How we ensure data extraction with Quality

Every data we harvest will go through removing incorrect, corrupted, incorrectly formatted, duplicate cleaning steps. Irrelevant data row and structural errors are removed automatically resulting clean dataset. Our service includes unwanted outliers' filters and missing data re-scraping for every data to skip missing. Firstly, the data is auto verified with AI power verification and secondly highly trained QA team to ensure that every single record is as accurate as possible.

We use a number of methods to ensure data extraction and quality:

We thoroughly inspect the source data to identify any issues that could impact data quality

We use standardization techniques to clean and transform the data

We use validation techniques to check the accuracy and completeness of the data

We use monitoring and auditing to track changes to the data and ensure data quality over time.

These methods help us to ensure that the data we extract is of the highest quality and can be used confidently for decision making. Data extraction is a systematic process of transcribing key information from primary studies included in the review. Extraction is done by two reviewers independently to increase accuracy. When extracting data, we make many test extraction to validate the data. This ensures that only reliable and high-quality data are used in the final review.

To assess data quality, we randomly take sample and review if there is acceptable range of values for a given field. As part of a recurring data quality process data schema are defined with quality alert notifications. Data from source and destination are split tested to manually verify accuracy. We monitor and manage data quality on a routine basis to ensure the best result. Our automated quality-monitoring and data-cleaning processes ensure we always deliver high-quality and consistently reliable data.