Master data management is a method of managing the entirety of an organization’s data as a single coherent system. MDM helps ensure the reliability of data coming from different data sources in different formats, which is critical for Big Data initiatives, data analytics decision making, AI training and digital transformation.
Master data management enables enterprises to link all critical data to the master file that provides a common point of reference. When implemented well, MDM streamlines the sharing of data across the enterprise. MDM requires an effective data integration strategy as part of the overall plan.
Over the past two decades, organizations have become increasingly reliant on data to streamline operations and compete more effectively. Since the quality of business intelligence (BI), analytics and AI results depend on the quality of data, master data management can help by:
- Removing duplicate data
- Integrating data from various data sources
- Standardizing disparate data so the data can be used more effectively
- Eliminating incorrect data
- Enabling a single source of reference (also called the “Golden Record”)
Master Data Management Processes
In truth, the full range of master data management processes are often a mix of underlying process. But in an effort to simplify, these are the key MDM processes:
- Business rule administration
- Data aggregation
- Data classification
- Data collection
- Data consolidation
- Data distribution
- Data enrichment
- Data governance
- Data mapping
- Data matching
- Data normalization
Effective master data management enables a clear and strategic flow between all data sources and the various destination systems for that data.
Main Benefits of MDM
It’s clear that in today’s metrics-based world, clear and coherent data management is absolutely critical to a competitive business strategy. Your company may lag in MDM, but it’s likely that your competitors are quite focused on it.
Specifically, here are the main benefits of master data management:
- Control – Know where your data is, where it’s headed, and how secure it is
- Data accuracy – Understand how closely your metrics track the factors you need to follow
- Data consistency – Avoid fluctuations in how closely your data flow tracks the underlying patterns
Master Data Management Use Cases
Achieving data accuracy, consistency and control is critical as organizations become more dependent on data for all their daily operations. When executed effectively, master data management can help organizations:
- Compete more effectively
- Improve customer experiences by being able to identify specific customers accurately across “touch points” (different departments and channels)
- Improve operational efficiencies by reducing data-related friction
- Streamline supplier relationships with vendor master data management
- Understand customer journeys through customer master data management
- Understand product life cycles in greater detail with product master data management
Master Data Management Vertical Markets Use Cases
In different vertical markets, master data management can help as follows (although the benefits are not necessarily industry-specific):
- Healthcare providers can get faster access to patient data for diagnostic and treatment purposes
- Banking and financial services organizations can reduce customer churn by providing timely, accurate, and personalized services to customers
- Insurance companies can improve claims processing
- Energy companies can balance supply and demand more accurately
- Supply chains can reduce waste (warehouse space, fuel, etc.)
- Retailers can synchronize online and brick-and-mortar channels
Master Data Management Challenges
One of the things underscoring the need for master data management is poor data quality throughout the enterprise. For example, enterprises typically have several customer records stored in different formats in different systems.
When that’s the case, the organizations may treat existing customers like unknown prospects, overstock or under stock products, face product delivery challenges and other challenges. Common data quality issues include:
- Duplicate records
- Erroneous information
- Incomplete information
- Inconsistent records
- Mislabeled data
Causes of poor data quality include:
- A lack of standards across the organization
- Different account numbers associated with the same entity
- Employees who take shortcuts (for example, entering J. Smith instead of John Smith)
- Redundant data in the organization (for example, customer John Smith appears in various enterprise systems for sales, marketing, technical support, customer support, and finance)
- Varied field structures in different applications that require data to be entered in a certain format such as John Smith or J. Smith
Trends in Master Data Management
In 2018, many organizations scrambled to comply with the EU’s General Data Protection Regulation (GDPR) which restricts the use of Personally Identifiable Information (PII). The regulation also places control over the use of that information in end users’ hands.
Similarly, the California Consumer Privacy Act is slated to take effect on January 1, 2020, although the content could evolve based on the November 2018 election. Alternatively, the Act may be replaced by a federal equivalent.
Over time, more countries and jurisdictions are creating privacy laws that impact companies with a presence or doing business in those locations. The result of the increased scrutiny is an increased demand for and dependency on master data management solutions.
An important aspect of MDM is metadata management which manages data about data. Metadata management helps organizations:
- Ensure compliance
- Locate a specific data asset
- Manage risks
- Make sense of data
- Perform data analytics across multiple data sources inside and outside the organization
Metadata management has always been important. However, it’s becoming even more important as the amount of data continues to grow with organizations extending out to IoT, IIoT and more third-party data sources.
Master Data Management Best Practices
Before seeking out a MDM solution, it’s important to understand basic master data management concepts. Otherwise, the overall data management strategy and architecture may lack critical capabilities. Some solution providers offer data management reference architectures that explain the basics and help customers understand the company’s product offerings in context.
Master data management architectural elements and tools include:
- Data federation
- Data marts
- Data networks
- Data mining
- Data virtualization
- Data visualization
- Databases
- File systems
- Operational data store
Master Data Management: Going Forward
Over the past couple of decades, large and medium enterprises have become increasingly reliant on master data management tools as the volume and variety of data have continued to grow and their businesses have evolved. As businesses continue to add more and different types of master data management capabilities, their master data management architectures can become complex and unwieldy. To simplify the complexity and increase market share, some vendors provide comprehensive suites or solutions that replace individual point solutions.
Master data management continues to grow in importance as businesses transition from periodic business intelligence (BI) reports to self-serve and advanced analytics. Master data management is also critical as organizations adopt and build AI-powered systems because at least some of the data an organization has will be used as training data for machine learning purposes.
In fact, master data management and data management generally have become so important that more organizations are hiring a Chief Data Officer (CDO), a Chief Analytics Officer (CAO) or both. The goal is to ensure the reliability of data, its strategic application and its overall management. Typically, they have a data steward on the team who’s responsible for implementing master data management.
When executed properly, master data management allows companies to:
- Integrate disparate data from various data sources into a single hub so it can be replicated to other destinations
- Provide a single view of master data across destination systems
- Copy master data from one system to another
