DELOITTE
Supplier Data Management as a Service
Are you demanding enough from your AI data management strategy , and can Machine Learning ( ML ) solve data gaps ?
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 .
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 .
Material Data Management as a Service
Is your cross-platform data management strategy keeping stakeholders and vendors aligned ?
Imagine a Business Unit ' s Analyst , who is able to eliminate phone calls and email-based data aggregation , with data governance , collection and resolution being managed through chatbotbased workflows . Vendor attributes and owners are cleanly aligned , with automated prompts across the diverse stakeholders to keep records up to speed .
Today ' s Workforce / Process Snapshot
Material Data Management for new material procurement , today , requires a complex work process across a multitude of internal groups , including Finance , R & D , Sales , Supply Chain , and Procurement . The Business Analyst today frequently is faced with managing and resolving inconsistent , inaccurate , or incomplete data entries , made more complex by nonstandardized data governance or data approvals processes . The net result is poor reference data , manual data population , missed vendor negotiating opportunities , as well as an inconsistent , internal book of record . aimagazine . com 105