Vendor data is essential for managing your supplier relationships, assessing their performance, and ensuring compliance with your contracts and regulations. However, vendor data can also be complex, dynamic, and prone to errors and inconsistencies. How do you ensure reliable vendor data that supports your vendor management goals and decisions? Here are some tips and best practices to help you.
A vendor data governance framework is a set of policies, processes, roles, and standards that define how you collect, store, validate, update, and use vendor data. It helps you ensure data quality, accuracy, completeness, and consistency across your vendor management system and other data sources. A vendor data governance framework should also align with your business objectives, vendor management strategy, and compliance requirements.
Make sure to build in governance for the unexpected. Examples include: 1)data from a vendor is not updating in line with the SLA. 2) algorithm driven threshold checks for massive swings in the data suggesting an upstream corruption or processing issue.
A vendor data management system is a software tool that helps you centralize, organize, and automate your vendor data collection and maintenance. It allows you to create and manage vendor profiles, contracts, documents, invoices, performance metrics, risk assessments, and other vendor-related data. It also enables you to integrate your vendor data with other systems, such as your ERP, CRM, or BI tools, to enhance your vendor analysis and reporting capabilities.
The heart of any system is what's required for master data management. Don't sacrifice doing that well. Match entities well. Do it at scale. Focus less on the management aspect of the vendor because it's most likely your organization has a central system for vendor management. Focus on what counts in a system like this.
Vendor data quality metrics are indicators that measure how well your vendor data meets your expectations and standards. They can include dimensions such as accuracy, completeness, timeliness, consistency, validity, and relevance. You should define and monitor vendor data quality metrics regularly to identify and address any data issues or gaps that may affect your vendor management performance and outcomes. You should also communicate your vendor data quality expectations and metrics to your vendors and hold them accountable for providing accurate and timely data.
Link this with TPRM (Third Party Risk Management) and build penalties in the contract for delay in providing data and inaccuracies in the data.
Vendor data audits and reviews are periodic checks that verify and validate your vendor data against your vendor data governance framework and quality metrics. They help you detect and correct any data errors, inconsistencies, or discrepancies that may compromise your vendor management processes and decisions. You should conduct vendor data audits and reviews at different levels and frequencies, such as at the vendor level, the contract level, or the system level, and according to your risk profile and data sensitivity.
Try and build your algorithm that's detecting data anomalies so this isn't a special or extra activity. Work off the premise of continuous governance. An audit is after the fact , the damage is already done. It's better than nothing , but not by much.
Your vendor data stakeholders are the people who are involved in or affected by your vendor data management activities, such as your vendor managers, procurement staff, finance staff, IT staff, vendors, and senior management. You should train and educate your vendor data stakeholders on the importance, benefits, and challenges of vendor data management, as well as on the roles, responsibilities, and best practices of vendor data governance. You should also encourage feedback and collaboration among your vendor data stakeholders to improve your vendor data management processes and results.
I have got a 10 pages case study on Vendor Data Management and am happy to share it (not sure how to include it here). The job of a Vendor Data Manager is underestimated. I suggest a person with the following skills: 1. Education: bachelors/ masters 2. Experience: 10+years 3. Analytical skills 4. Communication and collaboration skills 5. Project/ Program management Skills 6. Regulatory and compliance knowledge 7. Proficiency in data management tools 8. Negotiation skills 9. Attention to details 10. Ethical and transparent 11. Leadership skills 12. Influencing skills
Vendor data, Keep it current- contractual and operational with clear demarcation. Data governance should also point out to frequency and relevancy of such records. Piling up data without meaningful and contextual insights is a waste of $. Period review and assessment is a must.