Clarify the differences among MDM, data integration, and data quality. Delve into our piece on MDM, Data Integration, or Data Quality: What Sets Them Apart to discover how these disciplines contribute to enhancing your organization’s data ecosystem.

Data quality and Master Data Management (MDM) are two critical aspects of data governance that organizations must address. However, determining the right order of implementation between data quality and MDM can be perplexing.

This article aims to shed light on this topic, discussing the relationship between data quality and MDM and exploring whether data quality should come before MDM or vice versa.

Table of Contents

• Understanding Data Quality
• Understanding MDM
• Relationship Between Data Quality and MDM
• The Order of Implementation: Data Quality or MDM?
• Best Practices for Successful Data Quality and MDM Adoption
• Conclusion
• FAQs

Introduction

A 2008 Information Difference survey found that the average company had 6 customer master systems and 9 product master systems.

Larger organisations had many more than this, with 13% of companies having more than 100 systems holding critical master data.

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. 

In larger and more complex environments a full MDM platform can simplify the sharing of data across the enterprise and provide a front end for master data maintenance. 

Understanding Data Quality

Importance of Data Quality

Data quality refers to the accuracy, completeness, consistency, and reliability of data. High-quality data is crucial for informed decision-making, business operations, and compliance. Poor data quality can lead to errors, inefficiencies, and wrong conclusions. Therefore, organizations must prioritize data quality to derive value from their data assets.

Common Data Quality Issues

Data quality issues can arise from various sources, including data entry errors, duplication, inconsistent formats, and missing or outdated information. These issues can result in redundant data, conflicting records, and unreliable insights. Understanding and addressing these common data quality issues is essential to maintain data integrity.

Master Data Management (MDM)

What is MDM?

MDM is a comprehensive approach to managing an organization’s critical data assets, known as master data. Master data includes essential entities such as customers, products, employees, and suppliers. MDM aims to create a single, reliable, and consistent version of master data across the enterprise. It involves data governance, data stewardship, data integration, and data quality.

Benefits of MDM

Implementing MDM offers several benefits to organizations. It enables efficient data sharing, improves data accuracy and consistency, enhances decision-making, supports regulatory compliance, and facilitates data-driven initiatives like analytics and personalization. MDM acts as a foundation for achieving data governance and data quality objectives.

Relationship Between Data Quality and MDM

Data Quality as a Prerequisite for MDM

Before implementing MDM, organizations need to ensure a solid foundation of data quality. MDM relies on accurate and reliable data for successful consolidation, harmonization, and distribution of master data. Without addressing data quality issues upfront, MDM initiatives may face challenges in achieving the desired outcomes.

MDM as a Solution for Data Quality Issues

On the other hand, MDM can serve as a solution to address data quality issues. By implementing MDM, organizations can establish data governance policies, data stewardship roles, and data quality controls. MDM provides a framework to standardize data formats, eliminate duplicates, enforce data validation rules, and improve data consistency. These measures contribute to enhancing overall data quality.

The Order of Implementation: Data Quality or MDM?

Now, the question arises: Which should come first, data quality or MDM? The answer depends on the specific circumstances and organizational goals.

To use an analogy. data quality is the horse to the master data management cart. For a while, the horse will be able to carry the required load. Eventually, the load may become too much for the horse and a full Master Data Management solution may be required. 

In this case, the data quality “horse” will continue to provide the pulling power that allows the cart to function.

On the other hand, the expensive cart is fundamentally useless without a functional horse.

This position is summarised by Aberdeen Research analyst, Nathaniel Rowe. “If you put MDM in place but you’re using old, substandard data, you won’t see much value from the effort,” he said. “You’ll have issues with the data if it isn’t standardized.”

 “If you only have the budget to do data quality, that’s more important, but keep looking toward the horizon for the next step,”

Nathaniel Rowe

Data quality should not just be a tick box in the MDM stack. Data quality management is a complex problem that is made worse when multiple sources of data are consolidated for MDM.  Data quality should be assessed independently of MDM to ensure that you have a solution that is appropriate for your needs.

We have delivered a number of MDM projects with varying architectures, depending largely on what could be leveraged within the existing environment.

The common factor in each project was to ensure that the underlying data was able to support the business requirement.

By blending a business and data focus we deliver incremental benefits that justify the existing spending and build the business case for additional phases.

Let’s explore both scenarios:

Implementing Data Quality before MDM

In this approach, organizations prioritize addressing data quality issues before embarking on MDM initiatives. By thoroughly assessing and improving data quality, organizations ensure that the foundation for MDM implementation is solid. This approach helps prevent potential challenges that may arise during MDM implementation due to poor data quality. By prioritizing data quality, organizations can establish a reliable data infrastructure that supports accurate and meaningful master data.

Implementing MDM before Data Quality

Alternatively, some organizations choose to implement MDM first and address data quality issues within the MDM framework. By consolidating and harmonizing master data through MDM, organizations gain a better understanding of their data landscape. MDM provides a structured approach to identify and rectify data quality issues, leveraging data governance and stewardship capabilities. This approach allows organizations to achieve simultaneous progress in both MDM and data quality improvement.

Best Practices for Successful Data Quality and MDM Adoption

Regardless of the order of implementation, organizations should follow best practices to ensure successful adoption of data quality and MDM. Here are some key practices to consider:

Establishing a Data Quality Framework

• Define data quality metrics and standards based on business requirements.
• Implement data profiling and data cleansing processes to identify and rectify data quality issues.
• Establish data quality monitoring mechanisms to continually assess and maintain data quality.

Building a Robust MDM Strategy

• Clearly define the scope and objectives of the MDM initiative.
• Involve stakeholders from various departments to ensure comprehensive master data coverage.
• Develop data governance policies and data stewardship roles to support MDM governance.

Conclusion

In conclusion, both data quality and MDM are essential components of effective data management. While data quality acts as a prerequisite for successful MDM implementation, MDM also contributes to improving data quality by providing a framework for data governance and stewardship. The order of implementation, whether prioritizing data quality or MDM, depends on organizational needs and goals. By following best practices and considering the specific context, organizations can achieve optimal results in data quality and MDM adoption.

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FAQs

Is data quality the sole responsibility of the IT department?

No, ensuring data quality is a shared responsibility that involves various stakeholders, including business users, data analysts, and data stewards. Collaboration between IT and business departments is crucial for effective data quality management.

Can MDM be implemented without addressing data quality?

While it is possible to implement MDM without explicitly addressing data quality, it is generally recommended to establish a solid foundation of data quality before embarking on MDM initiatives to ensure optimal outcomes.

How long does it take to implement data quality and MDM initiatives?

The implementation timeline for data quality and MDM initiatives varies depending on the organization’s size, complexity, existing data infrastructure, and goals. It can range from several months to a year or more. Proper planning, resource allocation, and stakeholder involvement are key factors that influence the duration of implementation.

What are the potential challenges in implementing data quality and MDM initiatives?

Some common challenges include data silos, resistance to change, lack of data governance framework, inadequate data quality assessment, and integration complexities. It is crucial to address these challenges proactively through comprehensive planning and stakeholder engagement.

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