Spotless measure the quality of datasets according to specification against a set of business rules. The business rules are automatically generated using machine learning algorithms and can be customised to meet the appropriate business requirements.
Typical applications include:
- Integration datasets from different providers - Spotless will validate that each feed is well formed and consistent with other feeds
- Loading data into a data lake - although full transformation is not required on data ingested into a data lake, simple validation using Spotless to ensure the data is well formed and well referenced saves significant time downstream
- Cleansing data scraped from the internet - web scraped data is notoriously irregular, and Spotless will ensure that no information that does not conform to the expected specifications is loaded
- Integrating internal data from different platforms - you can define specific business rules to provide consistent and valid data whenever it is loaded
- Truncating or removing overlapping sessions; extended or filling sessions with gaps between them