Ensuring high analytics accuracy starts with continuous monitoring of data quality. My approach involves a few key steps. First, I establish automated validation rules for accuracy, consistency, and completeness across data pipelines. Then, I implement real-time monitoring using tools like Python or SQL scripts to flag any anomalies early. Next, I integrate data drift detection to track changes over time, ensuring models remain reliable. Finally, I set up automated feedback loops to address quality issues as soon as they arise. This method not only ensures data integrity but also enhances model performance and aligns with my focus on proactive, data-driven solutions.

Continuous monitoring of data quality is a deliverable of setting up an internal organisation Data Analytics user group where there are technical and business stakeholders. The business stakeholders will refine on an ongoing basis the business rules which define quality. The technical stakeholders will provide then mechanism for cleansing, tracking and reporting on the data quality issues.

Ensuring analytics accuracy requires a proactive and structured approach. I rely on automated tools for continuous data validation and anomaly detection, which quickly flag inconsistencies. Setting up alerts for unusual patterns ensures I can address issues promptly. Regular audits of data sources and processes help maintain quality over time. I also emphasize creating a robust data governance framework with clear ownership and accountability for data quality. Using dashboards to monitor key metrics in real time provides ongoing visibility. Most importantly, fostering cross-team collaboration ensures everyone understands the importance of accurate data and contributes to its integrity.

The data quality process consists of a minimum of 4 steps: 1. data profiling - with the help of appropriate tools, we determine what data we have, what patterns, limitations, etc. exist in it. 2. we determine the expected quality of the data by describing it usually in a data dictionary type tool 3. improve data quality using data quality tools or code in ETL processes 4. monitor changes in data quality levels using reports such as in Power BI And it is this last report that allows us to maintain continuous monitoring of data quality

Incorporate Statistical Monitoring Use statistical methods to monitor data distributions and trends over time. - Mean and variance checks: Identify unexpected shifts in data values. - Outlier detection: Flag extreme values that deviate from historical norms. - Trend analysis: Compare current trends against historical data for deviations. Statistical monitoring adds an additional layer of oversight, enhancing anomaly detection. Enable Real-Time Dashboards Deploy real-time dashboards to visualize data quality metrics and anomalies. -Error rates: Percentage of erroneous or incomplete records. -Anomaly trends: Patterns of unusual data behavior. -Timeliness: Lag between data collection and availability for analysis.