Analytics and business intelligence platforms — enabled by IT and augmented by AI — empower users to model, analyze and share data. Analytics and business intelligence (ABI) platforms enable organizations to understand their data. For example, what are the dimensions of their data — such as product, customer, time, and geography? People need to be able to ask questions about their data (e.g., which customers are likely to churn? Which salespeople are not reaching their quotas?). They need to be able to create measures from their data, such as on-time delivery, accidents in the workplace and customer or employee satisfaction. Organizations need to blend modeled and nonmodeled data to create new data pipelines that can be explored to find anomalies and other insights. ABI platforms make all of this possible.
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.
Gartner defines integration platform as a service (iPaaS) as a vendor-managed cloud service that enables end users to implement integrations between a variety of applications, services and data sources, both internal and external to their organization. iPaaS enables end users of the platform to integrate a variety of internal and external applications, services and data sources for at least one of the three main uses of integration technology: Data consistency: The ability to monitor for or be notified by applications, services and data sources about changes, and to propagate those changes to the appropriate applications and data destinations (for example, “synchronize customer data” or “ingest into data lake”). Multistep process: The ability to implement multistep processes between applications, services and data sources (for example, to “onboard employee” or “process insurance claim”). Composite service: The ability to create composite services exposed as APIs or events and composed from existing applications, services and data sources (for example, to create a “credit check” service or to create a “generate fraud score” service). These integration processes, data pipelines, workflows, automations and composite services are most commonly created via intuitive low-code or no-code developer environments, though some vendors provide more-complex developer tooling.
Marketing dashboard tools collect and integrate marketing data from multiple sources, visualize it, and enable visualization and exploration through a web-based interface. The technology helps marketers deliver near-real-time data and reporting to stakeholders across the marketing organization and the enterprise. These tools cater to the business user rather than the technical professional, as they do not require you to manage a data warehouse or to define a semantic layer. The market features SaaS-based offerings from large, diversified providers as well as small pure-play vendors. Some of these sell marketer-focused solutions, and others have tailored their IT-focused offerings to this new buyer segment.