Data Quality Management – Guide

Data Quality Management – guide

Data quality management involves cataloging data assets, ensuring linearity in processes, reporting on performance, integrating with other systems for seamless operations, and maintaining security measures to protect sensitive information.

Key aspects include defining rules for profiling data, setting up alerts for anomalies, and establishing clear remediation strategies including root cause analysis (RCA) plans, automations, and documentation.

Privacy & Data Governance – guide

Data contracts are essential in defining the terms under which data is shared or used. Rules for data quality encompass completeness, validity, uniqueness, timeliness, and consistency to ensure that the data meets specific standards.

Maintaining privacy involves implementing measures such as protecting personally identifiable information (PII) through masking techniques, enforcing role-based access control (RBAC), and conducting regular audits with logging of activities.

Profiling/alerts: SLA/SLI, channels/priorities, templates.

Profiling involves analyzing data to understand its characteristics and detect anomalies. Alerts are set up based on service level agreements (SLAs) or service level indicators (SLIs) to notify stakeholders of issues in real-time.

Channels for alerts include email, SMS, or dashboard notifications with prioritization based on the severity of the issue. Templates are used to standardize alert messages and ensure consistent communication.

Frequently asked questions