The implementation style of master data management in your business is primarily determined by the organizational structure and business requirements. Master data management (MDM) is intended to eliminate functional data silos, and with the latest technological advances, it is now possible to access clean, validated data that caters to multiple domains in real time. Considering advanced MDM implementation styles, businesses can leverage the maximum potential of Master Data Management.
One major challenge to breaking down silos in a business is not always technology, but rather, company culture. The boardroom needs to have confidence in the security and reliability of the data, while also ensuring that a cross-enterprise MDM will meet the specific needs of each team. Different MDM implementation styles and uses can be tailored to address the trust level within a company’s culture and facilitate successful business transformation.
Our company offers a comprehensive master data management platform that can accommodate large data sets for various industries and usage scenarios. We have extensive experience in different MDM implementation styles and have identified four primary implementation styles and usage scenarios. Each of those 4 approaches has its own set of pros and cons.
The primary difference between these styles is whether data is centralized or synchronized across multiple hubs. Choosing the right MDM implementation style depends on your specific needs and goals. In this article, we will discuss each style in detail to help you make an informed decision.
Challenges in Managing Master Data in Scattered Systems & How MDM Solves it?
MDM implementation guarantees the uniformity, precision, and timeliness of all essential data within an organization’s IT infrastructure. It encourages effective teamwork between business and IT departments, simplifies intricate data management protocols, and enhances data integration efficiency.
Organizations that have adopted Master Data Management can confidently rely on accurate information to make informed decisions, resulting in significant advancements in their overall business outcomes
A significant amount of organizations have been collecting master data on their customers, products, and suppliers. However, a considerable number of them are facing difficulties in efficiently managing it.
According to the study report conducted by Stibo Systems and the Aberdeen Group, about 45% of businesses around the globe are struggling to identify their master data. This situation has been causing various issues with severe business implications. The seven major challenges associated with MDM are as follows:
1. Data Silos
The utilization of various versions of data sources both within and outside an organization can lead to duplicated, erroneous, and inefficient data. This challenge is often perpetuated by the use of unconnected legacy systems to maintain important data. As a result, there is a need for a more integrated and streamlined approach to data management to ensure accuracy and efficiency across the organization.
2. Data Quality
The usage of inconsistent, incomplete, and erroneous data within a business can have adverse effects on its overall performance. Furthermore, when such unstructured data is pushed to various channels, it can pose an obstacle to entering new marketplaces. It is crucial to ensure that data is accurate and complete before dissemination to avoid any hindrance in business operations.
3. Data Errors
Due to the input of data manually and data sources with low quality, these may appear across various channels including your ERP, CRM, catalogs, and webshop. As a consequence, your team’s cooperation, customer service, product returns, and communication may face negative outcomes.
4. Data Syndication
The absence of proper supervision over the distribution of data to various channels may lead to inconsistencies in product information across different retail or partner platforms.
5. Loss of Trust
The identification of outdated or incorrect elements within data can prove to be a challenging task. In the product life cycle, traceability often remains low, further complicating matters.
6. Customer Experience
The absence of a unified and consistent master database can lead to inconsistent customer interactions across different channels. As a result, providing an omnichannel experience becomes a challenging task.
7. Lack of Data Governance
The absence of a centralized data system poses a significant challenge to data governance. This can hinder an organization’s ability to comply with regulatory requirements and safety regulations and may compromise its overall effectiveness.
To ensure regulatory compliance and mitigate risks, it is essential to establish a comprehensive data management framework that centralizes data and enables efficient tracking and control. By doing so, organizations can better protect their data and achieve greater operational efficiency.
One of the main reasons for facing challenges in MDM implementation styles is the absence of a centralized database that provides a reliable and consistent single version of the truth. However, Master Data Management (MDM) can address these issues by creating a master data record that serves as a single source of truth throughout the organization.
This approach helps to enhance the accuracy, traceability, and consistency of data, thereby minimizing challenges associated with data management.
How MDM Solves Your Data Challenges?
Low-quality data can have a negative impact on various business processes and functions. The issue extends beyond just product data for online stores. Master data encompasses a broad range of information used to identify customers, suppliers, products, locations, and other organizational assets.
Adopting Master Data Management (MDM) solutions to improve data quality can unlock a company’s full potential beyond tackling operational challenges. MDM can enable companies to achieve strategic business goals, accelerate innovation, and generate business value.
This is accomplished through a range of benefits provided by MDM, including:
- One effective strategy for optimizing business processes is to break down data silos and establish connections between customer, product, supplier, and asset data. This can improve overall efficiency and enable more informed decision-making across the organization.
- One effective approach to ensure data accuracy and consistency is by creating a central repository for all your data, also known as a single source of truth.
- Improving data quality is essential for any organization as it helps to minimize data errors and mitigate associated risks. By reducing data errors, the organization can minimize the risk of making poor decisions or taking actions based on inaccurate data.
Why is Effective Management of Master Data Necessary?
As businesses get digital transformation, the enterprise-wide master data grows steeply. It becomes a big challenge for businesses to create a uniform set of identifiers for their master data to maintain its quality across the departments. This is where the need for master data management becomes necessary. Effective management of the master data is the key to succeeding in today’s evolving market condition and meeting the rising demands of modern consumers.
In addition, here are a few factors that illustrate the effectiveness of managing master data.
Master data is an essential element in making informed business decisions. Maintaining clean, reliable, and current master and hierarchy data throughout your analytical systems can enhance operational agility and reporting accuracy.
The result is the ability to make smarter and faster decisions, leading to improved business responsiveness. A flexible solution for managing master data can reduce costly IT and business transactions significantly.
Master Data Management (MDM) plays a crucial role in accelerating business growth. With accurate and reliable customer data, you can enhance your sales, service, and marketing efforts. By consolidating your master data into a single source of truth, including information on customers, products, and services, you can gain a deeper understanding of customer needs and proactively address them.
A streamlined master data system can also provide a competitive advantage by facilitating faster onboarding of new products and capturing more revenue opportunities throughout the supply chain.
To successfully leverage digital technology and achieve business transformation, enterprises need a cohesive strategy that includes a unified semantic data model for their primary master data objects. By implementing a well-managed master data system, enterprises can reduce the burden on their IT team and minimize costs associated with organizational integration.
This approach also helps to eliminate organizational barriers that might hinder information reuse. Ultimately, a centralized master data system can enable enterprises to open up new channels, expand customer touchpoints, enter new markets, and innovate the customer experience.
What is Master Data Management?
In the world of modern business, it is essential to have accurate, reliable data at your fingertips. To achieve this, companies are increasingly turning to Master Data Management (MDM). This process involves consolidating all data related to people, places, and things within a business – whether from internal or external sources – into a single, master record.
Once the data has been de-duplicated, reconciled, and enriched, it becomes a consistent, trustworthy source of information.
This helps to reduce data errors, cut down on redundancy, and promote accurate reporting. By creating a single source of truth, MDM enables workers to make better-informed decisions based on accurate data.
With access to this information, businesses can streamline their operations, improve their efficiency, and ultimately achieve greater success. So if you’re looking to improve your business operations, consider master data management implementation styles.
To explore more about MDM, you must read What is Master Data Management (MDM) and Master Data Management – An Ultimate Guide. These posts help you get exclusive insights into the different aspects and capabilities of a Master Data Management platform.
What are the Popular Use Cases of Master Data Management?
In the current business world, there are so many MDM use cases that can be referred to. However, the use case of master data management truly depends upon the business need and requirements. Among many MDM use cases, here are the lists of the 3 most common use cases briefly: Take a look.
1. Operational MDM
Operational MDM is a crucial aspect of data management that involves establishing MDM at the source of master data. This ensures that data consistency is maintained throughout the enterprise, thereby preserving the integrity of business processes.
Currently, two distinct areas have emerged within MDM: MDM for product data (also known as product data information management or PIM) and MDM for customer data. These domains have evolved significantly and are now addressed through a combination of single-domain-centric and multidomain MDM offerings. Operational MDM focuses on systems, applications, and purposes that are critical to the enterprise.
2. Analytical MDM
Analytical Master Data Management (MDM) is a set of skills, tools, and technologies used to improve data quality and accessibility. It is specifically designed to support business intelligence, reporting, and analytics.
Unlike operational MDM, which aims to fix data at its source, analytical MDM focuses on measuring business performance. It is deployed downstream of transactional or operational systems and is a critical component of business intelligence (BI) implementations. Its primary purpose is to provide high-quality data to support downstream BI requirements.
In recent years, there has been a notable increase in the interest in compliance management. Compliance data management refers to the process of collecting, organizing, and maintaining compliance data, which includes regulatory requirements, internal policies and procedures, and audits.
3. Compliance MDM
One of the primary benefits of compliance data management is the reduction of compliance risks. By storing accurate and complete compliance data, businesses can identify potential compliance issues and take preventative measures to mitigate them.
This reduces the risk of storing incorrect data and can help businesses avoid non-compliance fines and reputational damage. Overall, compliance data management is a valuable tool that can help businesses maintain compliance and minimize risks.
Compliance data management provides businesses with the ability to improve their compliance reporting, which is a significant benefit. By keeping accurate and comprehensive compliance data, organizations can produce more precise compliance reports. Those reports can be utilized to inform decision-making processes.
This, in turn, enables companies to fulfill their obligations and enhance their compliance performance, thereby ensuring that they are meeting their compliance requirements effectively.
Master Data Management Implementation Styles – Why are they Needed?
Implementing master data management is a critical factor in ensuring the success of MDM solutions. The implementation style used is a vital component in designing the MDM system, whether you opt for building component-based systems or utilizing a purchased platform.
A well-executed implementation of Master Data Management (MDM) can significantly enhance the quality and consistency of your master data, even in complex environments. It can also support operational and decision-making environments, facilitate the distribution of clean data to existing systems, and enable the building of service-oriented architecture (SOA) fabric.
To meet unique MDM requirements, different implementation styles have emerged. The most commonly used styles are Consolidation, Registry, co-existence, and Centralized.
Benefits of Considering MDM Implementation Styles
Implementing master data management can be a complex process for organizations. Choosing the appropriate MDM approach can be challenging as it depends on specific business requirements. For instance, some organizations require MDM for product information management to support global product data synchronization or supply chain management.
Conversely, other organizations need MDM for customer data applications to achieve customer-centric objectives. Ultimately, the goal is to provide businesses with a singular view of their master data, which can be tailored to various needs.
- Improving data quality will also enhance the overall efficiency and effectiveness of business operations
- To improve the efficiency and effectiveness of your organization’s data management
- Enhance data governance
- Improves stewardship
- Increases security capabilities
- Improves effectiveness and agility of the master data across enterprises
- Offers a more engaging customer experience and creates better upsell/cross-sell opportunities
- Provides deeper and more valuable business insights and speeds up the decision-making process
- Reduce operational costs and system costs.
4 Common Master Data Management Styles
For all organizations maintaining a single version of the truth of data alongside meeting regulatory obligations and compliance would be the foremost thing to focus on. In addition, enterprises would be given priority to improve their data quality, workflow process, and meeting the guidelines for data governance. Also, the primary goal of an organization would be ensuring their high-quality data is easily managed by the team and can be accessed promptly by various departments across the business.
Well, the above-mentioned factors can be easily achieved with the proper implementation of MDM by using common implementation styles. The type of MDM deployment determines how the system will impact your business core, and structure and assist the business to achieve goals.
1. MDM Implementation Style 1: Registry
This registry MDM implementation style works perfectly for an organization having large or multiple data sources, its own set of rules, and a structure for its business operation. The implementation of MDM using registry style is mainly used to identify the data duplicates by running data cleansing and various data matching algorithms.
The registry style of MDM implementation uses global identifiers for data using which they match records and identify the single version of the truth. This leads to the elimination of data duplicity. This registry style of MDM implementation makes changes to the existing data source and manages the data quality on its own. It offers an up-to-date view of the products by indexing the master data in real time.
Benefits of Registry MDM Implementation Style
Take a look over the benefits an organization can get by using the registry-style implementation of the MDM system.
- The data analysis process carried out in the registry-style approach eliminates the risk of overwriting the information in the data source.
- As the data source is unchanged in the registry style implementation, organizations can avoid compliance failure and other regulatory repercussions which may differ from country to country.
- The read-only view of data provided by the registry style helps businesses to remove data duplicity and gain consistent access to data.
- The cost of using registry-style MDM implementation is low and helps businesses to do rapid data integrations with minimal interruption of the application system.
2. MDM Implementation Style 2: Consolidation
When it comes to the consolidated style of MDM implementation, it is almost similar to the previous registry style except for the addition of data stewardship. In consolidation style, the source systems feed the data directly to the centralized data repository in a consolidated form. This creates a single version of the truth, also known as the golden record.
A golden record created by the consolidation style stores data in the central hub. The data stored in the hub is mainly used for report generation and reference. One thing you should note is that in consolidation style if any changes or updates are made to the master data, it will reflect the source systems. In most cases, this style of implementation is used for analytical MDM and it helps organizations clean, match, and integrates data in a centralized hub.
Benefits of Consolidation MDM Implementation Style
Take a look over the benefits an organization can get by using the consolidation style implementation of the MDM system.
- With a consolidation style, an organization can collect master data from various data sources and store them in a centralized master data hub.
- The consolidation style is less expensive and easy to set up.
- Creating enterprise-wide reporting is much easier with the consolidated style of MDM implementation.
3. MDM Implementation Style 3: Co-Existence
In the Co-Existence style, the MDM hub and the data sources are available and accessed at the same time. This style creates a consolidated master data hub and feeds the updated or upgraded master data back to its source systems.
If you look closely, you can understand the work operation of this Co-Existence style is almost similar to the consolidations style of creating a golden data record. However, the only difference is here the master data stored in the centralized master data hub gets updated in its source systems. This leads the data changes happening in both MDM and the application systems.
If you are already having a consolidation style of MDM implementation for your organization, you can easily evolve the style into co-existence by simply adding the loop back to the original data source systems.
Benefits of Co-Existence MDM Implementation Style
Take a look over the benefits an organization can get by using the Co-Existence style implementation of the MDM system.
- In this coexistence style, the master data is mastered in its source systems before synchronizing with the centralized master data management hub.
- The quality of the master data gets improved and the data accessing before much faster than before in the co-existence style.
- As all the master data attributes are stored in a single location, generating reports would be much easier and faster in a co-existence style.
4. MDM Implementation Style 4: Transaction or Centralized
Transaction or Centralized MDM implementation style is the last style that several large enterprises prefer to use. When compared with all other implementation styles, this transaction or centralized implementation style stands to be more important. In this implementation style, the master data hub becomes the single provider of the master data to an organization.
With this style, all the existing master data moves to the centralized data hub, and the remaining systems outside the data hub are not allowed to create or link with the master data. A business that is keen to have high control, and makes a lot of changes to its application infrastructure prefers this type of implementation style.
Utilizing this type of style for implementing an MDM system will enable businesses to create efficient workflows and to improve the essential validation capabilities for updating the ERP or other vital business systems that require accurate master data. This centralized style of MDM implementation often evolves from the consolidation or co-existence styles.
Benefits of Transaction or Centralized MDM Implementation Style
Take a look over the benefits an organization can get by using the transaction or centralized style implementation of the MDM system.
- A centralized style ensures your master data remains complete and accurate. Also, the master will have enough security and desired visibility policies.
- This style ensures the availability of a centralized set of accurate master data for all the existing domains.
- Leveraging the data governance capabilities of MDM becomes easy with this centralized implementation style.
How to Select the Right Master Data Management Platform?
Many Master Data Management tools are available in the market. However, after gaining an understanding of the four distinct styles of Master Data Management, it is important to consider factors that can aid in selecting the most appropriate style for your organization. Your business objectives and overall needs will play a crucial role in determining the most suitable MDM implementation style.
To assist in making an informed decision, the following points should be taken into account.
When considering the implementation of a Master Data Management (MDM) system for your organization, it’s important to keep in mind that each implementation style comes with different costs. Therefore, the cost will play a significant role in determining which MDM implementation style is best suited for your organization.
If you are running a small business with more data sources, then choosing the “Registry Style” will be the most suitable option. This style has a lower implementation and maintenance cost, making it a budget-friendly option for your organization.
2. Number of Data Sources
When deciding on the most suitable MDM implementation style, it is important to consider the number of data sources in your organization. If your organization has a large number of data sources and you want to retain the original data even after establishing the golden record, it is advisable to opt for the “Registry” style.
However, if your organization has a limited number of data sources and you wish to maximize accuracy for better reporting and analytics, any of the other three implementation styles can be chosen based on your current budget.
If you’re looking to expand your business in the coming days, it’s important to consider the implementation styles of consolidation, co-existence, or transaction MDM. With these styles, you can easily adapt and modify them to meet the evolving requirements and objectives of your brand. It’s a crucial decision that can help you achieve your goals and grow your business effectively.
Master Data Management (MDM) is a critical process that helps organizations manage their data assets more effectively. By implementing MDM, businesses can improve the quality and consistency of their data, reduce errors and redundancies, and improve decision-making capabilities.
In this guide, we have explored different MDM implementation styles and their benefits, including centralized, consolidated, registry/Hub-based, and hybrid models. Each approach has its strengths and weaknesses, so it’s important to carefully evaluate your organization’s needs before choosing the right implementation style.
With the right MDM strategy in place, businesses can unlock the full potential of their data assets and gain a competitive edge in today’s rapidly evolving business landscape.