Analytics Datasets

A dataset is a collection of data that contains items (or records). 

For each " theme" in LogAlto, there is a dataset that allows you to create reports to analyze, filter, and aggregate the data.

1. Available datasets
2. Some examples

Available datasets

The following datasets are available:

Diffrences between Indicator Datasets

Notes:

* = full history is available, with a flag to filter to the latest value only.

† In IndicatorDisaggregatedValuesTheme, when a global indicator is disaggregated by project, the project-level value corresponds to the project total.

‡ For project-disaggregated global indicators, final_target can also represent the project total target; only the latest target is exposed.

The datasets differ primarily in terms of data granularity, historical coverage, and availability of target and contribution metrics.

The Indicator Values dataset is the most complete and flexible. It supports all types of requested content—total cumulative, progress, and target values—and provides full historical data. However, it does not include disaggregated breakdowns. This makes it well suited for tracking overall performance over time at an aggregate level.

In contrast, the Indicator Disaggregated Values dataset focuses specifically on granular breakdowns (e.g., by region, gender, or category). While it supports cumulative and progress views for disaggregated data, it does not include total-level metrics or target-related values. Its history is mixed, meaning some disaggregations may have full history while others may not. This dataset is best for detailed analysis rather than high-level reporting.

The Indicator Target vs Actual Values dataset is designed to compare actual performance against targets. It supports total cumulative and progress metrics with full historical data but does not include disaggregated data or contribution metrics. This makes it ideal for evaluating whether targets are being met over time, but only at an aggregate level.

Finally, the Indicator Values Contribution dataset is the most limited in scope. It only provides the latest values (no historical data) and focuses on contribution metrics for totals. While it supports cumulative and progress-related content, it excludes disaggregated and most target progress views. This dataset is mainly useful for understanding the most recent contribution breakdowns rather than trends over time.

In summary:

• Use Indicator Values for comprehensive, historical, high-level analysis
• Use Indicator Disaggregated Values for detailed breakdowns
• Use Indicator TargetActual Values for tracking performance against targets
• Use Indicator Values Contribution for latest contribution insights only

Absolutely—this is where the differences between datasets really become practical. Below are concrete report examples you can build with each dataset, along with what question each report answers.

📊 1. Indicator Values (Totals + Full History)

👉 Best for: High-level performance tracking over time

Example Reports

• 📈 KPI Trend Report
  • “How has total beneficiary reach evolved over time?”

  • Line chart of cumulative or periodic totals

    📊 Monthly Progress Dashboard

  • “Are we increasing output month over month?”

  • Bar chart showing progress per period

    🎯 Target vs Progress (Simple)

  • “Are we on track overall?”

  • Compare total actual vs target (no breakdown)

    📅 Year-over-Year Comparison

  • “Are we performing better than last year?”
  • Compare totals across years

🔍 2. Indicator Disaggregated Values (Breakdowns)

👉 Best for: Deep-dive analysis and equity insights

Example Reports

• 👥 Demographic Breakdown Report
  • “Who are we reaching?”

  • Split by gender, age group, etc.

    🌍 Geographic Distribution Map

  • “Where is impact happening?”

  • Region/country-level breakdown

    ⚖️ Equity & Inclusion Analysis

  • “Are we reaching underserved groups?”

  • Compare proportions across categories

    📊 Segment Performance Trends

  • “Which groups are improving over time?”
  • Trend lines per disaggregation

🎯 3. Indicator Target Actual Values (Performance vs Target)

👉 Best for: Accountability and performance evaluation

Example Reports

• 🚦 Target Achievement Dashboard
  • “Are we meeting our targets?”

  • % achieved (actual vs target)

    📉 Gap Analysis Report

  • “How far are we from targets?”

  • Target vs actual difference

    📆 Forecasting Report

  • “Will we hit the target by year-end?”

  • Use historical trend to project

    🏆 Performance Scorecard

  • “Which indicators are on/off track?”
  • Red/amber/green status

🧮 4. Indicator Values Contribution (Latest Snapshot Only)

👉 Best for: Understanding composition of current totals

Example Reports

• 🥧 Contribution Pie Chart
  • “What contributes most right now?”

  • % share of total by component

    🏅 Top Contributors Report

  • “Which areas drive results?”

  • Rank contributors (e.g., regions, partners)

    📊 Composition Snapshot Dashboard

  • “What does the current total look like?”

  • Breakdown of latest values only

    ⚡ Quick Executive Snapshot

  • “What’s the current situation?”
  • No trends—just latest state

🧠 Putting It Together (Real-World Combo)

In practice, strong reporting often combines datasets:

• Executive Dashboard
  • Indicator Values → trends
  • TargetActual → performance vs target

  • Contribution → current composition

    Program Review

  • Indicator Values → overall progress
  • Disaggregated → who/where impact
  • TargetActual → are goals met

🧭 Simple Rule of Thumb

• Want trends? → Indicator Values
• Want breakdowns? → Disaggregated Values
• Want targets tracking? → TargetActual Values
• Want latest composition? → Contribution Values