What is Master Data Management

Key components of master data management

Data Sources:

In master data management, the focus is on managing core business entities that serve as reference points for other transactional data. The types of data typically managed through master data management include:

• Customer data:
  This encompasses information about customers, such as their names, contact details, preferences, and purchasing history. Managing customer data allows businesses to maintain accurate profiles, enabling better customer service, fostering increased 
   
• Product data:
  Product information includes details like product names, descriptions, specifications, and pricing. Effective management of product data ensures that all departments work with consistent and up-to-date information, which is vital for marketing, sales, and inventory management.
   
• Supplier data:
  Supplier data includes information about vendors and partners. Accurate supplier data helps businesses manage relationships, negotiate better contracts, and streamline procurement processes.
   
• Employee data:
  Employee data typically includes personal details, job titles, departments, skills, and performance metrics. By providing a single source of truth for employee information, HR departments and managers can make informed decisions regarding workforce planning, talent management, and employee development. MDM also supports compliance with regulatory requirements related to employee data.
   
• Location data:
  Location data encompasses geographical information about offices, warehouses, stores, or other relevant sites critical to business operations. By creating s single locational view, MDM helps organizations manage logistics, supply chains, and customer outreach effectively by providing a clear understanding of where their physical assets are situated. In addition, consistent and accurate location 

Data Modeling:

Data modeling is an integral part of master data management, as it involves structuring data in a way that aligns with business requirements and ensures consistency across different systems. Traditionally, the process includes:

• Data entity definition:
  This involves defining the key entities, such as customers or products, and their attributes. It sets the groundwork for how data will be organized and managed.
    
• Relationships:
  Data modeling identifies and defines relationships between different entities. For example, a customer might have multiple orders, and these relationships are essential for maintaining an accurate data hierarchy.
    
• Standardization:
  Data modeling standardizes formats and data types, ensuring consistency. For instance, all phone numbers might be stored in a standard format, regardless of where they originate.

Historically, data modeling has been one of the most time-consuming and complex parts of MDM. This exercise can take over six months to complete, and traditional MDM solutions have necessitated the process be completed before data can be ingested by the MDM system. Modern MDM solutions, like CluedIn, have drastically simplified and shortened the data modeling process by ingesting data as-is, without the need for upfront modeling. This is possible because CluedIn is a Graph-based platform, which means that the relationships between the data are as important as the data itself, and can emerge naturally as data is ingested and processed.

Data Integration:

Data integration is crucial in master data management, as it consolidates data from various sources into a single, unified view. Key aspects include:

• Data sources:
  Organizations often have data spread across multiple systems, such as CRM, ERP, and legacy databases. Data integration involves identifying these sources and determining how to extract relevant information.  
   
• Data transformation:
  Different systems might store similar data in different formats. Data integration involves transforming this data into a consistent format that aligns with the organization's data model.
    
• Data loading:
  Once data is extracted and transformed, it needs to be loaded into the MDM system. This process ensures that the master data is up-to-date and reflects all relevant information from various sources.

Similar to the Data Modeling example, the above process represents a more traditional means of integrating data in preparation for MDM known as ETL (Extract, Transform, Load). Advanced MDM solutions take a different approach, known as ELT (Extract, Load, Transform) which means that data can be loaded into the MDM systems immediately following extraction from the source systems, and is transformed into a consistent format by the MDM system itself. This is the method used by CluedIn, and is another way in which it saves time and cost.

Master Data Management Processes and Techniques

Data Cleansing:

Data cleansing is an important component of Master Data Management (MDM). It involves identifying, correcting and removing inaccuracies, inconsistencies, and errors in the data. Data cleansing ensures that the master data is accurate, reliable, and ready for use across different systems and applications. Data cleansing typically comprises:

• Identifying errors:
  MDM systems normally use automated processes to detect common issues such as duplicates, missing values, or incorrect data types.
    
• Correcting data:
  Once errors are identified, data cleansing tools correct or remove faulty data. This usually involves standardizing formats, filling in missing values, and merging duplicate records.
   
• Improving data quality:
  The ultimate goal of data cleansing is to enhance overall data quality, which is fundamental to ensuring that business decisions are based on accurate information and that processes run as efficiently as possible. 

Data Enrichment:

Data enrichment refers to the process of improving existing data by adding relevant information from external or internal sources. This process adds value to the master data by providing a more comprehensive view of key business entities. Examples of data enrichment include:

• Adding context:
  Enriching data with additional context, such as demographic information or firmographic information, helps create a more complete picture of business entities.
   
• Utilizing external data:
  MDM systems often integrate external data sources, such as third-party databases and public records, to enhance master data, providing valuable insights and enhancing data accuracy.
    
• Improving decision-making:
  By providing more detailed and relevant information, data enrichment improves decision-making capabilities across various business functions.

Data Synchronization:

Data synchronization ensures that the master data is consistent across multiple systems and platforms. It involves orchestrating updates and changes to ensure that all systems reflect the latest information. This process is essential for maintaining a unified view of critical business data and includes:

• Identifying changes:
  Enriching data with additional context, such as demographic information or firmographic information, helps create a more complete picture of business entities.
   
• Co-ordinating updates:
  MDM systems often integrate external data sources, such as third-party databases and public records, to enhance master data, providing valuable insights and enhancing data accuracy.
    
• Maintaining consistency:
  The goal of data synchronization is to maintain consistency, so that all systems and teams work with the same version of the master data.  By minimizing conflicts and ensuring that master data is as up-to-date as possible, the insights and intelligence that are generated from that data are more trustworthy and impactful.
  
  

Benefits of Master Data Management

There are three primary benefits of master data management:

• Improved data quality:
  The most common driver behind MDM projects is a need to raise data quality, accuracy and reliability. By eliminating inconsistencies, eradicating errors, and enriching existing data, organizations can turn what it is often a fragmented and siloed data estate into a valuable resource. Modern MDM systems have embraced the idea of Augmented Data Quality, which goes beyond traditional methods and uses Artificial Intelligence, Machine Learning and other advanced technologies like Graph to automatically and intelligently raise data quality.
   
• Better decision making:
  Accurate and consistent data is vital for informed decision-making. MDM offers a single source of truth, or Golden Record, for their key business entities, which means that business leaders and decision-makers have a solid foundation of reliable data upon which to base their assessments and plans. Better decisions mean more successful outcomes, which lead to a more data-driven and analytical approach across the organization.
   
• Operational efficiency:
  The need to manage cost and risk is prevalent across almost every business. By eliminating redundant data and consolidating information into a single, unified view, businesses benefit from a clear and consistent view of their data. This reduces the time and effort spent on data-related issues, as well as reducing the risk of disruption and expense due to outdated and inconsistent data.

Master Data Management Challenges

• Data silos:
  Data silos occur when different departments or systems within an organization maintain separate and isolated sets of data. In many instances, data relating to the same entities (customer, employee, location, etc.) will reside in several different systems. These silos can lead to inconsistencies and prevent the sharing of important information across the organization. MDM helps to break down these silos by consolidating data from disparate sources into a centralized repository, thereby creating a unified view. When every team works from the data set, collaboration is improved, conflict reduced and it is far easier to reach a consensus about important decisions. For more detailed information on data silos, read the related white paper.
    
• Data governance:
  MDM falls under the umbrella of Data Governance, which is the process of managing and security data throughout its lifecycle. Although MDM systems are not Data Governance solutions per se, the two are highly complementary and many enterprises choose to use both. Data Governance is primarily concerned with the creation and implementation of policies, assigning roles and responsibilities, and maintaining compliance with regulatory requirements. MDM is an important tool in the enforcement and application of the rules and policies set by a Data Governance program, in that it provides the tools to enhance data quality and reliability, track data lineage and enforce security and data protection requirements. To learn more about how Data Governance and MDM work together, read the in-depth article.
    
• Scalability:
  Depending on the size and nature of the organization, datasets can be very large and volumes can grow exponentially as data is accumulated from various sources. The challenge therefore is to ensure that an MDM system is able to scale to handle the increasing load while maintaining performance and consistency. Modern MDM systems should be designed with scalability in mind, and in most cases that means using an MDM system that is truly Cloud-native, which not only means that elastic scale is at your fingertips, but that it works with the economics of the Cloud and delivers other inherent benefits such as security, policy management and performance efficiency. Find out more about Cloud-native MDM systems here.

Master Data Management Best Practices

MDM has a reputation for being costly, complex and difficult to demonstrate value from. Follow these guidelines below to ensure that your MDM project is positioned for success from the outset.

Define clear objectives

At its core, the purpose of MDM is to prepare data to deliver insight. That insight forms the basis of decisions that can improve both productivity and profitability. It is therefore imperative that clear objectives are defined from the start, and that these objectives align with the organization's strategic priorities. Objectives should address specific business needs, such as streamlining operations, improving customer retention and accelerating product development.

Clear objectives help to set the right expectations amongst stakeholders, and pave the way for the metrics and milestones that will be used to measure success along the way. Without well-defined goals, MDM projects risk losing focus and buy-in, and will, most likely, fail to deliver tangible value. 

Bring the business into the data value chain

Enabling business users to be directly involved in data management through Master Data Management (MDM) is crucial because it bridges the gap between technical and business viewpoints. Business users possess invaluable domain knowledge and understand the specific needs of their departments, customers and suppliers. When they are actively involved in data management, they can ensure that the data aligns with business objectives and requirements, leading to more relevant and useful insights.

Involving business users also enhances data quality because they can identify and address data issues that technical teams may not have the context to identify. This collaborative approach not only ensures that data is accurate, up-to-date, and reflective of real-world business contexts, but it also fosters a sense of ownership and accountability, which can lead to more effective data governance and a stronger data-driven culture within the organization. 

Implement strong governance

Regardless of whether you choose to invest in an MDM solution before, after, or at the same time as dedicated Data Governance tools, robust governance policies are essential for effective MDM. These policies should define how data is managed, protected and used, as well as who is responsible for overseeing which parts of the process. Governance is intrinsically linked with regulatory compliance, but it should also be viewed as a means of supporting growth initiatives. Having this framework in place, and using MDM as means of executing your Data Governance strategy, will ensure that an MDM project is optimized for success from the outset.

Start small and iterate

One of the most common mistakes organizations make when implementing MDM is attempting to master all key entities at once in a “big bang” style approach. MDM projects are typically complex, because they involve multiple data sources, stakeholders and processes. That complexity is only exacerbated when a project encompasses multiple domains. A phased approach is far better, as it allows organizations to focus on mastering smaller subsets of data first, thereby making the process more manageable and reducing the likelihood of overwhelming teams with complexity.

A phased approach also allows teams to achieve incremental improvements and demonstrate early successes, identify and address risks early on, and allocate time, budget and human resources appropriately.

What is Master Data Management - Final Thoughts

MDM was once seen as an antiquated and complicated discipline, but is now returning to prominence as organizations grapple to realize the potential of their data in a meaningful way. The rapid adoption of advanced analytics, AI and ML has, for many, only succeeded in highlighting the inability of their existing data assets to provide the reliable and consistent supply of high-quality data needed for them to succeed.

For many, it is a case of going back to basics and addressing fundamental issues – like data quality – that have always been present but are only now causing urgent and deep-seated difficulties.

MDM is vital to building a solid foundation of trusted data upon which data-driven initiatives can thrive, and the new breed of modern MDM solutions is poised and ready to do just that.