Business-First approach to Master Data Management (MDM)

At Master Data Management, we understand that MDM implementation is not as easy as downloading a new piece of software. It’s a process that requires many steps.

MDM Implementation

Gartner indicates that only 33% of MDM programs will succeed in demonstrating value to the organization.

MDM programs without a data management strategy are most likely moving data with redundancy. This is a prescription for failure.

Consolidating critical data points across different application environments like CRMs, ERPs and other core system applications, etc. is very complex because many applications share common data points, but they are not related through enforced data model layers.

Key success factors for MDM implementation

Before implementing an MDM program, it’s important to identify and address the following key success factors:

1. End-User Buy-In: When starting the program, it’s important that scope and objectives are created with the end user’s buy-in. As a business-driven, IT-enabled program, MDM helps business stakeholders to own the data and ownership of the outcomes. Most MDM outcomes are expected to improve business process management program efficiency or redefine existing business processes. Expecting business teams to accept new changes without business buy-in is one of the most common causes of MDM failures.

2. Data Classification and Policies: Data may be classified in many ways based on both internal and external policies. In recent years, with far-reaching regulations such as BCBS 239, GDPR and PoPIA, the need to classify critical or personal data, understand policy, usage, access, and distribution rights have come to light, with increased fines, financial, and reputational risks for lack of compliance.

3. Golden Source Rule: The ability to establish the authority of the data source using clear business objectives that are verifiable to prove the data origin. This also includes data ownership that defines when and how the data from the source can be re-written or changed and who can change it.

4. Data Catalog/Inventory: Most MDM programs start with a small data set like customers or geography. As they incrementally expand, it becomes more difficult and complex to address multi-domain MDM without proper data cataloguing or inventory. To do so, business users leverage data catalogues to identify and use the right sets of master data for decisions.

5. Enterprise Data Quality: Can you trust your data? Data quality needs to address end-user challenges that are more subjective in nature such as data believability, data trustworthiness, data provenance, etc.

6. Data Ownership: Identifying who owns the data has to occur before an MDM program is rolled out. Once the data custodian is identified, that individual then has to identify and bless all required rules and policies to ensure MDM success.

7. Measurable outcomes: MDM programs usually propel business process changes within the user Before implementing your MDM program, it’s important to benchmark business efficiency and then measure incremental benefits achieved by the business team post-implementation.

The success of the above requirements is contingent upon solid data management and data governance programs. Tools and technology can help address any of the areas above that might be main points rather than success points. Ideally, organizations want to find an easy-to-manage, single solution that can be implemented quickly in a cloud environment, while complimenting core MDM tools.

Methodology for MDM Implementation

Master data management 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.

We recommend a four-step process for initiating and implementing MDM

Business Alignment

Master Data Management (MDM) involves not only the management of data, but also the management of the business procedures that depend on accurately managed master data, including the procedures responsible for producing, updating, and discontinuing such master data. 

Business alignment ensures that an organisation's business objectives, processes, and technology infrastructure for master data are aligned and working towards a common goal.

Here are some reasons why business alignment is a crucial first step in MDM:

1. Ensuring that MDM supports business objectives: MDM should be aligned with an organisation's business objectives and priorities. Without this alignment, MDM may not provide the necessary value to the business. For example, if a company's main business objective is to improve customer satisfaction, MDM initiatives should focus on improving the quality and consistency of customer data.

2. Creating a common language for data: Business alignment ensures that all stakeholders agree on common definitions and rules for master data. This helps to eliminate confusion and misunderstandings when working with data. For example, a bank may have different departments using different terms to refer to the same financial products. Business alignment can ensure that everyone is using the same terminology when referring to these products.

3. Facilitating cross-functional collaboration: MDM initiatives often involve multiple departments and stakeholders. Business alignment facilitates collaboration and communication between these groups. For example, a healthcare provider may need to ensure that patient data is consistent across different departments, including medical records, billing, and customer service.

4. Ensuring technology supports business processes: MDM initiatives require a technology infrastructure that supports the organization's business processes. Business alignment can ensure that the technology is aligned with these processes. For example, a retailer may need to ensure that its point-of-sale systems, e-commerce platforms, and inventory management systems all work together to provide a seamless customer experience

MDM must begin with data governance.

In our experience, master data programs that are IT-driven and do not have strong business engagement end up perceived as failures, irrespective of what is delivered. Data governance enables business alignment by bringing business and IT stakeholders from across the enterprise together to agree, share and enforce policies and rules for the use of data to support key business processes.

MDM without data governance is ‘data integration’, not MDM! - Andrew White, Gartner

Business vision and goals can be used to identify the accountable business executives that must be engaged in order to define the master data scope and objectives.

We recommend that these executives form the core of a strategic data governance program to determine and prioritise the financial benefit that data brings to the organisation and identify and mitigate the business risks associated with poor data practices and quality

Create a focused scope by supporting key business processes first.

A successful MDM program ensures data consistency, completeness, and accuracy, but simply implementing an MDM program by itself cannot ensure success unless business-supported data management programs are run in parallel. 

MDM should be used to optimise key business processes. The master data governance committee or steering committee should include business stakeholders relying on these processes – these include producers, consumers and owners of data - as well as IT executives responsible for enablement and delivery. 

Engaging these stakeholders early and often allows a focused master data scope to be defined and implemented, whilst ensuring that other stakeholders understand when and how their requirements will be accommodated.

Data governance is a critical success factor to ensure business alignment for the MDM project.

Governance should enable collaboration between all stakeholders, identify key data requirements and standards, and create the context and priorities for successful MDM. Without data governance, MDM projects frequently unravel due to conflicting needs and political agendas.

Governance ensures strategic planning and communication of decisions and discoveries related to data such as: What does the data mean? Where does the data come from? How do I find the data I need?  Who uses the data, and for what purposes? Who is responsible for capturing data and ensuring quality?

 If you are trying to implement an MDM stack without understanding how the data must be used by different stakeholders then you don't have MDM, you have data integration.

For example

A global manufacturing company realized that its multiple divisions were using different systems and processes to manage customer data. This led to inconsistent and inaccurate data, which in turn caused delays and errors in business processes. The company embarked on an MDM initiative to improve the quality and consistency of customer data. However, they quickly realized that business alignment was a crucial first step.

The company conducted a thorough review of its business objectives, processes, and technology infrastructure. They worked with stakeholders from different departments and divisions to create a common language for customer data and to ensure that the MDM initiative supported the company's business objectives.

This business alignment enabled the MDM initiative to be successful and provide the expected value to the business.

Master Data Entity Model

A master data entity model is a coherent, abstract model of the typically complex combination of data sources and physical data structures that make up the master data landscape.  It depicts business concepts and entities, and how they (should) interact with each other, in a way that is relevant and understandable to non-technical consumers, and which is not dependent on existing applications data models.

A master data entity model ensures that all stakeholders have a clear understanding of the data entities that need to be managed and the relationships between them. This helps to ensure that the data is accurate, consistent, and up-to-date across all systems and applications.

Let me give you an example. Imagine a retail company that sells products online, in-store and via a network of partners. The company has multiple systems and applications that store customer data, partner data, product data, and sales data. Without a master data entity model, it would be challenging to ensure that all of this data is accurate and consistent.

Using this model, the company can define MDM processes to ensure that all of this data is accurate, consistent, and up-to-date across all systems and applications. This helps to improve the customer experience, reduce operational costs, and support business growth.

By creating a master data entity model, the company can identify the critical data entities that need to be managed, such as customers, products, and sales transactions. They can also define the relationships between these entities, such as the fact that a customer can purchase multiple products and that each product has multiple sales transactions

Here are some ways that an abstract master data model can help to design and plan an MDM implementation:

1.       Providing a common language for data: A business-first data model ensures that all stakeholders are using the same terminology and definitions for data. This eliminates confusion and misunderstandings when working with data. For example, a bank may have different departments using different terms to refer to the same financial products. A canonical data model can ensure that everyone is using the same terminology when referring to these products.

2.      Enabling data integration: A master data entity model provides a looking glass against source data, using the abstract data model as the bridge between related data in separate sources. For example, a healthcare provider may need to integrate patient data from different systems, including medical records, billing, and customer service.

3.      Simplifying data management: The master data entity model must consider how (and where) end-users will participate in stewardship functions, such as cleansing, matching and merging master data records. For example, a retailer may need to clean and transform product data from multiple suppliers before loading it into a central product catalogue. 

4.      Facilitating MDM tool selection: An abstract master data entity model helps to design and plan an MDM implementation by providing a blueprint for the master data entities and attributes. This can ensure that the MDM implementation is aligned with the organization's business objectives and processes. For example, a global manufacturing company may use an abstract data model to define the standard format and structure for materials data that is required to support their manufacturing process. This helps with identifying appropriate technology choices that can support this process

Critical Data Elements

It is important not to confuse this abstract Master Data Entity Model with the Information model. An Information Model is a standardised model of systems, while the process-focused Master Data Entity Model provides a way to master critical, shared data.

Identify critical data elements supporting these models and the producing and consuming systems

What master data is required, where is it currently stored, and how is it changed?

For example

A telecommunications company had multiple systems and applications that managed customer data. The company realized that inconsistent and inaccurate data was causing delays and errors in business processes. The company decided to implement an MDM solution to improve the quality and consistency of customer data. However, the company faced challenges in designing and planning the MDM implementation, as the data was stored in different formats and structures across different systems and applications.

To address this challenge, the company developed an abstract data model that defined the standard format and structure for customer data. This data model included common customer data entities such as name, address, and contact details, as well as attributes such as data type, length, and format. The canonical data model provided a blueprint for the MDM implementation, which ensured that the MDM solution focused on improving the quality and consistency of customer data. The MDM implementation used the abstract data model as a reference for data integration, data transformation, and data cleansing tasks. The use of a master data entity model helped the company to successfully implement the MDM solution and achieve its business objectives.

Data quality standards

 Defining data and matching standards is an important step in designing and planning an MDM implementation. It involves identifying the data elements that need to be managed as master data, defining their attributes and values, and establishing match criteria for identifying duplicates and inconsistencies. 

Here are some ways that defining data and match standards can help to design and plan an MDM implementation:

1. Ensuring consistency and accuracy of data: Defining data and match standards helps to ensure that master data is consistent and accurate across different systems and applications. It establishes rules for data entry, validation, and verification, which reduces the risk of data errors and inconsistencies. For example, a healthcare provider may define data and match standards for patient demographics, such as name, address, and date of birth. This can ensure that patient data is accurate and consistent across different systems and applications.

2. Facilitating data integration and sharing: Defining data and match standards helps to facilitate data integration and sharing between different systems and applications. It establishes a common language and format for data that can be used across different systems and applications. For example, a financial institution may define data and match standards for customer data, such as name, address, and social security number. This can ensure that customer data is consistent and accurate across different systems and applications, which facilitates data integration and sharing.

3. Improving data quality and reducing data redundancy: Defining match standards helps to identify duplicate and inconsistent data, which can lead to improved data quality and reduced data redundancy. It establishes criteria for identifying duplicate and inconsistent data, which can be used to clean and consolidate data in a manner that will be trusted. For example, a retailer may define match standards for product data, such as product name, description, and SKU. This can help to identify and eliminate duplicate and inconsistent product data, which can improve data quality and reduce data redundancy.

4. Supporting data governance and compliance: Defining data and match standards supports data governance and compliance by establishing rules and policies for managing master data. It establishes rules for data ownership, access, and usage, which can help to ensure compliance with regulatory requirements and industry standards. For example, a pharmaceutical company may define data and match standards for drug data, such as drug names, dosages, and indications. This can help to ensure compliance with regulatory requirements for drug labelling and packaging

Struggling to decide whether you need data quality or full master data management?

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

Conduct an MDM Readiness Assessment on the key master data.

Are there any unpleasant surprises lurking in the data? Data Quality efforts typically take much longer and cost much more than planned.

An MDM Readiness Assessment is vital to understanding and managing the data risk inherent in MDM, defining a data model that will minimise data migration failures, and keeping implementation time scales within scope and budget.

The MDM Readiness Assessment will provide you with critical metadata for planning the most cost-effective data models, migration strategy and implementation plan. Ultimately, data quality is the difference between trusted master data and yet another silo.

Implement Data Quality metrics

Is your master data fit for purpose?

Master data that is of poor quality can quickly spread to consuming systems, degrading the overall quality of data within your business. Data stewards, and other interested stakeholders, should monitor data quality dashboards to ensure that master data remains fit for purpose, and to allow corrective action if it reaches unacceptable levels.

For example: A global manufacturing company had multiple systems and applications that managed product data. The company realized that inconsistent and inaccurate data was causing delays and errors in business processes. The company decided to implement an MDM solution to improve the quality and consistency of product data. However, the company faced challenges in designing and planning the MDM implementation, as the product data was stored in different formats and structures across different systems and applications.

To address this challenge, the company defined data and match standards for product data. The standards included the product name, description, SKU, manufacturer, and pricing information. The company also established match criteria for identifying duplicate and inconsistent product data, which included the product name, SKU, and manufacturer. The data and match standards provided a blueprint for the MDM implementation, which ensured that the MDM solution focused on improving the quality and consistency of product data. The MDM implementation used the data and match standards as a reference for data integration, data transformation, and data cleansing tasks. The use of data and match standards helped the company to successfully implement the MDM solution and achieve its business objectives.

The 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
• An understanding of key stakeholders and their roles
• A master data entity model with source-to-target mapping
• Data quality standards, validations, match rules and risks must be catered for during implementation.

How does technology support MDM?

Get the Whitepaper

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 an MDM solution 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. By aligning the MDM solution with the business objectives, MDM technologies can be selected that best meet the business requirements.

What is the importance of data quality to master data management?

In general, data quality is considered a fundamental aspect of any data management initiative, including Master Data Management (MDM). Data quality refers to the accuracy, completeness, consistency, and reliability of data, while MDM focuses on the governance and management of an organization's critical data assets, often including master data.

In the context of implementing data management practices, it is recommended to prioritize data quality before implementing MDM. This is because MDM relies on accurate and reliable data to be effective. If data quality is poor, MDM efforts can be compromised, leading to erroneous and inconsistent master data.

By first establishing robust data quality processes, organizations can ensure that their data is accurate and reliable. Once data quality measures are in place, MDM initiatives can be implemented to further enhance the management and governance of master data.

In summary, while both data quality and MDM are essential components of effective data management, it is advisable to prioritize data quality before embarking on MDM initiatives.

What are typical MDM architectures?

The four primary MDM implementation styles are:

1. Consolidation: Data is stored centrally with no attempt to clean up distributed systems. It is relatively simple to implement and facilitates enhanced reporting, but does not address underlying data quality issues
2. Registry: A centralised key links related records that remain fragmented.
3. Coexistence: A central hub is used to maintain master data that is distributed to consuming systems
4. Centralised:  Used where master data is authored, stored, and accessed from one or more MDM hubs, either in a workflow or a transaction use case. 

Each model has pros and cons and the correct choice will depend on your circumstances.

For example: A global telecommunications company had multiple systems and applications that managed customer data. The company realized that inconsistent and inaccurate customer data was causing delays and errors in business processes. The company decided to implement an MDM solution to improve the quality and consistency of customer data.

The company first aligned the MDM solution with the business objectives. They identified the key data elements that needed to be managed as master data, such as customer name, address, and contact information. They also established data governance policies for managing customer data, such as data ownership and access.

After aligning the MDM solution with the business objectives, the company selected MDM technologies that best met their requirements. They selected data profiling and cleansing tools to improve data quality, reused existing data integration and ETL tools to integrate data from different systems and applications, and a master data repository to store the master data. They also implemented data governance and security tools to ensure compliance with regulatory requirements.

By selecting MDM technologies after aligning the MDM solution with the business objectives, the company was able to successfully implement the MDM solution and achieve its business objectives of improving the quality and consistency of customer data.

15 years of 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 a number of 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:

• End-to-end strategic program
• Data Governance and Quality for MDM
• Readiness assessment and data migration
• Best-of-breed tools
• MDM Training and Certification

FAQ

What are the common challenges in MDM implementation?

Common challenges in MDM implementation include data governance issues, data quality issues, lack of stakeholder buy-in, insufficient budget and resources, and lack of understanding of the business requirements.

What are some

Some best practices for MDM implementation include creating a clear business case, defining a comprehensive data governance framework, involving stakeholders throughout the implementation process, establishing data quality metrics and monitoring procedures, and selecting an MDM solution that aligns with your organization's needs and requirements.

We address these with our business-first approach which prioritises these areas.

What is a Master Data Readiness Assessment?

A Master Data Readiness Assessment examines the organisation’s master data landscape in the context of the reasons for wanting to implement master data management in the first place and the strategic goals behind those. It is a tool that can be invaluable in determining whether the necessary master data fundamentals are in place prior to a strategic or business-critical initiative that relies on quality master data for its success, or simply for business-as-usual requirements. The assessment evaluates different dimensions of master data management practices across each master data domain in order to determine whether the data meets requirements.

How long does it take to implement an MDM solution?

The time it takes to implement an MDM solution can vary depending on the size and complexity of the organization and the scope of the implementation. Typically, it can take anywhere from six months to two years to implement an MDM solution

What are the different types of MDM solutions?

There are several types of MDM solutions, including customer data integration (CDI), product information management (PIM), and multidomain. CDI focuses on managing customer data, PIM focuses on managing product data, while multidomain solutions are designed to support customer, product and other types of master data with a consistent platform.

Based on the business requirement, one or more of these may be the correct choice.

How do you measure the success of an MDM implementation?

Measuring the success of an MDM implementation involves establishing metrics to assess the effectiveness of the solution. Some common metrics include data accuracy, data completeness, data consistency, data quality, and user adoption. Regular monitoring and reporting on these metrics can help to identify areas for improvement and ensure that the MDM solution is delivering the expected benefits.

User adoption is critical to the success of an MDM solution. To ensure user adoption, it is important to involve stakeholders in the implementation process, provide adequate training and support, and demonstrate the benefits of the solution to users. Clear communication of the purpose and benefits of the MDM solution can also help to encourage user adoption.

Where can I get MDM training?

Our eLearningCurve curriculum in master data management comprises 9 courses leading to a CIMP accreditation in MDM