As processes and toolsets change, simply storing commercial data isn’t enough. Teams need it cleaned, categorised, and turned into insights that can trigger action. Planhat does this transformation inside the platform. Formula fields calculate across fields and records, and Calculated Metrics create live time-series trails. All of this this can be interpreted and categorised with the help of natively available LLMs. The adapted data can then be funnelled to your automations, workflows, and other processes.
01
Dynamic trend analysis
Use calculated metrics to turn imported events and daily changes into rolling ratios and trends - product adoption curves, ticket resolution velocity, revenue development, or any custom trail you need.
02
Rollups across records
Create formula fields to pull and aggregate data from anywhere in the model (e.g. number of open tickets, total value of opportunities, latest support escalation) and surface it on the company or wherever you need it.
03
Normalisation and scoring
Clean, weight, and combine data points (using native LLM categorisation or your own logic) so you can compare apples-to-apples and build scores like churn risk, health, or ICP fit that drive action.
What does Data Transformation actually do with raw data before it reaches Planhat workflows?
Raw data from product logs, CRMs, and billing systems often speaks different languages. Planhat's Data Transformation layer standardises fragmented inputs into a unified, time-aware model — turning a raw login event into an adoption signal, or a billing update into a retention milestone — so that every piece of data carries the commercial context needed to drive action.
How does Data Transformation make automation more reliable at scale?
Automation is only as reliable as the signals that trigger it. Planhat's transformation engine converts raw noise into high-fidelity operational metrics — such as onboarding activation signals or risk indicators — so that commercial Workflows trigger on structured, meaningful data rather than unprocessed inputs, regardless of the volume of accounts being managed.
How does transformation connect a data pattern to an immediate operational response?
In traditional stacks, data transformation happens in a warehouse for the sake of a dashboard. In Planhat, transformation happens directly within the platform. When a transformation rule detects a specific pattern — like a drop in a calculated usage ratio — it immediately fires a risk mitigation Workflow rather than simply updating a chart.
How does the transformation layer manage overwhelming data volume in complex environments?
Modern enterprises produce an overwhelming volume of raw events. Planhat's transformation layer intelligently aggregates and filters millions of time-series events, distilling them into the few critical signals that actually impact NRR, keeping commercial teams and AI workflows focused on high-value outcomes.
How does Data Transformation contribute to trustworthy AI outputs?
AI models require structured, semantically consistent data to provide accurate predictions. Planhat's transformation framework enforces strict data semantics, ensuring that every account, user, and activity is correctly related and time-stamped. This high-integrity foundation makes capabilities like predictive churn scoring and next best action recommendations reliably actionable.