Challenges in implementing anomaly detection include:
Defining normal data patterns: It can be difficult to establish what constitutes "normal" behavior in complex datasets.
Handling false positives: Algorithms may sometimes flag normal data as anomalous, leading to unnecessary corrections.
Scalability: As data volumes grow, ensuring anomaly detection systems can scale to meet the increased demand can be challenging.
Heterogeneous data sources: Data from various platforms and formats may require tailored algorithms to effectively detect anomalies.