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1. Automated Data Quality Checks 2. Implement Real-Time Monitoring 3. Data Logging and Auditing 4. Set Up Alerts Integrate alerting mechanisms via email, Slack, or SMS when anomalies exceed thresholds. 5. Regular Data Reviews Conduct meetings with stakeholders to ensure data quality concerns are addressed collaboratively. 6. Implement Data Governance Policies Define clear ownership and responsibilities for data quality. 7. Redundancy and Backup Implement redundancy in your data pipelines to prevent anomalies caused by hardware or software failures. 8. Evaluate and Improve Continuously assess the effectiveness of monitoring systems and refine them based on new patterns or changes in data sources. 9. Train your team in best practices.

• Real-time anomaly detection which continuously monitoring data as it is generated or entered into the system to immediately identify any irregularities. • Use Alerting Tools which detect anomaly and alert immediately. • Implement validation rules at data entry points to ensure that only valid data is entered • Use automated tools to scan through the data and identify anomalies • Regular data audit • Always maintain up-to-date backups so you can restore your database to a previous state if anomalies do occur. • Properly train the personnel who will be interacting with the database. Provide detailed documentation to avoid human errors which can lead to data anomalies.

To ensure the prompt detection of data anomalies, proper monitoring is to be set up along with immediate alerts, so as to cater the need to get those resolved. Finding the root cause of these anomalies is the most important part, having a proper RCA and how that can be mitigated ensures data quality. Data governance is something to be done once we have proper understanding of the data. There are multiple tools to support data quality like graphana, prometheus etc.

A few things to consider: include monitoring and alerting in the definition of done, define SLIs and SLOs for your data workloads, run blameless post-mortem meetings after every incident to foster continuous improvement, and instrument the incident response itself to track and celebrate those improvements.

Few things that can help in detecting and improving data anomalies handling 1. Build a comprehensive observability framework to include sensitive data indicators. 2. Build rule based corrections for common patterns. Keep refining and adding on a continuous basis. 3. Set up SOP and patterns to address manual data corrections 4. Setup continuous monitoring and dependency discovery.

- Use modern tools, such as Databricks, to ensure data quality. - Actively involve business teams in the construction and review of pipelines. - Adopt observability tools, such as Great Expectations, to monitor and validate data automatically. - Implement a centralized catalog to track metadata and data lineage. - Prioritize data governance with proper access controls. - Promote a data-driven culture, encouraging the use of data as the foundation for strategic decisions. - Empower teams by providing training and tools to efficiently handle data. - Encourage continuous feedback cycles to adjust and optimize pipelines. - Conduct regular audits to identify improvements and keep processes up to date.