Strong insights have always been important for business management. In today’s era, the increasing amount of accessible data generated every day in modern businesses is overwhelming. Therefore, the need for a way to structure, understand and use this data becomes increasingly important to business leaders. Not only can quality data inform efficient decision-making, but it can also drive exponential business growth when it’s managed effectively.

In fact, it’s estimated that more than 33% of enterprises worldwide will have practiced data-driven decision-making at some point by the end of 2023. And it’s increasingly easy to see why so many businesses are adopting the business intelligence approach. Data analytics alone can enable organizations to make decisions up to five times faster than without it, potentially saving businesses significantly in terms of time, resources, and cost.

Businesses that are not able to make decisions based on data will fall behind the competition and eventually fail. 

However, data is all about quality. To ensure value and data usability, businesses must practice effective data management. There are two paths that can be taken here, ‘standard’ or ‘general’ data management, or master data management. These are two different approaches to organizing and maintaining data within the business, and they have some distinct differences. 

In this article, we will explore the differences between general data management and master data management, and why having a MDM (Master Data Management) framework in place is crucial for businesses.

Data management is a critical component of any organization's operations, as it ensures that any data being used is accurate, reliable, and quickly available when needed. The whole process of data management includes several key activities, such as data architecture, data modeling, data integration, data security, and data quality. Each of these activities plays an essential role in ensuring that data is managed properly and that it can be used effectively by the organization, like supporting decision-making, for example. 

Perhaps one of the most important aspects of general data management is data architecture. This refers to the overall structure and organization of the data and includes things like data modeling and data integration. Data modeling involves creating a logical representation of the data, which is used to ensure that the data is organized in a way that is easy for the end user to understand and leverage. Data integration, on the other hand, refers to the process of combining data from different sources into a single, cohesive whole – this is where the term ‘a single source of truth’ comes into play. Data integration provides the means for businesses to have a central repository for all data. This is essential for organizations that have data sets spread across multiple internal or external systems, as it ensures that the data can be easily accessed and analyzed.

Another important aspect of data management is data security. This is especially important in today's digital age, as data breaches and cyber attacks are becoming increasingly common. Organizations need to ensure that their data is protected from unauthorized access and that any breaches are detected and addressed in a timely manner. Functions such as access and admin controls, encryption, and incident response plans are all part of today’s data security expectations and demands. 

Data quality is also a key component of data management, referring to the accuracy, completeness, and consistency of data. Data quality is essential for effective data usability, to ensure that any insights or decisions made based on the data are accurate. Processes and controls must be put in place to make sure that data is entered and updated correctly, and that any issues are identified and rectified quickly.

Overall, data management is essential for organizations of all shapes and sizes and across all industries. It enables businesses to effectively use their data for various purposes, such as reporting, analytics, and perhaps more importantly, decision-making. 

Master Data Management (MDM) is a discipline within data management that focuses on the critical data assets of an organization, such as accounts, vendors, employees, products, and other operational data. It’s where the business partners up with its technology to facilitate uniformity and accuracy of the master data assets of the organization. It refers to the non-transactional data that is shared across the organization and forms the basis for all business transactions and processes. This data is typically utilized by multiple departments and team members within the organization and is often critical for decision-making and business operations purposes.

The goal of MDM is to create a single, accurate, and consistent version of the master data that is shared across the organization, to instill consistency across the board, and facilitate that single source of truth for data that everyone can view and use. This single source of truth is achieved through a combination of data governance, data quality, data integration, and data modeling. This is sometimes also referred to as a “golden record”.

Data governance is a critical aspect of MDM, as it ensures that the master data is managed and controlled in a consistent and effective way. Data policies, data ownership, and data stewardship are all considered. Data quality is also important, as it ensures that the master data is accurate, complete, and consistent via practices such as data validation, data cleansing, and data matching. Like general data management, data integration is also an important aspect of MDM, it makes the master data easily accessible and available to others in the organization. Within the context of MDM, data integration encompasses data mapping, data warehousing, and data federation. Data modeling also ensures that the master data is organized in a way that allows easy accessibility and usability.

Throughout its lifecycle, the MDM process also involves maintaining the integrity, accuracy, and governance of the master data. Ongoing data quality checks, regular data updates, and ensuring compliance with data governance policies and standards are all part of the process. In today's fast-paced business environment, where data is constantly changing, and new data sources are continuously being added, this is increasingly important. 

With MDM in place, organizations can be confident that their master data is accurate, reliable, and accessible, which ultimately helps to drive the business forward.

The main difference between data management and master data management is the scope and focus of the activities involved.

While general data management covers a wide range of tasks and data types, MDM is specifically focused on the critical master data assets of the organization, such as customer, vendor, and product data.

In essence, data management tends to be centered around the data platform, solving issues and problems that arise such as the data pipeline or data security breaches. MDM, on the other hand, looks at how those issues and problems can be prevented from occurring in the first place, by proactively monitoring and managing the master data. 

A higher level of governance and control is also required for MDM compared to data management. The integrity and accuracy of the master data have a direct impact on the overall health and performance of a business. It is the very foundation of all business operations and reports, and any errors or inconsistencies in the data can lead to serious financial losses, due to incorrect or delayed business decisions which all impact the bottom line. The level-up required for MDM can be a turn-off for organizations due to the extra time, resources, and cost that can run alongside it. However, for organizations that are striving to improve their business operations, MDM is the next logical step.

The master data management market is set to reach USD$34.5 billion by 2027 according to recent research, marking a CAGR of 15.7% from 2022. This growth is due to various factors such as a rise in demand for data quality and data management tools and services, combined with growing regulatory compliance standards that businesses must adhere to.

So, why is master data management so important?

We already know that MDM is a critical component of any organization's operations, as it ensures the quality and integrity of its master data. Now let’s look at what happens if you have poor-quality master data. There is a range of issues that can occur here, such as incorrect reporting, flawed analysis, and incorrect decision-making, which can have serious business implications. This can then lead to reputational damage or financial losses that severely impact the bottom line. With MDM in place, businesses can rest assured knowing that the organization is running smoothly and effectively with the help of accurate master data. 

MDM also helps to improve operational efficiency and employee productivity by eliminating the need for manual data reconciliation and enabling the automation of business processes. By having a single version of truth for all master data, organizations can avoid the need for multiple departments to maintain their own copies of the same data, which can lead to discrepancies, errors, and multiple versions of the same data being shared around. This can also help to reduce the amount of time and effort that employees need to spend on data-related tasks, which can improve productivity and free up time for more value-added tasks.

Regulatory compliance is greatly supported with MDM, as the accuracy and completeness of the master data are ensured. With the increasing number of regulations and data privacy laws, organizations need to ensure that they are able to demonstrate that they are in compliance with these regulations. Being able to provide accurate and complete information about their customers, products, and vendors is vital. MDM helps to ensure that the master data is accurate and complete, which in turn helps to ensure that the organization is in compliance with these rules and regulations.

Lastly, MDM also facilitates data-driven initiatives, such as data analytics and artificial intelligence, by providing a solid foundation of clean and consistent data that can be used for multiple purposes. With the increasing importance of data-driven decision-making, organizations need to ensure that they have access to accurate and consistent data that can be used for analytics and AI. MDM facilitates master data that is accurate, complete, and consistent, which in turn helps to ensure that the organization has access to the data that it needs to make informed decisions and drive its business forward.

So, there we have it. While data management and master data management are essentially cut from the same cloth, they’re being used to create two different outcomes. Data management is being used to create the day-to-day scarf that can be worn by any type of data, and master data management is the tailored suit that only fits critical master data. 

Having a robust MDM framework in place is crucial for businesses to ensure the quality and integrity of their master data, improve operational efficiency, support regulatory compliance, and enable data-driven initiatives.

Are you looking to venture down the path of master data management? Reach out and get a demo of how easy it can be to get started with a tool like Exmon.