Real-time machine data

­Employ various mathematical methods for data validation, utilizing predefined rules to assess data against established thresholds, trends, and patterns. Correct or notify relevant users of irregularities and improve OT data quality.

Acceptance of data changes

­Make it possible to manually enter and edit the data. Control the process by configurable acceptance workflows including confirmation and publication of such data. Execute validations on manual data entry and track the history of data changes.

Business and relational data

­Give access to pre-configured data sets, as required by your organization structure and data security policies. Precisely define the role of each person or organization unit in the process of data quality assurance workflow.

Advanced data validation

­Incorporate machine learning in data quality checks. Use algorithms learning from validated data, detecting anomalies and improving accuracy through adaptation. Enable rapid recognition of unforeseen scenarios thanks to digital twin capabilities.