the first platform of data quality in the ingestion phase, open to any data base technology, either for architectures Cloud/Multicloud or OnPremise.
It is scalable, non-invasive and can be implemented in environments in real time or batch.
A few years ago it was usual that organisations thought that data quality in Big Data environments was not relevant or necessary. It was thought that you could obtain good results by using data without concern for quality.
Once noticed that the obtained information had errors because of the bad data quality, attempts were made to solve the problem using data profiles. It increased significantly the cost to improve quality, the adaptation and maintenance of data.
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Data quality is crucial when it comes to streaming, real time or similar data processing, especially in the artificial intelligence environment. In short: there is no time to refine, correct or modify data that have entered the database.
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"I am project manager in the consumer goods area and today, thanks to quality by Mática, I am able to implement more than 100 quality rules from my external sources - Nielsen, Kantar and INQVIA - in the same amount of time that a data engineer used to code, test and deploy just one of them "
Talking about data streaming means that the time between the data loading and the consumption of data is reduced to a minimum. If the data are wrong they will be consumed wrong too. If a business is based in data analysis with poor quality the consequences could be catastrophic. This situation can lead to marketing campaigns with wrong goals, imbalances in the supply chain, the wrong movement of robots or other errors in planning, forecast and simulation.
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quality by Mática was born to provide solutions to the problems related to data quality. An error in the origin can produce expensive mistakes in the destination. quality by Mática seeks to assist companies in environments that process information in real time and where the owners of information do not have the time to verify the quality of the information.
It is based on automatic learning, Big Data and predictive analysis algorithms. quality by Mática manages the quality of the data in the loading process. It provides a user-friendly and quality mechanism to measure data quality in real time for any information processing system.
evolución
In 2018 we started with the first tests of what today is Aqtiva and we saw its potential. We started our financing search, that we finally found in 2019, from CDTI (Centro para el Desarrollo Tecnológico Industrial) in the framework of the call for R&D projects Transferencia Cervera and the support of Centro Tecnológico Gradiant. The project IA2C will finish during the first months of year 2021, but many of the developments and R&D innovations have been implemented in quality by Mática.
En octubre de 2020, los fundadores de Mática Partners, constituimos Aqtiva Data Tecnologies SL con el objetivo de evolucionar y distribuir quality by Mática pero ya bajo el nombre comercial de Aqtiva.
In October, the founders of Mática Partners established Aqtiva Data Tecnologies SL with the aim of evolving and distribute quality by Mática under its trade name: Aqtiva.
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For further information click here to visit Aqtiva.ai webpage
am I loading mistaken or weak data in my business BigData?
how much will cost to code 50 data quality rules in each one of my ETLs?
can I implement my own quality rules if I am a user?
can I trust the expensive data profiling or data cleansing procedures?
can I consume data which has been ingested and authenticated miliseconds ago?