What common data quality issues arise in aeolytics and how can they be mitigated

In aeolytics, common data quality issues include incomplete data, data inconsistency, and inaccuracies due to faulty sensors. Incomplete data can result from communication failures or power outages, leading to gaps in time-series data. Data inconsistency might occur when integrating datasets from different sources, especially if they use varying units or formats. Sensor inaccuracies can arise from equipment degradation or calibration errors. To mitigate these issues, regular maintenance and calibration of sensors are crucial to ensure accurate measurements. Implementing redundancy in data collection systems can help address communication failures, ensuring that data is backed up and can be retrieved if primary systems fail. Employing data validation and cleaning processes can correct inconsistencies and fill in gaps, using statistical methods or machine learning algorithms to predict missing values. Furthermore, standardizing data formats across systems and enforcing strict data governance policies will enhance data quality and reliability. Key Takeaway: Mitigate aeolytics data quality issues with sensor maintenance, data validation, and standardized data governance.