Augmented analytics uses AI to automate analytics workflows in platforms, contextualizing user interfaces with automated insights, generative storytelling explanations and collaborative exploration. Driven by machine learning (ML) and generative AI, augmented analytics enables natural language queries and personalized analytics catalogs. It democratizes advanced analytics with augmented data ingestion, data preparation, analytics content and DSML model development. It also curbs human biases and accelerates insights for diverse users.
The data integration tools market comprises stand-alone software products that allow organizations to combine data from multiple sources, including performing tasks related to data access, transformation, enrichment and delivery. Data integration tools enable use cases such as data engineering, operational data integration, delivering modern data architectures, and enabling less-technical data integration. Data integration tools are procured by data and analytics (D&A) leaders and their teams for use by data engineers or less-technical users, such as business analysts or data scientists. These products are consumed as SaaS or deployed on-premises, in public or private cloud, or in hybrid configurations.
Decision intelligence platforms (DIPs) are software used to create solutions that support, automate and augment decision making of humans or machines, powered by the composition of data, analytics, knowledge and artificial intelligence (AI) techniques. DIPs must have collaborative capabilities for decision modeling, execution and monitoring. DIPs are used to design decision-centric solutions, explicitly model decisions, orchestrate decision execution flows, and evaluate and govern decisions and audit their outcomes. Optional features include logic-based techniques, machine learning, business intelligence, natural language processing, optimization, graph technology, AI agents, simulation, real-time event stream processing and multi-structured data preparation.