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What is Master Data Management (MDM) and How Does it Work?
A comprehensive guide to overcoming your company’s trickiest data record challenges. We deep dive into master data management: what it is, how it works, the benefits and the challenges. We also provide tips on how to leverage master data management.
In today’s digital landscape, businesses are easily inundated with vast amounts of data from various sources. Maintaining the integrity and quality of this data can feel challenging, especially when it’s fragmented or siloed.
That’s where Master Data Management (MDM) comes in. MDM plays a pivotal role in achieving data consistency and reliability across organizational functions. After all, quality and reliable data is not only the cornerstone of effective decision-making, it can be a critical asset for competitive advantage.
What is master data?
Master data represents the core data about an organization’s entities typically used to provide essential business context. What an organization considers master data will vary depending on the business model, although most will agree that customer and supplier data are one of the best examples.
Master data might include information related to people and companies, but also products, suppliers, or locations, and could be stored across various IT systems and platforms. This can be problematic for a number of reasons, including:
Conflicting or duplicate information
Outdated information
Inconsistent data attributes
Data in different formats
Lack of real-time synchronization between sources
Challenges to connect data across systems
This can cause issues for the integrity of master data, and is why MDM should be a key part of data management strategy. It’s a strategic discipline that focuses on creating and maintaining a cohesive and accurate set of master data from all areas within an organization. MDM involves processes for consolidating, cleaning, and standardizing data so that businesses have a single source of truth.
Let’s take a couple of examples of master data to outline why it’s so important to manage:
Customer information
If a customer updates their address on your systems, this data change should be spread across sources. Otherwise problems might be encountered if they make an order and the sales and shipping systems have different details. To maintain good customer satisfaction and maintain business continuity, this source of master data needs to be managed.
Product pricing
If you’re running a discount and one team updates a product’s price on the sale system but it’s not reflected on the website, customers may be confused or frustrated. Such inconsistencies can cause problems for organizations, so product data is another source to maintain.
Regulatory issues
Regulatory standards are becoming more stringent. And while there are many different kinds of standards, the issue at the heart of most is data and how it’s governed. Specifically: data quality, reliability, safe usage, consistency and control. To stay compliant, many companies fulfill the minimum requirements of the standard – but then find themselves struggling when they realize that their efforts failed to address critical data issues at the source.
Operational inefficiency
Without a true view of data, your data teams are overloaded with manual tasks to get even the most simple analytics done and miss out on innovations such as AI.
How does master data management work?
In order for MDM to work effectively, there are some key elements to get right. These are:
Data matching
Simply put, all data from different sources needs to be combined into a unified view. It’s the starting point for most MDM projects.
The disparate systems used by businesses might include enterprise resource planning (ERP) systems, customer relationship management (CRM) programs, billing systems, MarTech and product information management (PIM) tools. Successful MDM will not only consolidate data from all these sources, but will resolve the data into entities.
Data quality
For successful MDM, the data needs to be of high quality, but common issues include duplicates, inconsistencies, and inaccuracies. The best MDM processes and tools use entity resolution to clean, validate, and enrich master data. This means stringent data quality standards can be upheld and organizations can trust the data.
Data governance
All the policies, standards, and procedures that dictate how master data is managed, utilized, and protected within an organization can be referred to as its governance.
Governance is essential for accountability, ensuring data is handled ethically and that processes comply with regulatory requirements. Effective data governance will include:
Defining roles and responsibilities
Setting data stewardship protocols
Implementing access controls to maintain data security
Data synchronization
To ensure master data remains consistent and up to date, data needs to be synchronized. If any changes occur, they should be reflected in the data. By synchronizing data across various platforms, organizations can enhance operational efficiency, reduce the risk of errors, and ensure that all stakeholders have access to the most current information.
However, the types of MDM aren’t always real-time – it depends on the implementation style. There are four main types:
This is when all data sources go into a centralized repository. The data is cleaned and consolidated in this repository, so the original data isn’t altered. Instead, changes to the master data continue to be made through the MDM system, designed to spot duplicates and other issues in data from various source systems. This approach is great if you have a large number of data sources and want to establish one authoritative source (without overwriting information in the originals).
In this model, all data is consolidated into what’s known as a ‘golden record’ of all master data. This is then stored in the central hub and used for reporting and reference. Any updates or changes made to the master data in a consolidation model are then applied to the original sources too.
A golden record is established in a similar way to consolidation, but master data changes can happen in both the centralized MDM system and the source data. So data can be managed at source, but synchronized in the hub to still offer a single source of truth in real-time.
With this approach, the central MDM hub is the true record or origin of data – rather than providing a view of data stored elsewhere. It’s the centralized repository where data is managed and enhanced, and other data sources receive updates from it to maintain consistency.
Data stewardship
Another key element is the role of overseeing the management and quality of master data within an organization – the data stewardship. By establishing dedicated roles, organizations can enhance accountability and drive better data management practices. But the key parts of stewardship, such as monitoring data usage and collaborating with other departments, can be fulfilled without the need for dedicated roles. It can also be achieved by empowering all employees to do their bit to keep master data accurate and relevant.
Gain control of your data
What are the benefits of master data management?
When established correctly, all models of MDM offer a range of benefits. Just some of these include:
Greater operational efficiency
MDM has the potential to transform an organization's operational landscape. This is predominantly by reducing the time spent on data reconciliation and error correction, so that operations can be streamlined and teams can focus on core business activities.
Potential cost reductions
Linked to above, businesses might be able to save money on operational costs associated with data handling, particularly when it comes to managing data discrepancies.
Improved customer experience
Targeted marketing campaigns, personalized product offerings, and improved customer service can all be achieved through MDM and the benefits of maintaining a unified view of customer data.
Increased confidence in business decisions
With a comprehensive view of critical business entities and their data, stakeholders can make informed decisions. They can also identify trends, assess performance, and forecast future outcomes with greater confidence.
What are the main challenges with master data management?
Unfortunately, the path to effective MDM can be fraught with challenges that hinder progress and compromise the integrity of data. Just some of the main issues to overcome are:
Accuracy
AccuracyUnreliable data can stem from various sources, including human error during data entry, system integration issues, or outdated information. To maintain data integrity, organizations need robust processes for data cleansing and validation, which isn’t always straightforward.
Time and technology requirements
Time and technology requirementsIntegrating disparate data sources is also complex and requires technical expertise and resources. The challenge is exacerbated in a globalized environment, as well as if organizations have to navigate various data formats, systems, and protocols – however, for the most complex of businesses, the benefits are even greater.
Resistance from employees
Resistance from employeesWe all get accustomed to existing processes and systems, so it can be difficult to change. It’s important to manage this and promote the benefits of a culture of data-driven decision-making which MDM supports. This is crucial to overcoming any resistance and ensuring that all stakeholders understand the value. Training and education can further facilitate this transition and gain support from departments.
Regulatory compliance
Regulatory complianceAdding yet another layer of complexity to MDM, organizations face increasing scrutiny regarding how they handle and protect data. It’s essential to adhere to data governance policies and regulations, so any MDM solution must provide reliable audit trails and facilitate compliance by ensuring that data is maintained and readily accessible for review.
Tips on how to leverage master data management
Implementing a well-executed MDM strategy is a multifaceted endeavor that requires a blend of technology, processes, and stakeholder involvement. To set yourself up for success, consider the following advice:
1. Establish a strong data governance framework
At the heart of a successful MDM strategy lies a solid data governance framework. This involves defining roles, responsibilities, and policies for managing data across the organization. It should clearly outline data ownership, accountability, and processes for maintaining quality; clarity over these elements can help foster a culture of data stewardship where data is treated as a valuable corporate asset.
Organizations also have to decide what framework they want for permissions over master data. It could follow a:
Hybrid architecture. You may permit certain changes to stored data to be made by different users.
Repository architecture. Storing all enterprise-level data in one centralized location – and allowing all relevant departments to make use of it – is the best way to maximize the benefits of MDM.
Registry architecture. Giving the majority of users read-only permissions can prevent unwanted modifications, but it may slow down some of the efficiencies of MDM and restrict the benefits to a few.
2. Identify and involve key stakeholders
It’s important to talk to those who work closely with master data and get their buy-in. After all, this early engagement with key stakeholders ensures their needs and concerns are addressed in the MDM strategy. By involving representatives from sales, marketing, finance, and operations, you can better understand the challenges faced and tailor processes accordingly.
What’s more, regular communication throughout MDM implementation can help build trust and encourage collaboration, reducing resistance to changes in data management practices.
3. Take it slow and steady
Rather than attempting to deploy a comprehensive MDM solution all at once, you can focus on incremental implementation and continuous improvement. Having defined and manageable phases of the implementation helps everyone get short-term benefits while working toward a long-term vision. Each phase should focus on specific business objectives and deliver tangible results, such as improved data quality or enhanced reporting capabilities.
4. Invest in the right technology
To support MDM efforts, it is essential to invest in the appropriate technology stack. An effective MDM solution should include capabilities for data integration, data quality, and data governance. That’s where Quantexa can help with automation features for data cleansing and reconciliation, significantly enhancing data quality and operational efficiency. We help organizations to empower their teams to leverage data more effectively. Here’s just an example of what we can achieve with our data management tools:
60x faster data resolution for rapid time to value
99% accuracy of data matching through Entity Resolution
20% reduction in records through deduplication
5. Focus on managing data quality
Data quality is the cornerstone of successful MDM, so it’s no surprise we’ve touched on quality throughout. But how do businesses avoid poor quality data? Robust data quality management practices, which involve regular monitoring, profiling, and cleansing of master data, should be established.
By implementing data quality metrics, businesses can assess the health of their data and drive initiatives to rectify any deficiencies.
6. Provide training and support
Finally, any strategy should recognize the importance of user training and ongoing support. Providing employees with the necessary training on MDM processes and tools ensures that they can effectively contribute to data management efforts.
Having established channels for continuous support, such as help desks or dedicated personnel, to assist users with any challenges they may encounter can also help. It’s all about fostering a knowledgeable user base to enhance engagement with MDM initiatives.
MDM not only addresses the challenges associated with data quality and integrity but also lays the groundwork for advanced analytics, business intelligence, and customer engagement initiatives.
Companies should approach MDM in a way which best suits their business needs, but complex or growing organizations can really benefit from establishing processes sooner rather than later. A data management solution can help solve some of the most critical data challenges and unlock significant value from business insights.
Master data mangement FAQs
What is the MDM framework?
A master data management framework simply refers to the defined processes and structured methods a company chooses to collect, manage, organize and distribute data.
What is the difference between ETL and MDM?
ETL (Extract, Transform, Load) and MDM (Master Data Management) are both related to data management, and can complement each other.
ETL focuses on processes which improve how suitable data is for use – particularly how it can be best prepared for analysis. ETL tools help organizations properly extract data from various sources and transform the format so it’s integrated and ready for use in reporting. MDM, on the other hand, is about a central source of master data, and how that is managed and maintained.
What are the stages of MDM?
All MDM implementations will vary, but typically follow similar stages. These are:
Identifying all sources of master data and who (whether individuals, vendors or third parties) is involved in producing that data
Establishing a method for collecting the data
Matching the data
Choosing a data governance framework
Utilizing a MDM model and tool that offers the right data infrastructure
Testing the system
Maintaining and modifying as needed
What is the difference between a SCV and MDM?
Single Customer View (SCV) and Master Data Management (MDM) can both help businesses gain comprehensive understanding of customers.
MDM is a robust set of processes for data consistency across the organization. One way of building a SCV is by using an MDM solution because it can aggregate data from different sources and provide a practical overview of customer information. This would provide a SCV – a single record of customer or prospect data.