ASTREA

Automated Data Cleanser
Clean Data Results in Accurate Insights

Astrea

Automated Data Cleanser
Clean Data Results in Accurate Insights

Astrea cleans data automatically and uses its deep learning capabilities to detect issues and alert users.

This enables the availability of quality, error-free data, which in turn will drive more meaningful insights quickly without the need to spend hours in sanitizing data.


Your journey onboading astrea

Astrea combines checks for user-provided constraints for known business logic with unsupervised learning to perform tasks like imputation and error detection and correcting/fixing them to produce high-quality data sets. This data can be validated easily and used for reporting, analytics, and modelling.

Value Propositions

Self-supervised
learning

Learns features of a dataset without defining a specific task like classifying according to a label field

Regularizing data constraints

Applies business logic to defined constraints to form a common basis when analyzing data

Active
learning

Enables user feedback through acceptance or rejection of multiple suggestions, supporting continuous model improvement

Comprehensive error detection

 Detects multiple types of errors and omissions ranging from missing values, typos, to numerical and categorical mismatches and fixing them

Success Stories

A supply chain company was successful in automating its processes for on-demand production projection views by its global sales team with a goal of reducing cost and time spent to sort and analyze data. Trabeya leveraged Astrea to automatically clean data and added its deep learning capabilities to detect issues and alert users dynamically, to ensure they were free of errors.

This livestock business did not have real-time insights because its transactional systems weren’t optimized for analytics. Instead of building a data warehouse or a data lake, powered by data marts or cubes to serve individual analytics use cases, we were able to unify the semantics of their data. The ability to dynamically clean and detect errors enabled quality, error-free data, driving more meaningful insights quickly without the need to spend hours in sanitizing data. As a result, the company was able to reduce cost by more than 3x.

An organization that owns and operates a large cattle herd across multiple cities successfully moved a step ahead by enabling its recorded data was error-free.Even though it was successful in completely replacing its manual supply chain processes to record and maintain data, forecasts were not always accurate and required additional work by the team to clean and sanitize the available data for forecasting. This helped to increase weekly forecast accuracy by 25% initially due to the prevailing poor quality of data.

Trabeya used Astrea to combine checks for user-provided constraints for known business logic with unsupervised learning to perform tasks like imputation and error detection and correcting/fixing them to produce high-quality data sets. This data could then be validated easily and used for reporting, analytics, and modelling.

products

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