Historically, it was the norm for organizations to believe that the quality of data in BigData environments were not relevant or necessary. It was believed that one could get good results from the exploitation of data without caring about quality.
Once it was detected that the information obtained was carrying errors due to the poor quality of data, an attempt was made to alleviate the issue by profiling data. This significantly increased the cost of improving the quality, adaptation, and maintenance of the data. 
This became more relevant with respect to streaming data processing, in real-time or similar, especially in the artificial intelligence environment. To put it simply, there is no time to profile, correct or modify data. 
The time between data ingestion and consumption is kept to a minimum. If the data is a bad load of data, it will be consumed badly. If a business relies on analytics based on poor quality data the consequences can be devastating. This can lead to marketing campaigns with the incorrect targets, mismatches in the production or supply chain, the incorrect movement of robots, as well as other errors in planning, forecasting and simulation.

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Aqtiva was created to provide solutions to the issues surrounding data quality. An error at the source can generate expensive errors at the destination. Aqtiva seeks to provide assistance to businesses in environments that process information in real-time and where owners of the information do not have time to verify the quality of the information.

 

Based on machine learning, Big Data and predictive analytics algorithms, Aqtiva provides data quality management at the intake time. This provides a real-time data quality and auditory mechanism for any information processing system.

 

Shareholding Structure

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