The tools provide advanced data intelligence, data quality, and data modeling capabilities aimed at helping customers ensure the AI readiness of their data, the company said.
Quest Software has released version 14 of its erwin Data Modeler and erwin Data Intelligence tools, offering what the company describes as advanced data intelligence, data quality, and data modeling capabilities. The tools are intended to help customers ensure the AI readiness of their data, the company said.
Introduced August 27, erwin Data Modeler 14 and erwin Data Intelligence 14 provide a comprehensive approach to managing data across complex enterprise landscapes, the company said. With the release of erwin Data Intelligence 14, Quest said it was providing an enhanced data quality and observability framework. The tool continuously monitors data health across platforms, providing anomaly detection for critical data sources, ensuring that data supporting AI models remains accurate and reliable.
Quest said erwin Data Intelligence 14 features self-learning capabilities that “automatically evolve” the framework to meet the needs of the organization and fine-tuning data quality measures based on real-world usage. Also featured are data quality discovery features that provide a search and filter experience similar to online shopping sites, allowing users to explore data quality metrics through an analytics dashboard. And with the introduction of erwin Data Intelligence Cloud, users have the flexibility to choose a deployment model best for their infrastructure, whether on-premises, privately hosted, or using Quest’s cloud-hosted solution on the Microsoft Azure cloud.
With erwin Data Modeler 14, new capabilities streamline modeling data processes, such as improved integration with modern data platforms, enhanced UI customization, and advanced reverse engineering capabilities for NoSQL platforms, Quest said. These improvements make it easier to design and manage data architectures, according to Quest. Quest’s erwin Data Modeler 14 also offers enhanced support for NoSQL databases, improved denormalization processes, and integration with platforms such as PostgreSQL and Google BigQuery.
