Gartner defines agentic analytics as software used for the process of data analysis that applies AI agents across the data-to-insight workflow, orchestrating tasks semi-autonomously or autonomously toward stated goals that support, augment or automate insights. Agentic analytics’ must-have capabilities are data source connectivity, data preparation, agent workflow orchestration, automated insights and natural language query. Optional capabilities include data storytelling, a coding assistant, function calling, agent memory, embedded analytics and platform administration. Agentic analytics is the evolution of augmented analytics through the application of AI agents to data analysis. Must-have capabilities are: data source connectivity data preparation agent workflow orchestration automated insights natural language query Optional capabilities include: data storytelling a coding assistant function calling agent memory embedded analytics platform administration
Analytics and business intelligence (ABI) platforms prepare, model, analyze and visualize data to support decision making. They deliver insights through AI-powered conversational experiences, interactive dashboards and classic reporting. They support collaboration between business and technical users for defining the dimensions, measures and business rules used to create and maintain semantic models. The platforms provide functionality for agentic analytics, where AI agents coordinate tasks across the data-to-insight workflow to automate insight delivery under governance and audit controls. Analytics and business intelligence platforms integrate data from multiple sources, such as databases, spreadsheets, cloud services and external data feeds, to provide a unified view of data, breaking down silos and transforming raw data into meaningful insights. They also allow users to clean, transform and prepare data for analysis, in addition to creating data models that define relationships between different data entities.
Data preparation is an iterative and agile process for finding, combining, cleaning, transforming and sharing curated datasets for various data and analytics use cases including analytics/business intelligence (BI), data science/machine learning (ML) and self-service data integration. Data preparation tools promise faster time to delivery of integrated and curated data by allowing business users including analysts, citizen integrators, data engineers and citizen data scientists to integrate internal and external datasets for their use cases. Furthermore, they allow users to identify anomalies and patterns and improve and review the data quality of their findings in a repeatable fashion. Some tools embed ML algorithms that augment and, in some cases, completely automate certain repeatable and mundane data preparation tasks. Reduced time to delivery of data and insight is at the heart of this market.