In pursuing cutting-edge enterprise technology, one unwavering truth stands out: achieving transformative data-powered innovation hinges on the foundation of comprehensive data management and governance. A robust master data management strategy is the cornerstone of such a foundation.
This article will provide an in-depth overview of master data management, explore its benefits, and how it can be merged with existing systems.
What Is Master Data Management: Table of Contents
What is Master Data Management (MDM)
Master Data Management is a method used to create and maintain a single master data record — the best version of truth — for each of the following entities related to an organization’s operations:
- Parties (e.g., customers, employees, healthcare providers, and suppliers).
- Places (e.g., offices, branches, and stores).
- Things (e.g., assets, materials, and products).
This best version of truth (i.e., golden record) is created by merging internal data from across the enterprise to assemble specific identifying attributes and integrating external data for enrichment and validation.
Once created, the master data serves as a trusted view of business-critical information shared across the company to:
- Foster accurate reporting,
- Minimize data errors,
- Boost customer experience,
- Eliminate redundancy, and
- Make informed, data-driven decisions.
Master Data Examples
All data critical to a business is considered master data. Master data:
- Is non-transactional (i.e., describes the parties, places, and things involved in the transactions),
- Embeds all basic information needed for running business operations,
- Does not change often, and
- Can include reference data (e.g., country codes, languages, and currencies).
The following are two typical examples of master data.
1. Master Customer Data
One of the most common domains of master data provides essential attributes such as:
- Customers’ names,
- Phone numbers,
- Addresses, and
- Loyalty numbers.
Up-to-date and accurate master customer data is imperative to efficient operations. Inaccurate and poor-quality data can make or break a business by negatively impacting operational efficiency, decision-making, and customer trust.
For example, having multiple addresses that vary across departments for a single customer might lead to incorrect order deliveries, missed marketing opportunities, and/or billing issues.
In addition to mastering individual customers, hierarchies may be built to enable the mastering of relationships between them. This enables capabilities such as householding, organization of buyer groups, and even visual breakdowns of customer relationships.
Implementing a customer master keeps all customer information reliable and synchronized across the enterprise allowing it to be more effectively leveraged to accomplish new business initiatives and streamline operations.
2. Master Organization Data
Organization domains include information about suppliers, partners, and other types of organizations. This master data set includes information like the organization’s:
- Names,
- Addresses,
- Tax IDs, and
- Contact people.
A master data management solution mitigates data errors and record duplications, as information is updated automatically and in one place. This way, even if an organization’s name is entered incorrectly by a department, the data will be merged under the correct record based on other mutual parameters (e.g., tax ID).
Furthermore, mastering organizational data enables the creation and maintenance of organizational hierarchies. By leveraging such features in MDM, relationships between organizational entities be mastered at an enterprise level. Furthermore, multiple versions of hierarchies may be created to view the same organization in various contexts depending on the requesting business unit. For example, maintaining both a corporate hierarchy and a financial hierarchy for the same organization.
Master Data Management Principles
A robust master data management strategy is based on nine key principles.- Data Governance. Establishes clear ownership, accountability, and policies for managing master data to ensure its quality, security, and compliance with regulatory requirements.
- Data Quality. Ensures that master data is accurate, complete, consistent, and up to date. This is attained by implementing data validation, cleansing, and enrichment processes.
- Data Integration. Integrates master data from multiple systems and sources across the organization to create a single, authoritative source of truth.
- Data Standardization. Defines and enforces standards for naming conventions, data formats, and coding schemes to ensure consistency and interoperability of master data.
- Data Stewardship. Assigns data stewards responsible for managing and maintaining specific subsets of master data. It includes resolving data quality issues and ensuring data integrity.
- Data Lifecycle Management. Manages the entire lifecycle of master data from creation to retirement, including versioning, archiving, and auditing.
- Data Accessibility. It provides authorized users easy access to master data through user-friendly interfaces and integration with business applications.
- Scalability and Flexibility. Designs MDM solutions that can scale to accommodate the growing volume and complexity of master data and adapt to changing business requirements.
- Continuous Improvement. Continuously monitor and evaluate the effectiveness of MDM processes and adjust to improve data quality, governance, and overall performance over time.
The Importance of Master Data Management
According to recent research, over 50% of enterprises manage at least 5 PB of data. Due to this high amount of information, many companies struggle with fragmented, incomplete, and inconsistent data, leading to a variety of business problems. Gartner has calculated that the average cost associated with poor data management can exceed $1.2 million a year. Effective MDM solutions help mitigate these pain points. For instance, Adani Group, India’s largest port operator, increased productivity by 15-20% leveraging the power of a centralized master data solution. However, organizations see a multitude of additional benefits provided by MDM, as detailed below:- Single View of Customers. Ensures a seamless and unified view of customer records across all platforms. For example, a customer’s name in a customer service database might be Bob Ross, but in another database, it could be Robert Ross or Bobby Ross. A master data management solution can recognize these variations as the same customer based on the same address or phone number.
- Elimination of data inconsistencies. Blends customer and product data, resolving discrepancies and eliminating duplicates. The same product might have a customer product number on the invoice that differs from the one on the organization’s internal database. In a mergers and acquisitions (M&A) project, MDM can help remove redundant data and reduce silos.
- Business operations improvements. By providing accurate and complete customer information, MDM enhances business processes such as marketing campaigns, customer engagement efforts, and supply chain management.
- Decision-making enhancements. Empowers employees and data scientists with comprehensive customer insights, facilitating informed data-driven decision-making and streamlined processes.
4 Main Master Data Management Implementation Styles
There are several different master data management architecture styles. Enterprises should choose the suitable deployment type based on their: core business, organizational framework, and objectives.
Infoverity can advise you on the best approach and help you select from various implementation styles. Here, we examine the four most common.
1. Registry Style
This data model links the master data management records and the source systems without modifying the master data. MDM uses data cleansing and matching algorithms to identify duplicate entries.
It enables organizations to analyze data at a minimum cost without overwriting the source. This style is often chosen by healthcare companies where the volume of data is growing exponentially.
2. Consolidation Style
This is the upgraded version of the registry model. Data is consolidated and cleaned from various source systems in the master data management hub to create a single source of truth.
Dubious data is verified and corrected by a human data steward. It is quick and inexpensive to set up as there is no need to discontinue legacy systems. It is mainly used for business intelligence (BI) analysis and reporting.
3. Coexistence Style
Similar to the consolidation style, when new sources of master data are added, MDM initiates processes to clean and integrate the information into a data management hub.
Any change to the master data is updated in the hub and immediately shared with the other systems so that everyone in the organization uses and sees the same data.
4. Centralized or Transaction Style
In this approach, all new data is authored and managed in the MDM tool itself. Stewards review questionable records while systems and applications receive updates from the master data management hub. It is an expensive and complex process to set up. However, it guarantees the highest level of:
- Accuracy,
- Control over security, and
- Visibility policies.
This style is popular among large corporations with strict data governance policies and works well for sorting out messy data that cannot be put together in a useful way.
MDM is necessary both in practice and as a technology to empower the data-driven progress of any organization striving to:
- Enhance customer satisfaction,
- Improve operational efficiency, and
- Decrease costs.
Infoverity master data management solutions can help you unlock the full potential of master data and empower you to thrive in the digital age.
