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Automated Data Cleaning
involves the application of machine learning to accomplish the data cleaning objectives of modifying or removing data that is formatted incorrectly, incorrect, duplicated, incomplete, or flawed in some other way.
It is estimated that up to 90% of time spent in the data science life cycle is spent on manual data cleaning efforts. This underscores the massive opportunity in correctly applying machine learning to alleviate data cleaning pain points.
The notable stages of data cleaning include:
- Standardization of data
- Validation of data
- Deduplication of data
- Analysis of data quality
Here at Diffbot, our unique Natural Language Processing and Machine Learning-enabled platform pulls in facts from over 98% of the web. Our platform exceeds industry expectations regarding automated data cleaning through the following stages:
- Initial crawl of locations from which data will be extracted
- Classification of pages to understand the context in which on-page data lives
- Extraction of data
- Natural Language Processing including relation extraction and coreference
- Data integration including the linking of different records and the constitution of a more finalized entity through data fusion
Our automated data cleaning efforts are informed by both the comprehensiveness and accuracy of data extracted from the web, with our automated data extraction routinely outperforming manual (human) research on the same topics.