According to a recent Dataversity article, analysts predict that digitized businesses will stand out through their enterprise data management and data governance strategies in 2023. Many businesses today, especially global enterprises have hundreds of separate applications and systems (ie ERP, CRM) where data that crosses organizational departments or divisions can easily become fragmented, duplicated and most commonly out of date. When this occurs, answering even the most basic, but critical questions about any type of performance metric or KPI for a business accurately becomes a pain.
Getting answers to basic questions such as “who are our most profitable customers?”, “what product(s) have the best margins?” or in some cases, “how many employees do we have”? become tough to answer – or at least with any degree of accuracy.
Basically, the need for accurate, timely information is acute and as sources of data increase, managing it consistently and keeping data definitions up to date so all parts of a business use the same information is a never ending challenge.
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The Ins and Outs of Master Data in Database Management
Master data is the core set of business information that is critical for supporting operations and strategic decision making in organizations But what exactly constitutes master data, and why is managing it properly so important in database environments? In this comprehensive guide, we’ll unpack everything you need to know about leveraging master data for data management success
Defining Master DataMaster data represents the main business entities that are meaningful to an organization. This includes information on key people places and things like
- Customers
- Products
- Suppliers
- Employees
- Locations
In contrast to transactional data that reflects business events like sales and shipments, master data provides the contextual details necessary to make sense of those transactions. While transactional data is volatile and constantly changing, master data is relatively stable and changes less frequently.
Some common characteristics of master data:
- Provides core business context
- Relates to main business entities
- Changes infrequently
- Requires governance and quality control
- Used across systems and processes
For example, in a retailer’s database, the product name, description, and cost would be master data. The individual sales transactions of that product would be the transactional data. The master data gives essential context needed to analyze the transactions – like calculating profit margins.
Master data might include:
- Customer names, addresses, account details
- Product specifications, pricing, manufacturers
- Store locations, floor plans, inventory
- Supplier information, contracts, procurement data
This data provides the critical foundation for business operations. Transactions like orders, shipments, and invoices rely on having accurate, consistent master data.
The Importance of Master Data Management
With master data playing such a vital role, properly managing it is crucial. Master data management (MDM) is the overall discipline focused on establishing processes, policies, and systems to manage master data.
Some key reasons why master data management is important:
- Creates a “single source of truth” for core business data across systems
- Improves data quality through governance and standardization
- Supports accurate analytics and reporting
- Enhances operational efficiency and productivity
- Helps mitigate risk related to poor data
Without MDM, master data problems are common. Different applications or divisions may maintain their own versions of key data like customer details. This leads to inconsistencies, errors, and operational headaches when data can’t be reconciled across systems.
For example, the sales and marketing teams might have different mailing addresses for the same customer. Or product details like prices and descriptions might be inconsistent in separate e-commerce systems. MDM helps avoid these scenarios through centralized stewardship and governance of master data.
Master data management establishes common data standards and models. It also provides cross-functional data stewardship workflows. Master data from various source systems is consolidated, cleansed, and standardized. The final “golden record” master data set is made available to downstream applications.
This unified approach to managing master data assets enhances trust in the data. It also makes integrating data across systems much easier. With reliable MDM, organizations gain a holistic data environment capable of supporting advanced analytics, AI initiatives, and more impactful business insights.
Architectural Styles for Master Data Management
There are several architectural approaches organizations can take when implementing MDM capabilities:
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Registry style: A central MDM registry or catalog is created to provide a unified index of master data sources. The data isn’t consolidated, just mapped across sources.
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Consolidated style: Master data is physically consolidated from sources into an MDM repository that acts as the central trusted version.
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Coexistence style: A hybrid approach where master data is consolidated into an MDM hub but can also be synced back to source systems.
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Transactional style: The MDM hub becomes the system of record for all master data creation, updates, and access. Source systems are totally decoupled.
The best style depends on factors like existing infrastructure, budget, and how disconnected source data is. Tighter source system integration calls for consolidated/transactional styles. Loosely coupled sources can start with a registry for quick wins.
Master Data Management in Database Environments
Modern databases provide robust platforms for MDM implementations. Relational and multi-model databases like Oracle, SQL Server, and MongoDB support building MDM hubs and repositories. MDM capabilities are also increasingly embedded within database management systems directly.
For database administrators and data managers, master data management introduces additional considerations around:
- Data modeling – designing structures optimized for master data use cases
- Metadata management – tracking master data elements and lineage
- Data integration – ETL, replication, pipelines to consolidate master data
- Lifecycle management – governing master data changes and maintenance
- Data quality – cleansing, validating, and enriching master data
Logical master data models help orient database schemas for manageability. These models define entities, attributes, relationships, and mappings across source systems. Database designers can then build physical data models tailored to the underlying database technology.
Master data requires meticulous metadata management. Details like data element definitions, stewards, lineage, and refresh schedules are critical. This metadata aids discoverability, compliance, and effective data governance.
ETL, change data capture, and other integration techniques are used to consolidate and synchronize master data across systems. Batch or real-time movement into the MDM repository must preserve data integrity and quality.
Master data lifecycle processes oblige special handling not typical for transactional data. Change approvals, scheduled refreshes, version control, and retirement policies may be mandated. Database capabilities can support these automated master data workflows.
Given its importance, master data requires continual data quality and validation checks. Database cleansing, matching, and enrichment features help perfect master data. Constraints, rules, and other controls help safeguard its integrity.
With the right architecture, data model, integrations, and governance, databases provide a robust foundation for master data management success. Careful planning is needed to implement MDM capabilities that improve data consistency while remaining sensitive to business needs.
Challenges of Master Data Management
Master data management delivers substantial benefits but also poses notable challenges:
- Scoping complexity – Large MDM initiatives must be carefully bounded and staged.
- Data conflicts across sources – Business units may have conflicting perspectives on data standards.
- Integration overhead – Touching many downstream systems is arduous and risky.
- Compliance considerations – Rigorous controls must support auditing requirements.
- Lack of ownership – Master data stewardship responsibilities may be unclear.
- Merger & acquisition migration – Integrating organizational data is difficult after M&As.
- Reference data modeling – Generic data like product codes can be hard to consolidate.
- Reconciling big data – New data types may not fit traditional MDM paradigms.
Striking the right balance between IT-driven and business-driven data management is also hard. Overemphasis on either side causes problems. For success, master data governance needs both central data stewardship and distributed data ownership by business units.
Master Data Management Best Practices
Follow these best practices when planning and implementing master data management:
- Define MDM vision and roadmap cooperatively with business stakeholders
- Start with high priority master data domains that will deliver quick wins
- Phase rollouts incrementally to build credibility before broader adoption
- Reuse existing data governance teams and resources where possible
- Monitor data quality KPIs continuously, not just during project milestones
- Manage master data as an ongoing program, not a one-time project
- Use data stewards to liaise between IT and business owners of master data
- Be prepared to tune and refine data models and business rules after go-live
- Modernize systems incrementally to support new MDM architecture over time
Well-executed master data management unlocks immense business value. But MDM also introduces architectural shifts and new governance demands that affect many stakeholders. Patience, pragmatism and cross-functional partnership are key to navigating challenges on the journey to unified, trusted data.
Answers to Common Master Data Management Questions
How can data management drive operational efficiency with simplified workflows?
Data management drives operational efficiency by simplifying workflows. By centralizing and streamlining data processes, organizations can eliminate redundancies, reduce manual tasks, and automate data-related workflows. This leads to improved efficiency, reduced errors, and increased productivity across the organization.
How can data management increase agility with 360-degree views of data across the enterprise?
Data management increases agility by providing 360-degree views of data across the enterprise. This means that organizations have a comprehensive and unified view of their data from various sources, enabling them to make faster and more informed decisions, respond quickly to changes, and adapt to evolving business needs.
How can data management boost revenue and profitability with more accurate AI models?
Data management can boost revenue and profitability by providing more accurate AI models. By ensuring that data is accurate, reliable, and up-to-date, organizations can train AI models with high-quality data, leading to more accurate predictions and insights that can drive revenue growth and improve profitability.
How can data management enhance workforce productivity?
Data management enhances workforce productivity by enabling self-service data access. This means that employees can easily access the data they need without relying on IT or data specialists, allowing them to work more efficiently and make informed decisions.
What are the business-critical benefits of data management?
Data management provides several business-critical benefits, including enhancing workforce productivity through self-service data access, boosting revenue and profitability with more accurate AI models, increasing agility with 360-degree views of data across the enterprise, driving operational efficiency with simplified workflows, and increasing access to data on any platform, any cloud, and for any type of user in multicloud and multi-hybrid environments.
A Few Thoughts On Versioning and Auditing
No matter how you manage your master data, it’s important to be able to understand how the data got to the current state.
For example:
If a customer record was consolidated from two different merged records, you might need to know what the original records looked like in case a data steward determines that the records were merged by mistake and should really be two different customers. The version management should include a simple interface for displaying versions and reverting all or part of a change to a previous version.
The normal branching of versions and grouping of changes that source control systems use can also be very useful for maintaining different derivation changes and reverting groups of changes to a previous branch. Data stewardship and compliance requirements will often include a way to determine who made each change and when it was made.
To support these requirements, an MDM software should include a facility for auditing changes to the master data. In addition to keeping an audit log, the MDM software should include a simple way to find the particular change for which you are looking. An MDM software can audit thousands of changes a day, so search and reporting facilities for the audit log are important.
What is Master Data Management
What is master data management?
Master data management creates a master record (also known as a “ golden record ” or “best version of the truth”) that contains the essential information upon which a business or organization relies.
What is master data?
Master data is often called a golden record of information in a data domain, which corresponds to the entity that’s the subject of the data being mastered. Data domains vary from industry to industry. For example, common ones for manufacturers include customers, products, suppliers and materials.
What is a master data management architecture (MDM)?
In addition to master data management architecture, MDMs are also categorized based on what they are used for in an organization. For example, some MDMs are operational since they are used in routine data operations and are heavily focused on providing a consolidated view of core data assets to everyone who handles data in an organization.
What is Master Data & Reference Data?
The master record contains what an organization needs to know about critical “things”—a customer, location, product, supplier, and so on—to facilitate a task or action such as a marketing campaign, a service call, or a sales conversation. One easily understood type of master data is reference data. Reference data is a subset of master data.
