View From the Top by Bud Walker CIO, Melissa
The transformative power of GenAI promises to drive real-time business success, from enhancing data-driven insight and productivity to improving fraud detection and customer experiences. However, if data quality and semantic fundamentals aren’t well managed, GenAI applications can return
clearly inaccurate gibberish, or worse, biased results that can lead to GenAI “hallucinations” and chaos when applied in real-world applications.
At Melissa, we believe effective AI requires clean data. Every GenAI project should start with active data quality and core data quality management, including profiling, deduplication, cleansing, classification, and enrichment for outcomes you can trust.
Unison by Melissa does just that. It’s a visual, low-code data quality platform that makes it possible to generate and apply active rules to cleanse, integrate, harmonize, and enrich data referenced by your GenAI application. Using a solution like Unison helps you ground your AI projects in clean, meta-data rich business data and curated, meta-data rich reference data for training, and in semantically informed results supervision.
Melissa provides “Gold Standard” reference data to complement training and downstream supervision by comparing outcomes with expected reference data patterns and content to flag or correct potential errors. Melissa curates and integrates multiple sources to create the highest quality demographic, customer, firmographic, and geographic reference data resources.
With almost 40 years of experience overcoming the toughest data quality challenges, Melissa has the tools, reference data, and resources you need to hallucination-proof your GenAI applications.
To learn more about how you can partner with Melissa to kickstart a data quality strategy and build trust in your GenAI implementation, or to speak directly with one of our expert team members, click here.
Melissa
www.melissa.com