Teams and AI tools get side-tracked (or hallucinate) when field meanings or segment logic isn’t obvious - it doesn’t matter how clean and structured the data is, if no-one has context on what it represents. Planhat is built for clarity and context-sharing, letting you add plain-language descriptions to fields and save filters with readable names and conditions. This context travels everywhere: dashboards, automations, pages, alerts, and AI prompts alike.
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Add rich descriptions to any field
Write detailed explanations and examples directly on fields (e.g. what belongs in “Churn Risk Comment” and which keywords matter) so people and agents understand intent at a glance.
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Save filters that read like natural language
Name and reuse filters with clear titles and visible logic – “High-Value EU Renewals <90 Days” (ARR > $100k AND Region = EU AND Renewal Date ≤ 90d) – across reports, workflows, and AI queries.
What is the role of Data Semantics in how Planhat interprets customer signals?
Data Semantics move beyond raw aggregation by providing contextual awareness to every customer signal. Instead of just storing a login event, the semantic layer defines it as User Activation or Daily Adoption Velocity. This shared meaning ensures that every department — from Finance to Customer Success — interprets customer behaviour through the same strategic lens.
How do Data Semantics make automation smarter than simple rule-based triggers?
Automation is only as smart as the data it understands. Planhat's Data Semantics create self-explanatory data structures — when the automation engine sees a signal, it doesn't just see a number, it understands the commercial intent. This allows the platform to trigger an expansion Workflow because expansion readiness was genuinely detected, not just because a field happened to change.
How do Data Semantics help account managers act faster on customer data?
Most teams suffer from data translation fatigue — they see a metric and have to ask what it actually means for the customer. Planhat's semantic model eliminates this gap by connecting data directly to operational outcomes. By converting raw telemetry into business signifiers — for example, 'Health Declining due to Low Stakeholder Engagement' — the platform moves instantly from 'What happened?' to 'What should we do?'.
How does a semantic layer handle inconsistent data from multiple source systems?
In multi-source environments — CRM, support, product — the same data often means different things to different teams. Planhat's Data Semantics layer normalises disparate inputs into a consistent commercial language, ensuring that whether a signal comes from a Jira ticket or a Snowflake table, it is weighted and interpreted according to its impact on NRR.
How do Data Semantics improve the accuracy of AI-driven predictions?
Large language models and AI agents don't just need data — they need semantics to reason. Planhat's semantic framework provides the high-fidelity context required for operationalised AI. Because the data is self-explanatory, AI can perform complex reasoning — predicting churn or suggesting a next best action — with an understanding of why it is doing so, rather than pattern-matching on raw numbers.