Data Anomaly Detector
modernize data anomaly detection by identifying unusual patterns in live data feeds
Fulcrum’s ML-driven Data Anomaly Detector, better known as “DAD”, applies cutting edge machine learning applications and data visualization to information technology systems historically run by SQL logic. It requires minimal user input or oversight, and becomes more effective as the user interacts with it. DAD leverages a blend of user context knowledge and machine learning methodology to automatically hone in on outliers and less obvious long-term trends or patterns in data.
Graphical User Interface
A Graphical User Interface (GUI) guides the user through a series of relatively simple prompts and the user-provided knowledge then gets incorporated into the underlying models.
With each facet of each new record, the tool conducts backend predictive checks to quantify the extent to which the observed data deviates from model expectations.
Deviations deemed statistically significant are noted as anomalies and recorded along with the level of significance.
Machine Learning to fit your business
DAD streams new data record-by-record to incrementally update Bayesian machine learning models for various facets of each new record
User Context and ML
DAD leverages a blend of user context knowledge with the ML models to become increasingly effective in spotting anomalies as the user interacts with it
DAD is customizable in both its anomaly detection tuning and dashboard reporting, and is designed to work with minimal user input
DAD can be hosted within a client’s own technical environment or within one of Fulcrum’s cloud-based environments
The team behind Fulcrum Impact is a seasoned and dedicated mix of data scientists, engineers, and developers producing meaningful insights from complex data.