Illumination Works’ ADCAT™ solution offers a powerful, time-efficient, and automated solution for healing data errors to ensure accurate reporting and predictions
ADCAT is an enabling technology to automate data error healing or provide correction recommendations with corresponding explanations, resulting in higher quality data and driving higher confidence decision-making and improved productivity
Key Benefits of ADCAT
- Proactive cleansing at point of data entry or post processing
- Improves analyst productivity due to less time correcting data
- High quality data enables confident decision making
- Competitive advantage from having timely, accurate, and complete data
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- Ensemble machine learning approach automates data tagging and cleansing for accurate decision making
- Single, standardized capability for enterprise-wide data cleansing and human-in-the loop recommendations
- Descriptive, predictive, and prescriptive analyses using error-free data results in more accurate insights and improved data-driven decisions
- TRL 7 solution deployed in Navy environment
- Primed and ready for DoD use with Phase III SBIR
Tradewinds Marketplace Awardable Solution
ADCAT comprises four components with customizable subcomponents
Data Quality Engine
Data profiling and error detection via business rule comparison and outlier analysis
Transformation Pipeline
Suite of methodologies to ready data for machine learning error correction methods
Prediction & Explanation Engine
Automatically heal data or provide correction recommendations with explanations
Model Quality Modeling
Model quality trending and automatic alerting for model retraining
ADCAT provides robust, optimzed, abstracted processes for easy scaling across domains
ADCAT is a non-proprietary solution with all source code available without restriction to DoD customers and applicability across many other industries
- Robust. Leverages multiple methods to use the best approach for the situation
- Holistic. Applicable to a variety of data sets and pipelines
- Extensible. Modular framework customized to fit the data at hand
- Flexible. Platform agnostic across different environments
- Trustworthy. Models with 90%+ accuracy for high confidence error correction
- Transparent. Human-understandable, AI-driven error correction
Ready to start your ADCAT self-healing data use case?
Schedule your personalized demo to experience ADCAT in action!
Reach out today!
Jan Turkelson, Senior Vice President
Janette Steets, PhD, Director of Data Science
Scott Rutledge, Government Director
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