Kore Integrate is the perfect platform to improve Data Quality Management, said Maxwell Dallinga in a recent blog post by Kore Tech.
Data quality management is a process to ensure that data is reliable and effective. It’s the pillar upon which AI sits in order to give accurate information.
Kore Integrate’s features are designed to integrate data between Rocket U2 (UniData/UniVerse) or ODBC data sources to Microsoft SQL Server, providing DQM as a result of the process.
These features include:
Quick Start Workbench
This feature helps provide data profiling by identifying gaps and inconsistencies that could affect data quality, allowing for a proactive approach to providing quality data for AI.
Kore SQL Error Management (KSEM)
KSEM provides consistent data quality assessment by continuously monitoring, logging, and identifying data errors. This allows areas of improvement to be identified which can improve the functionality of your AI.
ETL (Extract, Transform, and Load)
The ETL process allows for both data cleansing and standardization by transforming data into standardized, high-quality datasets. Especially through the transformation stage, data will become uniform and error-free before reaching the source used by an AI model.
Translation Tables
As an important step in the ETL process, translation tables are useful to provide data enrichment (and standardization). This is done by allowing users to design a system that fills missing fields and translates existing fields for a harmonized and consistent view of data.
Through comprehensive DQM practices, users can improve the operations of their business and the functionality of enabled AI.