Workforce management system Rippling has launched Data Cloud, a new suite of products that aggregates data from across a company into Rippling, connects it to worker identity, and makes it available for analysis, visualization, and action.
It also preserves and enriches data context to enable precise and accurate answers to important and nuanced business questions, the company says.
Data Cloud is a complete data stack, including data connectors, transformations, visualizations, AI-powered analytics, and even inbound zero-copy. It understands how all of that data relates to employees, managers, departments, locations, cost centers, permissions, and historical changes in an ever-changing business. That makes it possible to ask questions that traditional business intelligence systems struggle to answer correctly, Rippling says.
“Rippling started as an HCM, which makes it uniquely capable of understanding identity data: who works at the company, whom they report to, what they can access, what team they belong to, where they are located, what they do, and how all of that changes over time,” Suraj Savalia, director of product management at Rippling, said in a blog post on June 25.
“But this data is useful far beyond HR. A GitHub pull request has an author. A Salesforce opportunity has an owner. A helpdesk ticket has an assignee. A point-of-sale transaction has a cashier. A device has an employee. A payroll run has workers, departments, locations, and managers attached to it. Once those records are connected to worker identity, business data becomes easier to analyze, easier to govern, and easier to act on,” he continued.
“Rippling Data Cloud uses that identity layer across the entire stack: data ingestion, cataloging, transformations, history, dashboards, AI, and custom applications.”
Recommended Articles
Small Business June 23, 2026
Rippling Launches Business Banking
Rippling Data Cloud includes every component needed to run a complete AI-powered BI stack—from managed connectors up to visualization and collaboration.
It includes:
- Dashboards: Rippling AI generates charts and dashboards with trusted, reusable components and inspectable structured query language from natural-language prompts. Users can also build classic dashboards with charts, filters, pivots, calculated fields, and saved views. BI is different inside Rippling because dashboards inherit the context of the platform, Savalia wrote.
- Data connectors: Data connectors bring third-party business data into Rippling, preserving and enriching the context that makes it useful. Traditional ETL (extract, transform, load) tools move data from one system to another but leave teams to rebuild joins, permissions, metadata, object relationships, and worker identity mappings by hand. Rippling data connectors do that work automatically: they import data from systems like CRMs, support tools, finance systems, and other warehouses, then map that data into Rippling Custom Objects.
- Transformations: Transformations turns raw business data into governed, reusable datasets. Instead of letting every dashboard, SQL query, spreadsheet, or AI prompt define metrics slightly differently, transformations gives companies a central place to encode the logic behind the metrics business’s use, like revenue, margin, store performance, customer risk, or whatever else matters to a given operation.
- Data catalog: Data catalog gives Rippling Data Cloud and Rippling AI a map of a business’s data. It’s the central inventory for every data object in Rippling, including native Rippling data, data from data connectors, transformations, and external warehouse data.
- History: Object history lets Rippling Data Cloud answer historical business questions without projecting today’s organizational chart backward. Most business analysis is really asking what was true at a specific point in time: whom someone reported to, what team they were on, when their role changed, what workflow ran, who approved a change, or which org structure applied when a metric moved, Savalia says. Object history makes that context queryable across Rippling, so reports, dashboards, transformations, workflows, custom apps, and Rippling AI can reason from the actual historical state of the business.
- Custom apps: Custom apps lets teams build company-specific software on top of the data inside Rippling. Dashboards show you what’s happening, but most business problems still require a process: an approval, an exception review, a payroll adjustment, a remediation workflow, or a record that someone needs to update. Custom apps uses the same data, permissions, workflows, and object model that power the rest of Rippling.
- Snowflake zero-copy: Zero-copy for Snowflake lets companies use warehouse data inside Rippling Data Cloud without building custom pipelines. Data from Snowflake can appear in Rippling as external objects, where it can be joined to worker identity, governed by Rippling permissions, surfaced in the data catalog, and used by Rippling AI, dashboards, and transformations.
“Rippling Data Cloud, together with Rippling AI, unlocks a new frontier of analytical capabilities,” Savalia wrote. “Answer questions about the who behind every what. Better understand your company’s performance dynamics across sales, engineering, and operations using only a conversational interface. Rippling Data Cloud will instantly become a mainstay of every data-driven leader.”
Sign in to get access to this free resource, and all of our whitepapers and reports.
Download this content today!
Register Now Already registered? Click here to Log In
