How to Master Supplier Data Quality

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How to Master Supplier Data Quality

If you've ever wondered why your organization struggles with supplier management despite investing in sophisticated systems, the answer likely lies in your approach to Supplier Master Record governance.

While the process we'll outline applies to any piece of master data in your organization (contracts, pricing records, materials, etc.), supplier master records are typically the most complex data objects to manage for procurement. This makes them the ideal candidate for this deep dive.

No matter where your organization sits on the data governance maturity spectrum, from ad-hoc processes to fully optimized operations, this 5-step framework will help you take meaningful steps forward.

Let’s get started.

Step 1: The Supplier Data Inventory

The first thing to understand is that a supplier is a data object that exists outside of any single system. You need to conceptualize it as such to be successful.

This concept is important because it emphasizes that a supplier exists as an independent entity outside of any single system… It has its own unique identity, attributes, and relationships. When you conceptualize vendors as data objects, it helps you:

• Understand that the same vendor might exist in multiple systems but remains a single business entity

• Recognize the need to maintain consistency across all systems where vendor information appears

• Create a more structured approach to managing the attributes, relationships, and hierarchies associated with each vendor

This starts by cataloging the “who”, “what” and “where” of supplier data (your As-Is). This is the first step before launching any governance improvement initiative. You must comprehensively map the supplier data object’s reach in your business:

• What types of supplier information do you maintain today (across all systems)?

• At what stage of the vendor lifecycle is each piece of information collected or updated?

• Who is responsible for collecting, approving, maintaining, and updating each data element?

• Which systems capture each piece of information? (Note: You may have multiple systems for the same data across business units)

• Are all data fields required for all vendors or are some only required for certain categories?

• What category-specific information exists, if applicable?

Cataloging the answers to these questions will give you a deep understanding of the multiple “tentacles” of the supplier data object within your business.

Here’s a list of the systems where supplier data typical resides to give you some leads of where to look:

1. Spend Management Systems. Containing aggregated spend data by supplier from all transactional systems, historical purchasing patterns and trends, spend classification data (PO vs. Non-PO Spend Data), payment history and trends, etc.

2. Sourcing Systems. Containing contact information, legal entity details, qualification/classification data, sourcing event participation history, bidding history and performance, etc.

3. Contract Management Systems. Legal contracts and amendments, service level agreements, renewal dates and terms, contract performance metrics, obligation tracking data, etc.

4. ERP/P2P/Payment Systems. Purchase Orders (POs), Invoices, ordering addresses, payment addresses, bank account information, vendor master hierarchies, mappings to business unit usage, tax information, incoterm specifications, EDI/cXML connection details, etc.

5. SRM Systems. Risk profiles, performance metrics, supplier relationship data and hierarchies, external data (sanctions lists, diversity certifications, sustainability ratings), improvement program and initiative information, etc.

6. Auxiliary Functional Systems. IT asset management (hardware/software by vendor), enterprise risk management (third-party risk tracking and roll up to organizational risk level), quality management systems, regulatory compliance tracking, etc.

As you complete your inventory, you’ll naturally identify incoherencies and improvement opportunities in your current supplier data architecture, roles and responsibilities and governance processes, such as:

• Duplicate vendor records across systems

• Inconsistent naming conventions

• Missing critical data elements

• Outdated information

• Unclear data ownership

• Manual processes prone to error

Once you’ve gathered all your “raw material”, you can get explicit about roles and responsibilities.

Phase 2: Establish Key Supplier Data Management Roles & Responsibilities

Ideally, each piece of critical data in your business has:

• Data Owners. Senior executives accountable for specific data domains and decision-making

• Data Stewards. Subject matter experts who handle day-to-day data quality maintenance

• Data Governance Committee. Cross-functional team that develops policies and resolves issues

• Business Users. Staff who consume data and report issues

• IT Support. Technical specialists who implement and maintain systems

This in and of itself is a lot to ask… But for supplier data, this is made even more complex by the fact that supplier data can (and should) be broken down into sub-domains by business functions (procurement/sourcing data, financial data, risk data, IT and Security data, ESG data, quality data, etc.)

This is why clear role definition is required. It ensures accountability and proper data management throughout a supplier’s lifecycle by the correct stakeholder…

The hardest part of supplier data governance is capturing all of this information in a coherent, digestible format while aligning stakeholders.

Creating a data dictionary is essential here…

The data dictionary defines technical elements and their relationships, the systems in which they are maintained while linking them back to business terms. This enhances understanding of what each piece of data is and how it is actually used in the business.

Without a common language around supplier data, you’ll never graduate to the subsequent maturity steps…

Consider also creating a responsibility matrix (e.g. RACI) that maps data elements from the dictionary to owners across the business.

This can be done in an iterative fashion, starting with a subset of supplier data in the context of a project or function, expanding the model as you onboard stakeholders to this way of thinking/working.

For example, Procurement may take the lead on implementing this new way of conceptualizing the supplier master with critical “corporate suppliers”. They can start by identifying their data points, systems, business rules, etc. ignoring tail spend and what other functions do for now… Then, when they have a good handle on their model, they can extend it to include other supplier tiers or information maintained by other functions with all the additional complexity this implies.

The best way to eat an elephant is one bite at a time…

Phase 3: Assess Your Current State And Maturity Level

As you review your collected data, you’ll naturally drift into defining your desired end state (“To-Be”) at the data object/system/owner level. Formally documenting this desired end stated becomes your high level target architecture.

A helpful approach is benchmarking the different portions of your model against a recognized data governance maturity model (there are a number of them readily available with a quick web search), which typically includes progressive levels such as:

1. Initial: Ad-hoc processes with no formal governance

2. Repeatable: Basic formal processes established but inconsistently applied

3. Defined: Integrated program with standardized processes and clear ownership

4. Measured: Performance tracked with metrics and KPIs

5. Optimized: Continuous improvement with analytics and automation

Understanding your current position on this spectrum helps prioritize initiatives and provide a clear roadmap for advancement. It also gives you benchmarks on which you can “hang” a business case for improvement (if we move from maturity 1 to maturity 3, certain KPIx move from X to Y. Benefits can be calculated based on this change.)

Having this high level target for the whole architecture (without needing to define all the specifics…) ensures that any initiative carried out going forward is tied back to the overall vision… Essentially, you want to avoid having to redo work over time!

That being said, you may have to be opportunistic with improvement initiatives (e.g. working on improvements based on other projects already in the pipeline) or, if the business case is worthwhile, you may be able to launch supplier data specific initiatives for portions of your processes/architecture.

In any case, you want to ensure you’re always trending towards the global architecture vision.

Phase 4: Detailed System Mapping and Technology Selection

Once you've established your high-level target "To-Be" state, you’ll go deeper to map where each piece of information will be captured within each system based on the scope you can attack within active projects.

In the case where you have multiple systems for the same type of information (e.g. 18 ERP systems globally!), some systems require specific fields that don't exist in others, creating translation challenges.

This is why having a data dictionary is essential… Complexity becomes the main issue at this stage… But a pre-defined, overall way of working is how you conquer it!

You will have global rules, applicable to all systems, but you’ll also need to define and capture geographical and/or system based variants of those rules when required.

Supporting all of this with Excel sheets, web forms and manual processes is certainly possible but will be very labor intensive…

As your maturity increases, the need to evaluate and select appropriate governance technology tools will become self-evident. Consider these factors when choosing solutions:

• Scalability to accommodate your data volumes

• Integration capabilities with existing systems

• Data quality and validation features

• Flexibility to adapt based on various levels of data quality

• Workflow management for approvals

• Audit tracking and reporting capabilities

• Total cost of ownership and implementation complexity

You can tackle this phase as you deploy projects that touch the affected systems identified in your high-level architecture. Again, just ensure each project aligns with your overall governance vision.

Phase 5: Implement Governance Processes, Tools and Success Metrics

The final phase involves governing processes in steady state to ensure data is captured, updated, and maintained according to your target state architecture, along with metrics to measure success.

Implementation approaches can range from simple to sophisticated:

• Basic: Excel forms on an intranet submitted through a ticketing system, with manual validation by data analysts who gather appropriate approvals

• Intermediate: Workflow-driven forms with automated routing and approval paths

• Advanced: Master data governance tools that automatically route creation/update requests to appropriate stakeholders and update records in backend systems once approved

And frankly, in any sort of complex organization, all 3 approaches will probably be used at the same time in parallel…

Based on the maturity model you’ve chosen, establish key metrics to measure governance effectiveness and progression over time:

• Number of duplicate vendor records across systems

• Percentage of vendor records with complete critical attributes

• Average time to create/update a vendor record

• Compliance rate with data standards and policies

• Number of data quality incidents related to vendor information

• Business impact metrics (e.g., reduction in payment errors)

Let's be honest about a fundamental truth: most organizations have "bad data" in at least a portion of their business…

The notion that you must achieve perfect data quality before implementing technology solutions is not only impractical but also counterproductive. Instead, look for flexible tools designed to work with imperfect data while simultaneously helping you improve it.

Here are a few examples:

Supplier Data Platforms

External data enrichment platforms can significantly enhance your vendor master data by ingesting high-quality third-party data. These solutions can:

• Automatically validate and update basic vendor information

• Add missing classification data (industry codes, certifications, etc.)

• Identify corporate hierarchies and relationships

• Monitor for changes in supplier status (mergers, acquisitions, sanctions)

The key is finding platforms that use fuzzy matching algorithms and can work with partial or inconsistent information, rather than requiring perfect data to function.

MDG and SRM Solutions

Master Data Governance (MDG) and Supplier Relationship Management (SRM) tools formalize your governance processes through:

• Workflow management for approvals and changes

• Role-based access controls

• Data validation rules and quality checks

• Audit trails and version history

• Supplier performance tracking

When evaluating these tools, prioritize flexibility over rigid structures. The best solutions allow you to configure workflows and data models that match your business processes, not the other way around.

Intake and Orchestration Platforms

These platforms serve as the "front door" for all procurement requests and, yes, that can include vendor data creation and update requests:

• User-friendly interfaces for business users to request new vendors or changes

• Smart forms that adapt based on vendor type and business needs

• Integration capabilities with multiple backend systems

• Automated data validation and enrichment

• Process orchestration across teams and systems

Look for platforms that can manage the complete lifecycle while accommodating your specific organizational structure and approval hierarchies.

If you have bad quality data in certain scenarios, you can build data validation into the transactional business process!

Pragmatic Selection Criteria

When evaluating technology solutions, consider these crucial factors:

• Adaptability to imperfect data: Can the system function with incomplete information and improve data quality over time?

• Implementation approach: Does the vendor offer a phased approach that delivers value without requiring a complete data overhaul?

• Integration capabilities: How easily does it connect with your existing systems?

• User experience: Will business users actually adopt the solution?

• Configuration vs. customization: Can the system be configured to your needs without expensive custom development? Can it be done by the data owners or do you have to rely on IT (and risk capacity bottlenecks…)

Remember, the goal is progress, not perfection.

The right technology should help you improve data quality incrementally while delivering business value from day one.

Don't fall into the trap of believing you need to clean all your data before implementing a solution. Choose technology that's built around the reality of imperfect data.

Remember: It's a Continuous Improvement Journey

Supplier master data management is always a work in progress.

Having a clear high-level target state is essential before launching any related projects, but recognize that your governance approach should continuously evolve as you mature.

Implement a cycle of regular review and refinement:

• Schedule quarterly or bi-yearly assessments of your governance processes

• Gather feedback from data stewards and users

• Monitor your metrics and KPIs to identify areas for improvement

• Stay current with evolving business needs and regulatory requirements

• Reassess your maturity level annually to gauge progress

And then?

Start over from phase 1!

Your job is to build the best supplier data “sand castle” as possible… It’s long to put in place… It’s fragile… But it provides magnificent returns when you get it right!

This continuous improvement mindset ensures your governance framework remains relevant and effective as your organization evolves.

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