We’ve all heard the old expression “garbage in, garbage out.” Messy, incomplete data put into automation and AI systems will produce poor outputs that are impossible to make good sense of. To avoid this, organizations need to ensure that their data meets essential quality dimensions:
Completeness: Data sets must contain all necessary information for accurate analysis and action.
Consistency: Conflicting records in data turkey rcs data can lead to costly automation errors.
Timeliness: Data must be up-to-date to ensure current and relevant decision-making.
Accuracy: It’s critical that data correctly represents real-world conditions.
Healthcare: If automation and AI systems have incomplete patient history data, they could make inaccurate treatment recommendations.
Financial services: Low-quality transaction data could trigger unnecessary fraud alerts or overlook actual threats, impacting both security and customer trust.
Retail: Automation might reorder inventory based on faulty sales data, leading to stockouts or overstock.
Example: AIOps and Security Alert Monitoring
Here’s an example at the confluence of automation and AI: Imagine a company is leveraging an AIOps platform to automate its IT operations. A big component of the AIOps tech is security alert monitoring.