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.
Gartner defines decision intelligence platforms (DIPs) as software to create decision-centric solutions that support, augment and automate decision making of humans or machines, powered by the composition of data, analytics, knowledge and AI. DIPs enable enterprises to collaboratively design and explicitly model decisions, orchestrate decision flow during execution at scale, and enable monitoring and governance of decision quality, while learning from actions and outcomes. Features can include a combination of rule- and logic-based techniques, machine learning, real-time event stream processing, business intelligence, multimodal data and analytics preparation, natural language, graph technology, optimization, simulation or AI agents for decision intelligence. DIPs provide a solution to enhance how organizations make decisions, whether by humans or machines, individually or collectively. They address the growing challenge of making timely and accurate decisions in volatile, uncertain, complex and ambiguous ecosystems, for more demanding customers in disruptive, competitive and regulated markets. DIPs help by creating executable decision models that improve decision service composition and all-source intelligence to achieve better situational awareness, better recommendations or autonomous actions, tailored to specific decisions and outcomes. They can reduce the risk of poor decisions, allow organizations to anticipate change and respond more swiftly to opportunities at scale.