---
id: ch4-data-to-decisions
type: spine
title: "From data to decisions: one question and its evidence trail"
readingTime: 3
standalone: true
core: true
teaser: "Follow a potato-suitability question through JackDaw, and see what the answer must carry before a planner can use it."
voice: universal
parent: null
diagram: diagram-jackdaw-query-flow
recallQ: "What must a GeoAI answer carry before a planner can use it in a Regional Action Plan?"
recallA: "Visible references to the datasets behind it, with their source, update status, quality status and known limitations, so the reader can tell official statistics from pilot data, external web information, expert interpretation or experimental analysis (D4.2 §4.4)."
highlights:
  - "A question travels: area → routing → tools → approval → answer with tables"
  - "D4.2 distinguishes four provenance levels: source, ingestion, processing, usage"
  - "Six quality statuses, from validated to superseded, should steer which data the agent prefers"
  - "Better exposure of provenance in answers is a stated next step, not finished work"
status: accepted
concept: data-to-decisions
conceptTitle: "From data to decisions, with provenance"
parents: jackdaw
lang: en
lens: generic
depth: standard
formalism: none
visuality: balanced
lengthBand: standard
genre: worked-example
state: core
carriers: prose|table|diagram
---

The weak point in GeoAI is rarely the generated text. It is whether a reader can tell **where each number came from**. The test case below is D7.2's: a user asks whether a particular plot is suitable for potatoes.

## One question, step by step

1. **Area.** The user draws a rectangle around the plot; JackDaw attaches it to the message as WKT.
2. **Routing.** One model call classifies the request and picks relevant tools, for example land-cover statistics (OLU_EU), elevation from a digital elevation model, and agroclimatic indicators such as frost occurrence and growing degree units from ERA5-Land.
3. **Approval.** If a step is flagged as significant, an approval card shows its parameters and waits for the user to confirm.
4. **Tools run.** Each tool computes its result inside its own service, for the drawn area only.
5. **Answer.** The model explains the combined result in text; the underlying figures appear as tables and charts below it.

## What makes the answer usable

D4.2 is explicit that a Regional Action Plan cannot rest on fluent text alone. Users need to know whether an output rests on **official statistics, partner-provided pilot data, external web information, expert interpretation or experimental analysis**. For the platform, D4.2 defines four levels of **provenance**:

| Level | Records |
|---|---|
| Source | Original creator, provider, ownership |
| Ingestion | When and how the resource entered the platform |
| Processing | Changes after upload: reprojection, cleaning, enrichment |
| Usage | How services and pilot applications used it, where appropriate |

Every dataset should also carry a **quality status**: validated, partially validated, experimental, external source, historical or superseded. D4.2 asks the system to prefer resources that are relevant, current, sufficiently described and valid for the requested territory and period; an experimental or outdated dataset should change how an answer is presented. Metadata should distinguish "unknown", "not applicable" and "not provided". **Access is not reuse**: a user may open a dataset whose licence still forbids redistribution, and derived maps inherit the conditions of their inputs.

## Today and next

The Raven ingestion workflow already captures the basic provenance package (title, description, tags, temporal coverage, column descriptions, attached methodology). D4.2 also names the gap: keeping evidence trails transparent across several tool calls is hard, and **better exposure of source provenance** is a next step. GeoRAG and knowledge-graph work is expected to add source-to-answer traces toward the final platform. Until then, treat a JackDaw answer as **screening evidence**: keep its tables, name its datasets, and validate with people who know the plot before it enters a [Regional Action Plan](#c/regional-action-plan).

**Sources:**
- D4.2 PoliRuralPlus platform Beta, §4.4 Provenance, data quality and reuse conditions; §4.5 Data Cubes, GeoRAG and RAG-Knowledge Graph integration; §3.3 Jackdaw GeoAI chat agent — [PDF](https://www.poliruralplus.eu/docs/421/D4.2_PoliRuralPlus_Platform_Beta.pdf)
- D7.2 Practice Abstracts – batch 1, §2.3 Video: GeoAI meets LLMs — [PDF](https://www.poliruralplus.eu/docs/356/D7.2__Practice__Abstracts_-_batch_1_LxksN3q.pdf)