Don't Be Puzzled By MDM






We blend Master Data Management consulting, education and technology to provide a complete business solution to master your data.

Master Data Management (MDM) is an essential capability for any organisation that wants to maintain a reliable, accurate, and consistent view of its critical data. Through MDM, businesses centralize their data management efforts, ensuring that critical data is accurate, up-to-date, and accessible to everyone who needs it.

We have delivered MDM programs for clients in financial services, government and manufacturing.

What is Master Data Management and why is it important

break down data silos with MDMMaster Data Management (MDM) is the technology, processes and governance enabling an organisation to link and share critical master data. MDM ensures the uniformity, accuracy, stewardship, semantic consistency, and accountability of the enterprise's shared master data assets.

Master data are the objects of a business - typically people, places, concepts and things - that provide context for transactions. 

For example, a supplier (person) may provide a service (thing) under the terms of a contract (concept). Or a customer (person) may buy a product (thing) from a store (place).

How to achieve a single view of critical business data

Why do we need Master Data Management?

In practice, master data may reside across many systems, each applying different standards and rules. Even within a single system, data quality problems mean that master data is often duplicated or redundant.

Master data management allows businesses to create a "single source of truth" for their data, which means that everyone in the organization has access to the same information. This is particularly important for larger organizations that have multiple departments and teams, each with its own databases and data sources.

One of the primary benefits of MDM is that it enables organizations to make better, data-driven decisions.

By having a consistent and reliable view of their data, businesses can identify trends, patterns, and insights that would be difficult or impossible to see otherwise. This, in turn, can help them make more informed decisions, which can lead to improved business outcomes.

Another significant advantage of MDM is that it can help businesses improve their overall process efficiency. By centralizing master data management, organizations can streamline their data-related processes, reducing duplication, errors, and inconsistencies. This can lead to improved productivity and customer satisfaction.

Master Data Management is not a technology problem.

Master data management is a process of continuous improvement built around data governance and data quality,  It aligns data, process and governance to ensure consistent, reusable master data.

Keeping accurate master data requires significant alterations to business processes, and some of the most challenging obstacles in mastering data management are not technical but rather political.  Some examples include:

  1. Siloed organisational structures: Many organisations have siloed structures where different departments or business units operate independently, with their own processes and data. This can create challenges for MDM, as there may be resistance to sharing data or adopting common processes. For example, a company with separate sales and marketing teams may have different systems for managing customer data, making it difficult to create a single, accurate view of customers.

  2. Data ownership issues: In some organisations, there may be disputes over who owns or controls certain data sets. This can lead to delays or conflicts in implementing MDM, as different stakeholders may have different priorities or goals. For instance, a hospital may have multiple departments claiming ownership over patient data, which can complicate efforts to create a unified patient record.

  3. Resistance to change: Implementing Master Data Management often requires changes to processes, systems, and roles within an institution. However, some employees or managers may resist these changes due to concerns about job security, loss of control, or other factors. For example, an IT department may be hesitant to give up control over data management systems, even if implementing MDM would be beneficial to the organization as a whole.

Throwing technology at the problem without business oversight frequently makes problems worse. 

In the long run, tools and processes will be needed to ensure that clean master data can be maintained and shared. The size and complexity of both the organisation and the data landscape should be carefully considered before selecting appropriate tools.

Reference Data is a subset of Master Data

Gartner defines reference data as "a consistent and uniform set of identifiers and attributes that accurately describe the strategic information required for an organization to function properly.” Reference data provides context for reporting and analytics.

These critical data elements should be managed centrally, to ensure accurate reporting, to ensure consistency across business processes and applications, and to facilitate the sharing of data between divisions.

L’Occitane case study: What value are they getting from MDM?

Methodology for implementing Master Data Management

Master Data Management Essentials across people, process and technology

Master data managemenmaster data management essentialst is a discipline for creating and maintaining a trusted source of reference data. While technology is an important enabler, it should not be the driver.

In a 2021 Gartner research paper,  Getting Started with Master Data Management in Midsize EnterprisesAlan D. Duncan agreed that the business, not technology, must be the starting point for MDM.

Implementing MDM with a business-first approach

Our business-first master data management implementation approach can be summarised as 4 steps:

  1. Engage stakeholders through business alignment and data governance
  2. Understand the processes through an abstract master data entity model
  3. Assess and plan for data quality
  4. Select and implement appropriate tools

Read our blog post, 10 best practices for master data management

MDM Technology identification and implementation

understand the role of data qualityThe three stages above create the foundation for a successful master data implementation. We are now ready to assess technologies to support the master data implementation.

We should have a clearly defined and business-driven scope that includes:

  • Business goals and priorities
  • A master data entity model with source-to-target mapping
  • Data standards, validations, match rules and risks must be catered for during implementation.

What technologies make up an MDM solution?

A common error is to assume that MDM is achieved through the implementation of a so-called MDM tool. In fact, master data management can be achieved without the use of an MDM tool, although in most instances the right combination of tools will provide a lower-risk option than trying to build everything you need from scratch.

Some of the key technologies that can make up MDM include:

  1. Data profiling and cleansing tools: These tools help to identify data quality issues and inconsistencies in master data. They can be used to validate, clean and standardize data, which improves data accuracy and consistency.

  2. Data integration and ETL tools: These tools enable the integration of data from various sources and formats. They can transform and map data to a common data model, which ensures consistency and reduces data redundancy.

  3. Data governance and security tools: These tools enable the management of data ownership, access, and usage. They can ensure compliance with regulatory requirements and industry standards.

  4. Master data repository: This is the central repository for master data. It provides a single source of truth for master data, which can be accessed by different systems and applications.

  5. Data quality monitoring and reporting tools: These tools provide visibility into data quality issues and inconsistencies. They can generate reports and dashboards that help to monitor and improve data quality over time.

When selecting MDM technologies, it is important to consider business alignment first. This means understanding the business requirements, data management needs, and data governance policies. Secondly, understand which of the capabilities described above are in place, and can be reused. 

By aligning the MDM solution with the business objectives, and reusing existing tools and capabilities where possible, MDM technologies can be selected that best meet the business requirements.

20 Years MDM Implementation experience

Our consultants have delivered master data solutions for a range of data areas - including Product/Materials Data, HR/Employee Data, Supplier/Vendor Data, Customer Data and Name & Address Data.

We have worked in several industries - including banking, insurance, government, telecommunications, hospitality, mining and manufacturing

We understand the complexities of managing African data - including multiple languages, minimal standards and a lack of reference data and our methodologies address these complexities for best results.

Whatever your data quality problem, we provide a practical solution:

  • Strategic end-to-end MDM program
  • MDM Readiness Assessment and Data Migration
  • Data Governance and Quality for MDM
  • Train and certify your MDM team
  • Best-of-breed tools

Master Data Management Training

CIMP MDM TrainingThrough eLearningCurve, we offer 9 online courses, from the basics to advanced concepts, leading to a Certified Information Professional (CIMP) accreditation in  Master Data Management.


What is master data management (MDM)?

Master data management (MDM) is a process that involves identifying, consolidating, cleansing, enriching, governing, and maintaining an organization's critical data assets to provide a single, authoritative source of truth for the data.

What are some benefits of implementing MDM?

Some benefits of implementing MDM include improved data quality and consistency, reduced data errors and inconsistencies, increased operational efficiency, better decision-making, and enhanced regulatory compliance.

What types of data can be managed using MDM?

Multidomain MDMMDM can be used to manage various types (or domains) of data, including customer data, product data, financial data, employee data, and other critical data assets. Modern multidomain MDM solutions can cater for all of these and more.

What are some common challenges associated with implementing MDM?

Common challenges associated with implementing MDM include data silos, data quality issues, lack of data governance, resistance to change, and lack of executive sponsorship.

What are some best practices for successful MDM implementation?

Some best practices for successful MDM implementation include establishing clear goals and objectives, involving stakeholders in the process, developing a comprehensive data strategy, selecting the right MDM solution, implementing a data governance framework, and regularly monitoring and measuring progress.

Why is Data Governance essential for MDM?

 Proper governance sits on top of MDM, data movement or data warehouses for that matter, and ensures that the data is understood by the business from definitional, sourcing, quality, and accountability perspectives. 

Why is data quality essential for MDM?

Implementing an MDM product without considering data quality is simply a recipe for consolidating all of this data into another database - creating, in essence, a 7th customer master or a 10th product hub. Data quality is the differentiator that allows you to standardise and combine related records to create a trusted master data source. In fact, data quality can deliver 80% of the value of a full-blown MDM stack, at a fraction of the cost, in particular when integrating into your commercial ERP or CRM platform.

 While MDM is certainly a driver for data quality, the benefits of data quality must extend beyond master data to encompass all business-critical data, most notably, in order to support reporting and compliance requirements and enhance operational efficiency.

Read our Blog post Which Comes First: Data Quality or MDM?

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