Metric Relationship Tree
You Have Data. You Don't Have a Map of What Causes What.
When revenue drops, everyone guesses. Fig builds the causal map of your business — connecting every metric to its upstream drivers so that when a number moves, you trace the full chain and see exactly what caused it. No more guessing.
Your Business, Mapped End-to-End
Fig connects three types of metrics — the actions you take, the performance they drive, and the outcomes that result. Every layer is linked so you can trace cause to effect in seconds.
What Fig Tracks
Metrics
Quantitative measures your business tracks — revenue, churn rate, conversion rate.
KPIs
High-level performance indicators tied to business objectives — targets with thresholds.
Dimensions
The axes you slice metrics by — region, product line, customer segment, time period.
Tables
The physical tables in your data warehouse that hold the raw data behind metrics.
Columns
Individual fields within tables — the atomic level of your data that metrics are computed from.
Subject Areas
Logical groupings that organize metrics by business domain — Sales, Marketing, Operations.
Data Sources
The upstream systems that feed your warehouse — Snowflake, BigQuery, PostgreSQL, and more.
How Fig Connects Everything
Tracks how metrics are computed from columns and tables — full data lineage.
Maps which dimensions each metric can be sliced by — so Fig knows what breakdowns are valid.
Records how tables connect through join keys — enabling multi-table analysis automatically.
Links subject areas to their metrics — organizing the graph into navigable business domains.
Why It Matters
A knowledge graph turns isolated metrics into a connected system — so every analysis has full context.
Causal Chain Tracing
When revenue drops, Fig doesn't just say 'revenue is down.' It traces the full chain: Revenue comes from Sales Amount, which depends on Customer Count, which is driven by Repeat Purchase Rate — and surfaces the root cause at the end of the chain.
Concentration Risk Visibility
If 3 customers account for 60% of your revenue, the knowledge graph makes that dependency explicit. Fig can surface concentration risks during analysis, showing you which metrics are fragile and why.
Full Dependency Mapping
See which metrics actually matter versus which are downstream effects. When Customer Acquisition Cost rises, the graph shows every metric it impacts — so your team focuses on the cause, not the symptoms.
Fig Builds the Map for You
You don't have to figure out what causes what — Fig's AI reads your data, discovers relationships automatically, and proposes the map. You review and approve. You stay in control of what goes live.
AI-Assisted Graph Construction
Human-in-the-loop workflow ensures accuracy
Scan
Fig's AI agent connects to your data warehouse and reads your data sources automatically, including tables, columns, and existing documentation.
Discover
The agent identifies metrics, dimensions, join paths, and causal relationships — proposing nodes and edges for the graph.
Propose
Each discovered element is presented for review with the calculation logic, data types, and suggested relationships clearly shown.
Refine
Your team reviews, accepts, edits, or rejects proposals. The graph grows iteratively as you validate each piece.
You stay in control. Every node, relationship, and metric definition the agent proposes must be reviewed before it becomes part of your knowledge graph. The graph grows incrementally as your team validates each piece — ensuring accuracy from day one.
Pre-Built for Your Industry
You don't start from a blank page. Fig comes with starter maps for your industry — the metrics your peers track, the causal relationships that matter most, and the dimensions your business already uses. Customize from there.
Healthcare
Retail
Marketing
Supply Chain
Manufacturing
Finance
Frequently Asked Questions
What is a Metric Relationship Tree?+
A Metric Relationship Tree is a causal map of how your business metrics connect — which actions drive which performance indicators, and which outcomes they lead to. Fig builds it automatically from your data and lets your team enrich it with business context.
How is a Metric Relationship Tree different from a semantic layer?+
A semantic layer describes what your metrics mean. A Metric Relationship Tree maps what causes what — so when revenue drops, you can trace it back through the causal chain to find the actual driver. Semantic layers describe data. Fig's relationship tree explains it.
Does Fig replace my existing BI tool?+
No. Fig adds the causal decision layer that BI tools were never designed to provide. You keep your dashboards for reporting — Fig answers the 'why' and 'what to do next' questions your BI tool can't.
How long does it take to build the Metric Relationship Tree?+
Fig builds an initial map automatically in minutes by reading your connected data sources. Your team then reviews and enriches it with business context — most teams have a working map within a day.
Explore the Platform
Metric Definitions
Versioned, approved metrics for every agent and team
Learn moreBusiness Policies & Rules
Your business logic enforced on every query
Learn moreBusiness Entities
Accounts, customers, products resolved across sources
Learn moreFlows
Signal → Decide → Act automated end-to-end
Learn moreMonitoring
24/7 anomaly detection with automatic root cause
Learn moreDecision Algorithms
12 built-in algorithms for governed analysis
Learn moreRoot Cause Analysis
Automated causal chain tracing
Learn moreIn-Memory Cache
Reduce warehouse costs, sub-second answers
Learn moreStop Guessing What Caused the Drop
Connect your data warehouse and Fig builds the causal map of your business — so every analysis traces back to a root cause, not a theory.