what is master data management a complete guide

Introduction

Master data management (MDM) is a critical aspect of modern data management practices. MDM is the process of creating, maintaining and governing the master data, which is the core data that is used to support an organization's business processes and operations. Master data is typically shared across multiple systems and applications, and is used to drive business decisions. The goal of MDM is to ensure that this data is accurate, consistent, and reliable, so that it can be used to drive business value. Here's a complete guide to master data management (MDM).


What is Master Data?

Master data refers to the core, critical data that is used to support an organization's business processes and operations. This is non-transactional data. Master data is often shared across multiple systems and applications, and is used to drive business decisions. Examples of master data include:

  • Party, such as customers, employees, partners, patients, suppliers
  • Product, such as product name, asset, equipment, bill of materials
  • Financial, such certifications, accounts, financial policies
  • Location, such as agencies, stores, franchises, branches, offices


Why is Master Data Management Important?

Master data management is important for a number of reasons, including:

  • Data consistency: Master data is often shared across multiple systems and applications. Without proper management, this data can become inconsistent, which can lead to errors and confusion. MDM helps ensure that the data is consistent across all systems and applications.
  • Data accuracy: Master data is used to drive business decisions, so it is important that it is accurate. MDM helps ensure that the data is accurate by implementing processes for data validation and data quality checks.
  • Data governance: MDM helps organizations implement data governance processes, which are critical for ensuring that the data is used in an appropriate and compliant manner. Data governance processes include data security, data privacy, and data compliance.
  • Cost savings: MDM can help organizations save money by reducing the need for duplicate data entry and by improving the efficiency of business processes.


How Does Master Data Management Work?

Master data management typically involves a number of different processes, including:

  • Data collection: MDM starts with collecting the data from various sources, such as databases, spreadsheets, and external systems.
  • Data validation: Once the data is collected, it is validated to ensure that it is accurate and complete. Data validation can include checks for missing data, data format errors, and other issues.
  • Data quality checks: After the data is validated, data quality checks are performed to ensure that the data is accurate and consistent. This can include checks for data duplication, data inconsistencies, and other issues.
  • Data governance: Once the data is cleaned and validated, it is governed to ensure that it is used in an appropriate and compliant manner. This can include implementing data security and data privacy controls, as well as compliance with regulatory requirements.
  • Data maintenance: MDM also involves maintaining the data over time. This can include updating the data when it changes, archiving old data, and purging data that is no longer needed.
  • Data integration: MDM also involves integrating the data with other systems and applications, such as CRM and ERP systems. This helps ensure that the data is accessible and usable throughout the organization.


Master Data Management Solutions

There are several different solutions available for master data management, including:

  • MDM software: MDM software is a specialized type of software that is designed to help organizations manage their master data. MDM software typically includes features such as data collection, validation, quality checks, governance, maintenance, and integration. Some examples of MDM software include Informatica MDM, SAP Master Data Governance, and Informatica MDM Cloud.
  • Data governance tools: Data governance tools are designed to help organizations manage the governance aspects of their data. These tools can include features such as data security, data privacy, and compliance with regulatory requirements. Examples of data governance tools include Collibra, SAP Master Data Governance, and Informatica MDM Cloud.
  • Data quality tools: Data quality tools are designed to help organizations improve the quality of their data. These tools can include features such as data validation, data cleansing, and data matching. Examples of data quality tools include Informatica Data Quality, SAP Data Quality Management, and Informatica MDM Cloud.


Master Data Management Best Practices

To ensure that master data management is effective, organizations should follow best practices such as:

  • Develop a clear understanding of the organization's master data: Organizations should have a clear understanding of what data is considered master data and how it is used. This will help ensure that the data is correctly identified and managed.
  • Involve all stakeholders in the MDM process: MDM should be a collaborative effort that involves all stakeholders, including IT, business units, and external partners. This will help ensure that the data is accurate, complete, and meets the needs of all stakeholders.
  • Implement data governance processes: Data governance is a critical aspect of MDM, and organizations should implement processes to ensure that the data is used in an appropriate and compliant manner. This can include implementing data security and data privacy controls, as well as compliance with regulatory requirements.
  • Monitor and measure the effectiveness of MDM: Organizations should monitor and measure the effectiveness of their MDM processes to ensure that they are achieving their goals. This can include monitoring data quality, data consistency, and data governance.


Master Data Management in Practice

Implementing master data management (MDM) in practice can be a complex and challenging task, but it is essential for organizations that want to improve the quality and consistency of their data. Here are a few key considerations for organizations that are implementing MDM:

Define and scope the master data

The first step in implementing MDM is to define and scope the master data. This means identifying which data elements are considered master data and how they are used in the organization. This can be a complex task, as master data may be spread across multiple systems and applications. It is important to involve all stakeholders in this process, including IT, business units, and external partners, to ensure that the data is correctly identified and managed.

Data governance

Data governance is a critical aspect of MDM and is essential for ensuring that the data is used in an appropriate and compliant manner. This includes implementing data security and data privacy controls, as well as compliance with regulatory requirements. Organizations should establish data governance processes and procedures, such as data quality checks, data validation, and data cleansing, to ensure that the master data is accurate, complete, and consistent.

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Data integration

MDM also involves integrating the master data with other systems and applications, such as CRM and ERP systems. This helps ensure that the data is accessible and usable throughout the organization. Data integration can be a complex task, as it requires mapping data elements from different systems and applications, and may involve data transformation and data cleansing. Organizations should use specialized tools, such as data integration software, to help automate this process.

Data maintenance

MDM also involves maintaining the master data over time. This includes updating the data when it changes, archiving old data, and purging data that is no longer needed. Organizations should establish data maintenance processes and procedures, such as data archiving and data purging, to ensure that the master data is accurate, complete, and consistent.

Data quality

Improving the quality of the master data is an essential part of MDM. Organizations should implement data quality processes and procedures, such as data validation, data cleansing, and data matching, to ensure that the data is accurate, complete, and consistent. Data quality tools can help automate these processes and improve the quality of the data.


Master Data Management Challenges

While master data management (MDM) can bring many benefits to an organization, there are also a number of challenges that organizations may encounter when implementing MDM:

Data integration challenges

Integrating master data with other systems and applications can be a complex and challenging task. This may involve mapping data elements from different systems and applications, and may require data transformation and data cleansing. Organizations may also encounter challenges when integrating master data with external systems, such as data from partners or suppliers.

Data quality challenges

Improving the quality of master data can be a challenging task, as it requires a significant amount of effort and resources. Organizations may encounter challenges when implementing data quality processes and procedures, such as data validation, data cleansing, and data matching.

Data governance challenges

Data governance is a critical aspect of MDM, but it can also be challenging to implement. Organizations may encounter challenges when establishing data governance processes and procedures, such as data security, data privacy, and compliance with regulatory requirements.

Data maintenance challenges

Maintaining master data over time can also be a challenging task. Organizations may encounter challenges when updating data when it changes, archiving old data, and purging data that is no longer needed. This can be particularly challenging for organizations with large amounts of data, as it requires a significant amount of effort and resources. Additionally, maintaining master data may also involve dealing with data that is stored in legacy systems, which can add complexity to the process.

Data culture challenges

Another challenge organizations face with MDM is the cultural shift in the organization. It requires a cultural change in the way data is viewed, managed and used by an organization. Many times the data is siloed and not shared across the organization, and thus, implementing MDM requires a change in the data culture of the organization. This can be challenging, as it requires buy-in and cooperation from all stakeholders, including IT, business units, and external partners.

Resistance to change

Implementing MDM can be a significant change for an organization, and some stakeholders may resist the change. This can be particularly challenging for organizations that have been using the same data management practices for a long time. It is important for organizations to communicate the benefits of MDM and to involve all stakeholders in the process to help overcome resistance to change.


Master Data Management in the Cloud

In recent years, many organizations have started to implement master data management (MDM) in the cloud. Cloud-based MDM solutions offer several benefits over on-premises solutions, including:

Scalability

Cloud-based MDM solutions are highly scalable, meaning that organizations can easily add or remove resources as needed. This is particularly useful for organizations that have large amounts of data, as it allows them to handle the data more efficiently.

Cost-effectiveness

Cloud-based MDM solutions are typically more cost-effective than on-premises solutions. Organizations do not need to invest in expensive hardware or software, and they only pay for the resources that they use. This can help organizations save money in the long run.

Flexibility

Cloud-based MDM solutions are highly flexible, meaning that organizations can easily customize them to meet their specific needs. This is particularly useful for organizations that have unique data management requirements.

Automatic updates

Cloud-based MDM solutions are typically updated automatically, meaning that organizations do not need to worry about maintaining the software or applying updates. This can help organizations save time and resources.

Accessibility

Cloud-based MDM solutions are accessible from anywhere, meaning that organizations can access their master data from any location. This is particularly useful for organizations that have employees or partners working remotely.

In conclusion, Master Data Management is a critical aspect of modern data management practices. It helps organizations ensure that the data is accurate, consistent, and reliable, so that it can be used to drive business value. Organizations can implement MDM using specialized software, data governance tools, and data quality tools, and by following best practices such as involving all stakeholders and implementing data governance processes. Additionally, Cloud-based MDM solutions offer many benefits like scalability, cost-effectiveness, flexibility, automatic updates, and accessibility which can help organizations to manage their data more efficiently.


Master Data Management and Data Governance

Master data management (MDM) and data governance are closely related concepts, but they serve different purposes. MDM is focused on creating, maintaining, and governing the master data, which is the core data that is used to support an organization's business processes and operations. Data governance, on the other hand, is focused on ensuring that the data is used in an appropriate and compliant manner.

Data governance includes processes and procedures for data security, data privacy, and compliance with regulatory requirements. It also includes processes for data quality, data validation, and data cleansing. Data governance is critical for ensuring that the master data is accurate, complete, and consistent.

In practice, data governance and MDM often go hand in hand. Organizations that implement MDM should also implement data governance processes to ensure that the data is used in an appropriate and compliant manner. This can include implementing data security and data privacy controls, as well as compliance with regulatory requirements.


Master Data Management and Data Governance in the Cloud

Cloud-based master data management (MDM) and data governance solutions offer several benefits over on-premises solutions. Cloud-based MDM and data governance solutions can be accessed from anywhere and are highly scalable, meaning that organizations can easily add or remove resources as needed. They are typically more cost-effective than on-premises solutions and are updated automatically, saving organizations time and resources.

Cloud-based MDM and data governance solutions also provide organizations with more flexibility, as they can be customized to meet the specific needs of the organization. Additionally, cloud-based solutions offer security features and compliance capabilities to meet the strictest data protection regulations.

When implementing MDM and data governance in the cloud, it is important for organizations to ensure that they choose a solution that is compliant with the regulatory requirements of their industry. Additionally, organizations should carefully evaluate the security features of the solution to ensure that their data is protected.


Conclusion

Master data management (MDM) is a critical aspect of modern data management practices. It involves the creation, maintenance, and governance of the master data, which is the core data that is used to support an organization's business processes and operations. Data governance is a critical aspect of MDM, and is essential for ensuring that the data is used in an appropriate and compliant manner. Organizations can implement MDM and data governance using specialized software, data governance tools, and data quality tools, and by following best practices such as involving all stakeholders and implementing data governance processes. Additionally, Cloud-based MDM and data governance solutions offer many benefits like scalability, cost-effectiveness, flexibility, automatic updates, and accessibility which can help organizations to manage their data more efficiently.

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About the author 

George Firican

George Firican is the Director of Data Governance and Business Intelligence at the University of British Columbia, which is ranked among the top 20 public universities in the world. His passion for data led him towards award-winning program implementations in the data governance, data quality, and business intelligence fields. Due to his desire for continuous improvement and knowledge sharing, he founded LightsOnData, a website which offers free templates, definitions, best practices, articles and other useful resources to help with data governance and data management questions and challenges. He also has over twelve years of project management and business/technical analysis experience in the higher education, fundraising, software and web development, and e-commerce industries.

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