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Documentation Index

Fetch the complete documentation index at: https://docs.datagenie.ai/llms.txt

Use this file to discover all available pages before exploring further.

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

1

Dataset

Understand the analysis boundary and what DataGenie monitors.
2

KPI and Dimension

Learn the difference between what you measure and how you break it down.
3

Time and Granularity

Understand how aggregation level changes the shape of a trend and comparisons.
4

Top Stories and Contributors

Learn how DataGenie packages change and highlights the main drivers.
5

Filters and Segments

Learn how scoping affects results and how to compare like-for-like.
6

Output types

Understand what a scorecard, table, and chart represent so outputs match expectations.
7

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.

Where to go next

Top Stories

Start with the most important changes, prioritized by impact.

Wisdom

Ask questions in plain English and explore answers quickly.

Explorer

Break down change into key drivers and contributors.

Datasets

Configure KPIs, dimensions, and anomaly detection for your data.

Alerts

Stay updated on important shifts without constant manual checks.