Put your Data to Work

GARBAGE IN - GARBAGE OUT

It should be clear to everyone. Unstructured and poor data substantially limit the accuracy and effectiveness of artificial intelligence. When it comes to AI, "GARBAGE IN - GARBAGE OUT" applies even more. While procurement leaders eagerly invest in cutting-edge AI solutions promising revolutionary insights and automation, many are building these initiatives on quicksand. The uncomfortable truth is, that most organizations' master data - the fundamental information about products, suppliers, contracts, and spend categories - resembles a digital junkyard more than a strategic asset. Consider this scenario: Your AI recommends consolidating item "123.564.22" and "234.678.66" and "423.567.66" - not even knowing the material, the shape, the complexity and the surface treatment of these items. The result? Incorrect consolidation opportunities, inaccurate spend analysis and AI-Recommendations, that undermine - rather than enhance procurement performance.

Problems Multiply

Traditional procurement processes can function reasonably well despite unclear data, as human judgment can compensate for inconsistencies. However, this intuitive ability to correct errors is lacking, when it comes to predicting costs, predicting categories, predicting customs tariff numbers, etc. Artificial Intelligence also processes incorrect data, meaning that poor data quality is amplified in every algorithm, prediction, and recommendation. Organizations with clean, comprehensive corporate data gain exponential advantages from AI implementation. They can identify savings opportunities faster, predict costs, automate processes faster and more accurately. Meanwhile, companies struggling with data quality issues, find their AI investments delivering marginal or even negative returns.

The Solution: UPR - Unified Part Representations

Before implementing an AI solution, the data generation layer - the layer that converts corpoare data into a form, that can be directly used by AI - must be in place. Otherwise, all “new data" is useless, because it cannot be used by the AI models. A new data-layer is needed, to extract, to unify and combine data from familiar "silos" such as PDM, PLM, and ERP-systems.

The Luminarity UPR - Intelligence Board provides state-of-the-art functions to measure data quality in context to AI - globally

Our recommendations:

• Take care of the data generation layer and take a look at our UPR - Unified Part Representation. With Luminarity, the UPR is "ready to use" for your specific AI-solution or (of course ...) the integrated Luminarity AI.
• Define clear responsibilities and accountabilities for the content and quality of UPR - Unified Part Representations. For example, there are two roles in Luminarity: 1) The role of the administrator, whose task is, to generate the perfect UPR from the product description data. 2) The role of the AI-User, who completely automates core processes such as article classification (autonomous category management) and, for example, cost forecasting (autonomous costing).
• Implement mechanisms for AI to identify and flag potential data quality issues. This creates a virtuous cycle, where AI both benefits from and contributes to improved data quality. Luminarity provides the "UPR Intelligence Board" for this purpose. The UPR Board lists all transformed data (the UPRs) according to defined quality requirements. The “Global UPR Quality Index” enables global measurement of product-descriptive data quality within the company (see picture).

The ROI of Getting It Right

Organizations that follow these recommendations achieve the following effects:

• 30-50% improvement in AI recommendation accuracy
• Faster time-to-value for new AI initiatives
• Reduced manual intervention in automated processes
• Enhanced compliance and risk management capabilities

The Conclusion: Data quality is a strategic advantage. Companies, which want to be successful in the age of AI are those, that recognize the quality of corporate data as a strategic necessity - not just as an operational necessity. While competitors struggle with “garbage-in, garbage-out” scenarios, forward-thinking companies build competitive advantages through superior data foundations. The question is not, whether your company will eventually have to address product-data and master-data quality, but whether your company will proactively tackle this challenge as a springboard to AI success - or reactively view it as an obstacle to be overcome. The decision you make today, will determine whether AI becomes your company's superpower, or your most expensive disappointment.