provides a centrally-managed view of organization-wide data. It eases creating, consolidating, changing, and distributing master data across the system landscape.

What users like:

“It’s a highly recommended platform if you deal with master data related to material, supplier, customer, and finance. You can consolidate a massive amount of data in a database and play around with the data to accommodate changes requested by users.”

- SAP Master Data Governance (MDG) Review, Sahil M.

What users dislike:

“There should be more flexibility to create a custom Fiori application, including the customer scope. Also, the MDG documentation isn’t available online. Another point is that hierarchy creation and maintenance are unavailable on MDG.”

- SAP Master Data Governance (MDG) Review, Pranab M.

2. Syndigo Content Experience Hub

Syndigo Content Experience Hub offers an end-to-end solution for creating, managing, syndicating, enriching, and optimizing digital asset and product data. It’s known for enabling brands and suppliers to offer consistent content to e-commerce and store partners.

What users like:

“Our partnership with Syndigo has helped us serve customers better while growing internally and understanding the e-commerce business on a technical and forward-thinking level. Syndigo has been with us every step of the way, guiding us, and continuously improving the user experience.”

- Syndigo Content Experience Hub Review, Megan S.

What users dislike:

“It’d be super helpful to have a chat option available for assistance on problems I might have while working on my parts.”

- Syndigo Content Experience Hub Review, Sarah B.

3. Azure Data Catalog

Azure data catalog offers self-service data asset discovery with an enterprise-wide metadata catalog. It allows organizations to register enterprise data assets, connect data to tools, and make data source discovery seamless.

What users like:

“Azure is a fantastic, fully managed cloud service for getting a good hold of data sources. It allows anyone to understand data, no matter their job role.”

- Azure Data Catalog Review, Anubhab D.

What users dislike:

“As with any cloud platform, there are a few limitations, but Microsoft listens to feedback and fixes them. The new preview portal is sluggish, but it covers many gaps Microsoft had in the old portal.

Another factor is the cost of the Azure services compared to AWS. This could be a show-stopper for some organizations, but the value offered by Azure is better than AWS in the long term.”

- Azure Data Catalog Review, Prab T.

4. Dell Boomi

Dell Boomi offers an intelligent and scalable platform for synchronizing and enriching data across the enterprise. The agile technology foundation behind this platform contributes to the speedy flow of information, innovations, and interactions.

What users like:

“I like that Dell Boomi allows people to create ETL solutions for the client with little to no coding. This tool is straightforward to learn. Its learning curve is very shallow, which allows people to understand it in a short time. The simple drag-and-drop feature of Dell Boomi helps people create ETL pipelines successfully, even when they don’t have coding experience.”

- Dell Boomi Review, Ankur R.

What users dislike:

“Some of the procedure reports require changes and improved functionality to gather data for troubleshooting.I'd like to see mobile browser failures, records, and attempt broken procedures. Although Boomi helps you write a customized code for OOB types, I wish I could better organize the code and retain it. It can be challenging to report on process executions, delays, atomic states, and more. I'd love to see this knowledge on a dashboard.”

- Dell Boomi Review, Garnette L.

5. Oracle Enterprise Data Management Cloud

Oracle Enterprise Data Management Cloud offers a single platform for connecting disparate enterprise applications, managing master data changes, and distributing them to downstream applications. It also provides real-time collaboration and data visualization features.

What users like:

“I like to use this system since it offers quick access to unpredictable databases and a select view of specific and worldwide circumstances by concentrating all the information. Also, it allows me to enter and refresh information for fixing basic framework mistakes.

It offers information from numerous business zones like supply chain, finance, and logistics, in addition to tackling complex investigation issues. It’s easy to combine datasets, discover ongoing themes, and information honesty issues.”

- Oracle Enterprise Data Management Cloud Review, Dharmesi K.

What users dislike:

“Oracle Enterprise Data Management requires information handling, meaning there’s a need for information handling training which is quite demanding.”

- Oracle Enterprise Data Management Cloud Review, Jane N.

Make data-driven decisions with unified and trusted data

The more business data proliferates, the more insights and optimization opportunities emerge. But matching and reconciling data differences is highly error-prone. That’s why organizations are increasingly leveraging MDM tools to collect, consolidate, manage enterprise master data, and have a trusted 360-degree view of business entities.

Learn more about leveraging customer data for deeper analysis and enhanced insights.