Real-Time Supplier Data

Are you demanding enough from your AI data management strategy, and can ML solve data gaps?
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Imagine a Business Data Steward who is able to access one, true Supplier Master Data across the enterprise. Reference templates, and data are readily available, minimizing manual data entry. The Steward is able to validate data in real-time across business and regional units, and provide recommendations to optimize enterprise outcome. Solving data gaps becomes a function of the past, as ML connects the dots for commonalities across the business.

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Today’s Workforce/ Process Snapshot

Supplier data streams and operational steps are increasing due to higher vendor counts and reporting / data requirements. Today’s processes are managed over communications, email, and manual recollections and data entry, frequently with low levels of data governance. Poor data governance accelerates repeat entries and missed commonalities across business units, impacting Procurement, lost opportunities for contract optimization, and different product orders placed within or across business units or regions. Business Stewards’ job satisfaction is impacted, as their time is absorbed in manual retracing and entry of data, frequently being caught in the middle of internal debate, due to incomplete or manually over-written master data.

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Solution Components

Solution components include:

– Coherent master data management supported by AI/ML-powered chatbot based workflows
– Self-generating, touchless, AI-driven Master Data management
– Self-healing data supported by ML-driven data quality standards
– An Immutable Master Data Ledger, featuring intelligent, automated, trusted, and validated data governance structure across the enterprise
– Cross-ERP support

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Target Outcomes

Supplier Data Management can deliver:

– Reduced effort for Data Governance
– Data TCO reductions
– AI/ML-based predictive algorithms to autonomously pre-populate high volumes of data attributes

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