Real World Data Quality Issues: #6 in This Series
Managing millions of customer records requires precision. If you have 1,000,000 records with a 20% duplicate rate, you are looking at 200,000 redundant entries. Manually matching and merging this volume is cost-prohibitive, yet automated merging requires absolute trust. If an AI decision merges 100,000+ records incorrectly, there will be trouble.
What is Interpretable AI?
Interpretable AI refers to models where the internal mechanics are inherently understandable to humans. The logic is transparent before a decision is made.
- Key Benefit: Users can audit the “why” and “how” of a match upfront
- Outcome: High trust and control over automated merges
What is Explainable AI?
Explainable AI (XAI) uses complex techniques to explain “black box” decisions after they have occurred. The model remains a mystery, and the justification happens only after it has merged your records.
- Our customers do not want to have to justify their actions or the actions of an AI model
Case Study: The Danger of “Black Box” Duplicate Detection
A major retailer recently attempted to use a duplicate detection engine from one of the “Big Three” cloud providers. Despite being purpose-built for data matching, the black-box algorithm produced unusable results, matching a very high percentage of non-duplicate records.
There were discussions about how the model would probably do better given higher volumes of data. Those discussions didn’t sway anyone and the company abandoned the model.
That’s when they called us.
Data Studio by Acme Data: Trust Through Transparency
Our customers don’t want to guess why their data is changing. Data Studio utilizes Interpretable AI to ensure you understand the decisions the AI makes before a single record is merged.
If you could use some help with data quality, contact us. We’re easy.