Core Concepts
Learn the key terms DataGenie uses to describe your data, detect changes, and explain what drove them.
Core Concepts covers the shared vocabulary DataGenie uses to describe your data, the changes it detects, and how those changes are explained. Once these terms are clear, everything else in the product becomes easier to interpret and trust.
What you learn here
- What DataGenie considers a dataset, KPI, and dimension
- How time range and granularity shape what you see
- How stories summarize change and how contributors help explain it
- How filters and segments focus analysis
- What output formats mean (scorecard, table, chart)
- How knowledge inputs support more consistent answers (optional)
How the concepts connect
Dataset
Provides the analysis boundary.
KPI and Dimension
KPIs are what you measure; dimensions are how you slice.
Time and Granularity
Defines the window and resolution of analysis.
Top Stories
Summarizes meaningful KPI movement.
Contributors
Ranks what drove the change.
Filters and Segments
Scopes analysis to a specific subset.
Overview
Dataset
Understand the analysis boundary and what DataGenie monitors.
KPI and Dimension
Learn the difference between what you measure and how you break it down.
Time and Granularity
Understand how aggregation level changes the shape of a trend and comparisons.
Top Stories and Contributors
Learn how DataGenie packages change and highlights the main drivers.
Filters and Segments
Learn how scoping affects results and how to compare like-for-like.
Output types
Understand what a scorecard, table, and chart represent so outputs match expectations.
Knowledge inputs (optional)
Learn what Domain Knowledge, Cognitive Skills, and Business Events mean and why they matter for consistent answers.
Concept list
Dataset
A defined collection of metrics and dimensions that DataGenie monitors and analyzes.
KPI (Metric)
A measure you track over time, such as revenue, conversion rate, or return rate.
Dimension
A way to slice a KPI into groups, such as country, channel, device, or cohort.
Time range
The time window you are analyzing, such as last week, last month, or last quarter.
Granularity
The level of aggregation for time-based analysis, such as daily, weekly, or monthly.
Top Story
A summarized, high-impact change in your data that is prioritized for attention.
Contributors
The 2–3 dimension values that explain most of a Root KPI's change (e.g., "PayPal, Mobile, iOS" for a Payment Completion drop).
Filters and Segments
Constraints applied to focus analysis and the scoped view they create.
Output types
The format used to present an answer, typically a scorecard, table, or chart.
Knowledge inputs (optional)
Domain Knowledge, Wisdom Skills, and Business Events — the guardrails that encode your business rules so Wisdom answers deterministically, not by guessing.