Master Data Management: What It Is & How MDM Tools Can Organize ERP Data for Enhanced Business Intelligence

In today’s data-driven world, business intelligence and analytics play a huge role in better understanding your customers, improving your operations, and making actionable business decisions. While there’s no doubt about the value of implementing a BI solution, many ERP users face the same challenges around the quality and credibility of their data. The reason for this is that most companies don’t take the time to regularly cleanse, validate, and audit their data. Instead of an environment riddled with inconsistencies and inaccuracies to base your analytics on, MDM helps bolster confidence and trust in your organization’s data.

What is Master Data Management (MDM)?

This is where Master Data Management (MDM) comes into play. MDM is the process of defining, cleaning, managing, and maintaining the core (or “master”) data that gets shared across an organization. Master data is specifically the key entities that the business revolves around, such as customers, products, suppliers, employees, financial accounts, and more. Therefore, MDM’s goal is to provide a single, consistent view of your key business entities with established rules and standards, so you have full control and accountability over your data. This results in more accurate, dependable, and governed information for analytics and reporting, allowing you to make better decisions from higher-quality data.

Key aspects of MDM include:

• Data consolidation and integration across systems (e.g. ERPs, CRMs, etc.)

• Data cleansing, deduplication, standardization, and enrichment

• Governance of roles, policies, ownership, lifecycle, and security

• Metadata and semantic consistency (e.g. so “customer,” “product,” etc. mean the same in all contexts)

• Ongoing maintenance and synchronization so master data stays current and true

Examples of MDM

To solidify our understanding of master data management, let’s review a couple of examples of how MDM can be used:

• A manufacturer with many product lines, multiple catalogs, and various systems wants a single unified product master record. MDM standardizes attributes, classification, pricing, images, specs, and more to be shared across sales, marketing, and logistics.

• A finance team may need to harmonize data about accounts, cost centers, business entities, and more across different regions or countries. MDM enables efficient and consolidated reporting, budgeting, and auditing.

Master Data Management vs. Data Governance

For those familiar with data management and security, MDM may sound fairly similar to data governance. While they are related, they are also distinct — MDM focuses on the technical process and system side of managing the master data records. On the other hand, data governance makes up the rules, structures, and accountability for data.

Benefits of Master Data Management: Why MDM is Important

MDM used to be thought of as a “nice-to-have,” but it has recently become an absolute necessity. It helps ensure:

• More accurate decision-making: Without trusted master data, your reports, analytics, and business intelligence can be misleading, while inconsistent or duplicated data can lead businesses to incorrect conclusions.

• Better operational efficiency: Data that is misaligned or hasn’t been effectively cleaned results in errors, duplication of work, ongoing reworks, and other wasted time that increases costs and inhibits efficiency.

• Enhanced customer experience: When customer information is duplicated, inconsistent, or outdated, your communications with them can be frustrating and create a poor customer journey that lacks personalization.

• Regulatory compliance: Data privacy laws, financial audits, and other regulations typically require data to be consistent, traceable, accessible, and controlled — all of which MDM helps support.

• Effective scalability and agility: As your business grows, it will inevitably add new products, channels, data sources, and other complexities that will need to remain organized, which MDM makes more feasible.

• You have a single source of truth: The main crux of MDM’s value comes from its consistency so that finance, operations, sales, and marketing all work from the same definitions, alignments, and functions.

ERP Master Data Management for More Effective BI

While your ERP system might do a great job at collecting data, companies should always have a strategy in place to manage it all. When every application and business process maps to your master data, it quickly creates a cohesive (and unified) version of the truth. Here are five ways that MDM can help you better organize your ERP data for BI and analytics:

1. Simplifies Data Structure: With all of your data mapped to your master data, analysts get a clearer and more controlled view of your operations and how the company/department is performing.

2. Develops Data Governance: Designates a set of rules, hierarchies, and structures that facilitate consistency and compliance throughout the organization.

3. Improves Data Accuracy: Establishes one version of the truth immediately, helping to improve the quality and accuracy of your data for better decision-making.

4. Enforces Accountability Over Data: Empowers every employee to take accountability for the data that gets entered and used for decision-making.

5. Creates a Foundation for a Data Warehouse: Prepares your data for migration and integration required for centralized data storage.

MDM and a robust data governance strategy should be used together to improve the quality of your existing ERP data. Not only will this approach reduce the risk of entering and using bad data, it also helps with overall business intelligence and analytics adoption throughout your organization. Instead of facing the challenge of finding usefulness in the BI solution you implement, your business users will be able to see immediate value and productivity gains.

Challenges of Master Data Management (& How to Avoid Them)

While MDM can provide a host of valuable benefits, implementation can be challenging. Here are some common hurdles we see with master data management, as well as how to avoid or mitigate each one:

Data Silos

Different systems, combined with departments maintaining their own versions of data, can result in a lack of integration and significantly hamper MDM efforts.

How to prevent/mitigate:

• Map out all source systems

• Invest in data integration tools (like Simba)

• Ensure central oversight

Poor Data Quality

Incomplete, inconsistent, duplicated, outdated, or incorrect master data undermines the foundation of MDM and inhibits its utility.

How to prevent/mitigate:

• Leverage data profiling, cleansing, standardization, and enrichment

• Set KPIs around data quality

• Perform ongoing monitoring (using tools like Kalido can help)

Lack of Governance/Ownership

Without designated owners and stewards, role clarity, and policy enforcement, drift will likely occur and reduce MDM’s effectiveness over time.

How to prevent/mitigate:

• Define clear roles (e.g. data stewards and owners), policies, and workflows

• Get executive buy-in and sponsorship

• Include governance in design from the start

Scale & Complexity

As enterprises grow and expand, the number of master data domains, sources, volume, and change frequency continues to increase, amplifying MDM complexity.

How to prevent/mitigate:

• Begin with prioritized domains

• Use modular architecture and scalable tools

• Incorporate automated matching and merging

Technical Debt

Many large companies still use legacy systems that have inconsistent or non-standard data, limited connectivity, and out-of-date APIs, making data integration difficult.

How to prevent/mitigate:

• Take inventory of your legacy systems and plan migrations/remediations

• Build bridging and integration pipelines

• If full replacement isn’t feasible, consider overlay tools

Limited Adoption

People naturally resist change and departments may feel the need to guard their “own” data, leading to inconsistent adoption of rules/tools and ineffective MDM.

How to prevent/mitigate:

• Clearly communicate what is happening and demonstrate MDM’s value

• Provide training and demonstrations

• Involve stakeholders from across different teams and functions

How to Implement Master Data Management Effectively

In addition to the prevention tactics listed above, we’ve collected some of the most effective MDM steps so you can maximize the success of your implementation:

1. Assess & Plan: Take inventory of current master data domains, systems, data quality, and existing governance to define the scope and business objectives of your MDM plans.

2. Define Governance: Establish data security frameworks via roles, policies, and workflows, then set standards for definitions, taxonomies, reference data, and metadata. Kalido seamlessly supports this, providing governance modules to guide your design and planning.

3. Select Tools: Choose your MDM platform that can match your required domains, integrations, scale, data quality, and connectivity. Kalido is an industry-leading MDM tool, and includes a dynamic information warehouse solution to store your data.

4. Start Small: We recommend starting small with a pilot program or a small set of data sources because this makes it easier to test, refine definitions and processes, evaluate tool usage, and demonstrate value. This can be done with dashboards to monitor data quality improvements.

5. Integrate & Consolidate: Pull your data from all identified sources (e.g. using Simba’s connectivity drivers) in Step 1, apply the mappings and definitions, resolve any duplication issues, and harmonize the data. Set up synchronization or replication so your master data is kept up-to-date from its sources.

6. Roll Out & Expand: Once you’ve proven out the pilot program, roll out additional domains, business units, and regions, applying the lessons learned to refine governance, definitions, and tool configurations.

7. Monitor, Maintain, & Evolve: Once your MDM is up and running, implement ongoing data quality monitoring via dashboards and alerts for anomaly detection and drift. This monitoring should also include regular audits of master data, stewardship, and compliance, as well as feedback loops as the business changes. You could even embed these monitoring dashboards in tools you already use with Logi Symphony.

8. Measure & Demonstrate Value: Track the KPIs and metrics you identified during the planning phase, again using dashboards and reports to surface these improvements to stakeholders. Effective analytics tools (like Logi Analytics, Jet Analytics, or Angles) enable you to clearly show before/after comparisons, data trends, cost/benefits, and more.

Master Data Management Solutions & Tools to Enhance Your BI Projects

When done properly, reports using the right data quality and master data management processes will enable more effective BI and data governance. This can then be used as the groundwork for all your analytics and decision-making, putting you ahead of the competition.

Read to start transforming your master data management? Learn more about Kalido here or book a demo with our team to see how it can help your MDM today. Want to learn more about MDM and its practical applications? We have developed a comprehensive white paper on how to do just that, just click the button below to download it!