Kore Integrate empowers organizations to achieve high data quality when moving data from Rocket UniVerse/UniData-based and Open Database Connectivity (ODBC) systems to SQL-related environments using data quality management (DQM) methods. Kore Integrate’s DQM features foster data quality and consistency – key requirements for organizations seeking to leverage artificial intelligence (AI).
Schedule a meeting with our team to discover how Kore Integrate can empower your organization to foster quality data, which helps reduce data-error costs, ensure efficient AI modeling time, and achieve reliable and accurate AI models.
Complete, accurate, and consistent data lead to more reliable insights and forecasting.
Reduce data preprocessing time, which allows developers and data scientists to spend less time cleansing data and more time refining models.
Mitigate errors and inconsistencies in AI model training.
Minimize faulty AI results that require costly rework by preventing data errors upfront.
Kore Integrate enables organizations to create an AI-ready data environment where clean, consistent, and high-quality data powers advanced analytics and AI-driven insights. The platform’s robust DQM capabilities are crucial for enterprises looking to maximize the value of their AI investments, ensuring that only well-prepared data feeds into models to reduce error rates and enhance predictive accuracy.
Harness the full potential of AI-driven insights through Kore Integrate features such as ETL (Extract, Transform, Load), Kore SQL Error Management (KSEM), Quick Start Workbench, and Translation Tables.
The Quick Start Workbench feature in Kore Integrate streamlines data integration projects, providing ready-to-use templates and workflows for setting up data warehouses and operational data stores. This feature accelerates the process of data profiling, providing early identification of data gaps and inconsistencies that could affect AI model accuracy. Organizations can reduce the time and resources needed for data preparation by ensuring that data quality is addressed from the start, directly benefiting AI initiatives.
Kore Integrate’s ETL process helps prepare consistent, high-quality data that meets the rigorous input standards that AI models require. It does this through the following methods:
One of the key components of Kore Integrate’s ETL functionality is Translation Tables. Translation Tables provide a rule-based framework for data transformation, helping organizations achieve data enrichment and standardization. This feature enables data harmonization and correction for missing values. In AI applications, enriched and standardized data leads to improved model training and more reliable predictions.
Kore Integrate’s KSEM (Kore SQL Error Management) is instrumental in identifying and addressing data inconsistencies, which is crucial when building AI models that depend on precise data inputs. This feature continuously identifies, monitors, and logs data errors, supporting data quality assessment and ensuring that only high-integrity data feeds into AI tools. Accurate error tracking and data quality reporting allow organizations to correct issues before they impact AI model performance.
Organizations have significantly improved operational efficiency, eliminated manual tasks, saved hundreds of hours per month, and reduced costs by millions of dollars annually by leveraging Kore Integrate. Review our client stories below to discover how Kore can help transform your organization.
“We had some reports that used to take four hours to refresh and now they take four minutes. That’s the difference between ODBC and SQL.”
“Within the first 4 months, the company has seen savings of $45,000-$50,000 due to increased efficiencies associated with the integration provided by Kore.”
“Our team of 4-5 engineers would be doubled in size if it weren’t for Kore Integrate.”
“With a payback in less than a year, Kore Integrate has been one of the best technology purchases the company has made.”
