Rippling announced the launch of Rippling Data Cloud, a new suite of products designed to bring AI-powered business intelligence to workforce and operational data.
Rippling Data Cloud aggregates data from across a company into Rippling, connects it to worker identity, and makes it available for analysis, visualization, and action. The platform is designed to preserve and enrich business context so companies can answer nuanced questions about employees, teams, managers, departments, locations, roles, permissions, and historical organizational changes.
Rippling said traditional BI infrastructure often struggles with questions that depend on understanding who a piece of business data is connected to and what the organization looked like at the time an event occurred. For example, sales performance, support resolution times, engineering velocity, overtime trends, customer complaints, and other business metrics often require employee and organizational context to be interpreted correctly.
The company said this challenge has become more important as AI becomes more central to analytics. Without governed business context, AI systems may struggle to answer questions accurately across operational systems.
Rippling Data Cloud includes data connectors, transformations, visualizations, AI-powered analytics, inbound Zero-Copy capabilities, history, dashboards, a data catalog and lineage tools, and custom applications. The platform is designed to understand how data relates to employees, managers, departments, locations, cost centers, permissions, and historical changes across a business.
The platform’s Dashboards product enables Rippling AI to generate charts and dashboards from natural-language prompts using trusted reusable components and inspectable SQL. Users can also build traditional dashboards with charts, filters, pivots, calculated fields, and saved views. Dashboards inherit Rippling’s permissions and organizational context, allowing managers to view the same dashboard automatically scoped to their own teams.
Rippling Data Connectors bring third-party business data into Rippling while preserving context. The product imports data from systems such as CRMs, support tools, finance systems, and warehouses, then maps that data into Rippling Custom Objects so records such as GitHub pull requests, support tickets, sales opportunities, or point-of-sale transactions can be connected to the right employee, manager, team, permissions model, and business context.
The Transformations product turns raw business data into governed reusable datasets. Analysts can write SQL directly, while business users can use Rippling AI to help define and refine logic for metrics such as revenue, margin, store performance, customer risk, and other operational measures.
Data Catalog and Lineage provides a central inventory of data objects in Rippling, including native Rippling data, connected data, transformations, and external warehouse data. The catalog helps users find, understand, trust, and govern data while giving Rippling AI the context needed to choose the right objects, fields, joins, filters, and business definitions when answering questions.
Object History enables Rippling Data Cloud to answer historical business questions without applying today’s organizational structure to past events. This allows reports, dashboards, transformations, workflows, custom apps, and Rippling AI to reason from the actual historical state of the business, including who someone reported to, what team they were on, when their role changed, and what org structure applied at the time a metric moved.
Custom Apps allow teams to build company-specific software on top of data inside Rippling. These apps can use the same data, permissions, workflows, and object model that power the rest of Rippling, enabling business processes such as approvals, exception reviews, remediation workflows, payroll adjustments, and structured record updates.
Rippling also introduced Zero Copy for Snowflake, which allows companies to use warehouse data inside Rippling Data Cloud without building custom pipelines. Snowflake data can appear in Rippling as external objects that can be joined to worker identity, governed by Rippling permissions, surfaced in the Data Catalog, and used by Rippling AI, Dashboards, and Transformations.
Rippling said Data Cloud, combined with Rippling AI, is intended to help business leaders better understand company performance across sales, engineering, operations, and other teams through a conversational interface.
