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Stibo Systems - The Master Data Management Company
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Table of contents
Why is master data management important?
What are the benefits of master data management?
What are the different types of master data?
What are the most common master data management solutions?
What are the core capabilities of master data management?

What is Master Data Management?

Everything you need to know about industrial master data management (MDM) and how it brings data together from across the enterprise and business ecosystem to create a single, accurate and transparent record of the truth.

Introduction to master data management

Master data management (MDM) is the core process used to 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.

Master data can take the form of product, customer, supplier, master location data, and asset information, in addition to any information sources that drive your business.

The efficient management of master data in a central repository gives you a single authoritative view of information and eliminates costly inefficiencies caused by data silos.

It supports your business initiatives and objectives through identification, linking and syndication of information and content across products, customers, stores/locations, employees, suppliers, digital assets and more.
 

Why is master data management important?

MDM is important for several reasons:

  • Data quality:

    MDM ensures that data is accurate, complete and consistent across the organization. This improves the quality of data used in decision-making and reduces errors caused by inconsistent or inaccurate data.

  • Data integration:

    MDM helps to integrate data from multiple sources and systems, creating a single, unified view of data. This enables organizations to have a comprehensive understanding of their data and make better-informed decisions.

  • Operational efficiency:

    MDM can help organizations improve operational efficiency by reducing the time and effort required to manage data. By creating a centralized view of data, MDM eliminates the need for redundant data entry, improves data sharing and automates processes.

  • Regulatory compliance:

    MDM can help organizations meet regulatory compliance requirements by ensuring that data is accurate, complete and up-to-date. 

  • Customer satisfaction:

    MDM helps organizations improve customer satisfaction by providing a complete and accurate view of customer data. This enables organizations to provide better customer service, target marketing campaigns more effectively and improve overall customer experience.

  • Cost savings:

    MDM can help organizations save costs by reducing data errors, eliminating redundant data and improving operational efficiency.

Overall, MDM is important for organizations because it helps to improve the quality of data used in decision-making, streamline operations and ensure regulatory compliance. It also helps to improve customer satisfaction, reduce costs and improve overall organizational performance.

What are the signs that your organization needs master data management?

MDM is an important discipline for organizations that want to improve the quality of their data, streamline operations and ensure regulatory compliance. However, not all organizations may recognize when they need MDM.

Here are some of the most common signs that may indicate an organization needs MDM:


  • Data quality issues:

    If there are data quality issues such as incomplete, inaccurate or inconsistent data, it may be a sign that MDM is needed. Poor data quality can result in poor decision-making, inefficient operations and increased costs.

  • Data silos:

    If data is stored in multiple silos or systems across an organization, it can be difficult to create a comprehensive view of data. MDM can help to consolidate data from multiple sources and systems into a single, unified view.

  • Duplicate data:

    If there is duplicate data across different systems or departments, it can be difficult to determine which data is accurate. MDM can help to identify and eliminate duplicate data, reducing data redundancy and improving data accuracy.

  • Lack of data governance:

    If there is no clear ownership or governance structure for data within an organization, it can lead to inconsistent data management practices. MDM can help to establish a governance structure for data, defining who is responsible for data, how it is used and how it is maintained.

  • Inefficient data management:

    If data management processes are inefficient or time-consuming, it can be a sign that MDM is needed. MDM can help to streamline data management processes, reducing the time and effort required to manage data.

  • Compliance issues:

    If an organization is subject to regulatory compliance requirements, such as HIPAA in healthcare or GDPR in Europe, it may be a sign that MDM is needed. MDM can help to ensure that data is accurate, complete and up-to-date, helping organizations meet regulatory compliance requirements.

Overall, if an organization is experiencing any of these signs, it may be a good indication that MDM is needed to help improve data quality, streamline operations and ensure regulatory compliance.
 

What are the core capabilities of master data management?

The core capabilities of MDM typically include:

  • Data modeling:

    Developing a consistent and comprehensive data model that represents the organization's critical data entities, attributes and relationships.

  • Data profiling:

    Analyzing and assessing the quality, completeness and consistency of the organization's data to identify areas for improvement.

  • Data integration:

    Creating a unified view of critical data across multiple, disparate systems and applications to ensure consistency and accuracy.

  • Data governance:

    Establishing policies, procedures and processes to ensure that critical data is accurate, consistent and secure throughout its lifecycle.

  • Data quality management:

    Monitoring and maintaining the quality of critical data to ensure that it meets the organization's requirements for accuracy, completeness and consistency.

  • Data stewardship:

    Assigning responsibilities and accountabilities for critical data to ensure that it is managed effectively throughout its lifecycle.

  • Metadata management:

    Establishing a comprehensive metadata framework to manage the definition, usage and lineage of critical data.

By leveraging these core capabilities, organizations can create a single, reliable source of truth for critical data across the enterprise, enabling them to make better decisions, improve operational efficiency and enhance customer experiences.
Executive Brief How to develop clear data governance policies and processes for your MDM implementation

When implementing MDM, developing clear data governance policies and processes is key to its success. This executive brief will provide insights into how to develop effective data governance policies and processes to support your MDM implementation, ultimately ensuring that your data is trustworthy and impactful.

 Download Executive Brief
how to develop clear data governance policies and processes for your master data management implementation
 

What are the benefits of master data management?

Ensuring visibility and access to trustworthy data across the business ecosystem with MDM delivers multiple benefits for your business entity. Examples of benefits include:
Increase business agility Increase business agility

Increase business agility

Respond to evolving markets, quickly onboard products, add channels and manage mergers and acquisitions.
Ensure compliance Ensure compliance

Ensure compliance

Build trust through compliance, and customer loyalty by adhering to data privacy standards.
Manage and mitigate risk Manage and mitigate risk

Manage and mitigate risk

Drive accurate decisions with data governance, automate error-prone processes and prevent regulatory violations.
Empower the supply chain Empower the supply chain

Empower the supply chain

Enable traceability across systems, suppliers, ecosystems and channels, and insight into CSR and sustainability practices.
Accelerate digital transformation Accelerate digital transformation

Accelerate digital transformation

Fuel innovation initiatives like AI and personalization with high-quality data to drive differentiation, value and ROI.
Enhance operational efficiency Enhance operational efficiency

Enhance operational efficiency

Streamline and automate business processes, and improve decision making, productivity, quality and time to market.
Deliver exceptional experiences Deliver exceptional experiences

Deliver exceptional experiences

Drive personalized omnichannel experiences and enable customers to make confident, trustworthy decisions.
Start delivering the data transparency advantage Start delivering the data transparency advantage

Start delivering the data transparency advantage
 

What are the different types of master data?

Master data is a single source of common data, but there are many master data types. They are often duplicated across the business and stored on disconnected, siloed systems with little agreement on standard definition. Common types of master data are:

Product data

Rich and accurate product data helps customers make informed buying decisions. Studies show giving people better information and content boosts online sales and reduces returns. High-quality product data also facilitates accelerated vendor onboarding.

Customer data

Accurate, timely and complete customer data improves business initiatives and streamlines processes for both B2B and B2C companies. It drives accurate segmentation and reporting, more personalized experiences, increased sales, regulatory compliance and more.

Supplier data

Store and access all data related to your suppliers in one repository and reference it to any other data domain to achieve a 360° view of your critical data. This results in greater control, closer relationships and an improved supplier and product onboarding process.

Location data

Location data is essential for managing your physical stores, offices, warehouses and more. Combine location data with product and supplier data to gain better insight into your data supply chain and mitigate business risks.

Party data

Mastering party data allows you to create the relationships that are vital to gain an accurate understanding of the value of each of your records. It makes it easier to identify customers and provide better customer service.

Reference data

Keeping reference data fields such as country, currency and conversions up-to-date is critical to making key business decisions, understanding their impact on the performance of your systems and meeting regulatory compliance.

Asset data

Managing asset data with disparate systems across departments can drag down data quality. With all asset data linked together in a central repository you have an accurate overview of who is using what equipment, where it is stored, and when it was maintained and by whom.

Employee data

Employees are valuable assets. Managing employee data in a centralized MDM solution enables you to connect it with other domains and gain new insights, such as which employees have worked on what projects, where they’re located and their skill levels.

Ledger data

MDM prevents ERP from becoming a ledger data silo. By connecting ERP with other systems and third-party sources, MDM enhances efficiency and becomes your single source of truth, consolidating product data as well as supplier and vendor information.

Vendor data

For manufacturers, trustworthy and visible vendor data is crucial to secure correct billing and transaction data, achieve compliance with vendor and end-consumer demands and quickly expand with new vendors who are typically retailers.
Getting started Building the business case for master data management

This ebook provides a step-by-step approach to building a comprehensive MDM strategy, choosing the right platform and implementing your solution. With our ebook, you can start building your own success story about how MDM will help create value in your organization.

 Get the eBook
Building-the-Business-Case for master data management
 

What are the most common MDM solutions?

MDM extends the value of IT investments through integration with enterprise applications and systems. MDM solutions cover different data domains relevant to all of your business challenges. The four most common MDM solutions are:
Product MDM

Product MDM

Also referred to as product information management, Product MDM enables businesses to create a single source of truth for product data, and to share it with speed and confidence with customers and partners. The ability to enable and manage data transparency empowers businesses to address growing consumer demand for information and insight into product ingredients and origin, fueling engaging, trustworthy relationships while maintaining complete privacy and control.
Customer MDM

Customer MDM

Gaining a single view of your customer records is challenging due to data being scattered across multiple, disconnected systems. The inability to provide trustworthy customer data can lead to a poor customer experience, lost revenue and customer churn. Customer MDM connects, cleanses and enhances customer data, providing a foundation for personalized experiences as well as enhancing the ability to provide data transparency to support privacy, governance and regulatory compliance.
Supplier MDM

Supplier MDM

Supplier MDM enables a single, trustworthy view of your suppliers and their products for greater savings and efficiencies. It enables you to accelerate onboarding of supplier and product data, mitigate risk, increase product assortments and support supply chain resilience. With Supplier MDM it become easy to centralize and govern supplier and product data which results in greater data management cost savings, efficiency and customer confidence.
Location MDM

Location MDM

Gaining a single, trustworthy view of your location data, leveraging built-in data quality, governance and integration capabilities becomes possible with Location MDM. Location MDM enables businesses to manage, maintain, and seamlessly share location and site master data across multiple communication channels and applications to thereby, achieve a single view of your geographic, site and inventory data, which enables better customer experiences and boost profitability.

How to choose the right master data management solution for your organization

Choosing the right MDM solution can be a complex process, but there are some key factors that can help guide your decision. Here are some steps to consider:

1. Define your business requirements:

Identify the critical data domains that need to be managed, the business processes that require master data and the data governance policies and procedures that need to be enforced. This will help you determine the functionality and capabilities that your MDM solution needs to provide.

2. Evaluate the MDM vendor

Evaluate the MDM vendor based on their experience, reputation and track record. Consider factors such as their industry expertise, the size and scope of their customer base, their level of innovation and their ability to support your specific business requirements.

3. Assess the MDM platform

Assess the MDM platform based on its scalability, flexibility and integration capabilities. Consider factors such as the data model, the ability to handle multiple data domains, the support for data governance and stewardship and the integration with other IT systems.

4. Evaluate the data quality capabilities

Evaluate the data quality capabilities of the MDM solution, including data profiling and analysis, data cleansing and enrichment and data validation and verification. Consider factors such as the level of automation, the ability to handle large volumes of data and the support for data quality monitoring and reporting.

5. Consider the deployment options

Consider the deployment options for the MDM solution such as cloud-based, on-premise or hybrid. Evaluate the pros and cons of each option based on factors such as security, scalability and cost.

6. Evaluate the total cost of ownership

Evaluate the total cost of ownership for the MDM solution, including the licensing, implementation and ongoing maintenance and support costs. Consider factors such as the pricing model, the level of customization required, and the level of support provided by the vendor.

By considering these factors, you can choose the right MDM solution for your organization, ensuring that it meets your business requirements, aligns with your IT landscape and provides the functionality, scalability and data quality capabilities that you need.
MDM ROI Calculator Put a figure on the return on your data management investment

Data management projects are hard to quantify. There is great financial value to be gained from improving data quality and automating data processes. But how do you estimate it? The ROI Calculator can help you build your business case for MDM.

 Get your customized report
ROI calculator for master data management

The four most common types of MDM deployment styles and how to choose the right one for your organization

MDM can be deployed in different styles, each with its unique characteristics and benefits. The four main MDM deployment styles are:

  • Consolidation MDM:

    In this style, all master data is consolidated into a single repository, where it is standardized, cleansed and enriched. This approach is suitable for organizations with a large number of disparate systems that need to be integrated into a single source of truth.

  • Registry MDM:

    In this style, the master data is maintained in its original systems and the MDM system acts as a registry of the master data attributes. This approach is suitable for organizations that do not want to consolidate all data but need a way to manage the relationships between data objects.

  • Centralized MDM:

    In this style, the master data is managed centrally, but the data is synchronized with the source systems as needed. This approach is suitable for organizations that want to manage their master data centrally but also want to allow some autonomy in their business units.

  • Coexistence MDM:

    In this style, multiple MDM systems coexist within an organization, each managing specific domains of master data. This approach is suitable for organizations that have multiple lines of business that require different MDM systems.

To choose the right MDM deployment style for your organization, consider the following factors:

  • Business requirements:

    Identify the critical data domains that need to be managed, the business processes that require master data and the data governance policies and procedures that need to be enforced.

  • IT landscape:

    Understand the existing IT systems, the data sources and the data integration capabilities of your organization.

  • Organizational structure:

    Consider the organizational structure, the business units and the decision-making processes that need to be supported.

  • Data quality requirements:

    Determine the data quality standards that need to be met, and the level of data cleansing and enrichment that is required.

  • Compliance requirements:

    Consider any regulatory compliance requirements that need to be met and the level of data security that is required.

By evaluating these factors, you can choose the right MDM deployment style for your organization, ensuring that it meets your business needs, aligns with your IT landscape and complies with regulatory requirements.

Master data management best practices

Here are eight best practices for MDM:

1. Establish clear data governance

Data governance is critical for MDM because it defines who is responsible for data, how it is used and how it is maintained. Establish clear data governance policies and procedures to ensure that data is managed effectively.

2. Identify and prioritize data domains

Identify the key data domains within your organization such as product data, customer data or supplier data and prioritize them based on their importance to your business.

3. Develop a data quality framework

Develop a data quality framework to define the standards and processes for data quality. This should include data validation, data profiling and data cleansing.

4. Create a centralized data repository

Create a centralized data repository that stores all master data for the organization. This helps to ensure that data is consistent and up-to-date across the organization.

5. Use a standardized data model

Use a standardized data model to ensure that data is structured consistently across the organization. This can help to eliminate data silos and ensure data consistency.

6. Implement data security measures

Implement data security measures to protect sensitive data and ensure compliance with regulatory requirements. This includes user access controls, data encryption and data masking.

7. Implement data integration and synchronization

Implement data integration and synchronization to ensure that data is shared and updated in real-time across all relevant systems and applications.

8. Establish a data quality monitoring program

Establish a data quality monitoring program to monitor data quality over time and identify any issues or trends that require corrective action.

By following these best practices, organizations can effectively manage their master data and ensure that it is accurate, complete and consistent across the organization. This can lead to improved decision-making, operational efficiency and overall organizational performance.
How to create a solid RFP for MDM to underpin your enterprise data strategy

The process of identifying and purchasing enterprise solutions can vary widely, depending on the size and industry focus of the organization and the nature of their current infrastructure. Explore this comprehensive guide to help you identify the right solution and/or partner. 

 Get the Guide
RFP for master data management

How to successfully implement MDM

Implementing MDM can be a complex process, but there are some best practices that can help ensure a successful implementation. Here are some key steps to consider:

1. Define your MDM strategy

Develop a clear understanding of the business requirements, goals and scope of the MDM initiative. Define the data domains that need to be managed, the data governance policies that need to be enforced and the key performance indicators (KPIs) that will measure the success of the project.

2. Assess your current data landscape 

Conduct a comprehensive assessment of your current data landscape to understand the quality, completeness and consistency of your data. This will help identify any data quality issues and data silos that need to be addressed.

3. Develop a data model

Develop a comprehensive data model that defines the entities, attributes and relationships of the master data. This will serve as the foundation for your MDM implementation.

4. Choose an MDM platform

Choose an MDM platform that meets your business requirements, aligns with your IT landscape and provides the functionality and scalability that you need.

5. Build and deploy your MDM solution

Build and deploy your MDM solution based on the data model, using a phased approach that prioritizes the most critical data domains and business processes. Use data profiling and analysis, data cleansing and enrichment and data governance and stewardship to ensure that the data is accurate, complete and consistent.

6. Establish ongoing data quality monitoring

Establish ongoing data quality monitoring and reporting to ensure that your MDM solution remains effective over time. Define data quality metrics and KPIs that measure the effectiveness of your MDM solution and identify any issues that need to be addressed.

7. Maintain and evolve your MDM solution

Maintain and evolve your MDM solution to ensure that it continues to meet your evolving business needs and regulatory requirements. Conduct periodic reviews to assess the effectiveness of your MDM solution and identify opportunities for improvement.

By following these best practices, organizations can successfully implement MDM, improve data quality, reduce data redundancy and gain a unified view of their data. This can lead to better decision-making, improved operational efficiency and enhanced regulatory compliance.

Selected Customer Success Stories

kelloggs
The thing that has made Kellogg’s implementation unique is that it has been global from day one. When we evaluated solutions, it had to have that capability and be able to accommodate all the languages and nuances for our markets around the world." Global Director IT Marketing | The Kellogg Company
Read the Success Study
oatly customer video
When I think back about the collaboration I had with Stibo Systems, I see them as the role models I look for in new IT suppliers. ” Albin Junhede Sustainability Reporting System Lead, Oatly
Watch the Video
 

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