> For the complete documentation index, see [llms.txt](https://experience-digital-transform.gitbook.io/experience-digital-transformation/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://experience-digital-transform.gitbook.io/experience-digital-transformation/analytics/technical-specifications/2.-description.md).

# 2. Description

What the Analytics Building Block is, where it sits in the GovStack reference architecture, its key distinguishing principles, and its current scope.

> 🧭 **In one line.** A governed data lakehouse that turns registry and integration data into reconciled, decision-ready intelligence — built to plug into any GovStack deployment.

Governments generate and rely on vast quantities of data across all sectors of public administration. Turning this data into actionable intelligence is a prerequisite for evidence-based policy, efficient resource allocation, and proactive service delivery. The Analytics Building Block (Analytics BB) provides the reusable, interoperable analytical infrastructure that enables national and sub-national governments to store, harmonise, analyse, and visualise structured data in a secure, sovereign, and scalable manner.

The Analytics BB is positioned within the GovStack reference architecture as the authoritative layer for calculated indicators, predictive models, and consolidated analytical reporting. It operates downstream of data ingestion and registration layers, consuming harmonised data from registries and integration adapters, and delivering insights upstream to decision-support dashboards, open-data portals, and automated workflow triggers.

The Building Block is domain-agnostic. While the reference use cases below draw on social-protection and economic examples, the same generic capabilities apply to sector deployments — including climate and environment — where the data comprises greenhouse-gas inventory and indicator time series, sectoral activity data, and geospatial or Earth-observation assets. Section 12.3 shows how the Building Block serves a climate use case without domain-specific redesign.

Key distinguishing principles of this Building Block include:

* A strict Data Lakehouse architecture separating raw (Bronze), harmonised (Silver), and aggregated (Gold) logical layers to ensure data quality and traceability, with data validation, cleaning and reconciliation applied as data is promoted between layers.
* Functional separation from upstream ingestion, Personally Identifiable Information (PII) pseudonymisation, and consent management — these responsibilities belong to adjacent Building Blocks. PII pseudonymisation is performed by the ingestion/registry layer and the Information Mediator; consent records and status are managed by the Consent Building Block.
* Cloud-native, vendor-neutral deployment grounded in open standards and Infrastructure-as-Code practices.
* A dual-engine query model combining federated ad-hoc exploration with high-performance OLAP (Online Analytical Processing) dashboarding.
* Multi-source reconciliation that keeps each source whole and transparently designates the authoritative value, with governed human review where sources diverge.
* An AI-assisted natural-language query module enabling non-technical public officials to retrieve data-driven insights without SQL expertise, together with advisory AI recommendations for data quality, reconciliation and anomaly detection that always require human validation.

{% hint style="info" %}
🔒 **Scope boundary.** PII pseudonymisation is performed by the ingestion/registry layer and the Information Mediator — not by the Analytics BB, and not by the Consent Building Block, which manages consent records and status only.
{% endhint %}

## 2.1 Current Scope

The current version of this specification addresses the following functional areas:

* Data storage, governance, and lifecycle management within the Silver and Gold lakehouse layers.
* Data validation, cleaning, and reconciliation applied as harmonised data is promoted across the Bronze, Silver and Gold layers.
* Batch and event-driven analytical transformation pipelines.
* Multi-source reconciliation of complex datasets into a single authoritative view, including divergent evidence streams (e.g. national statistics versus satellite-derived signals) with transparent "official versus supplementary" designation and governed human review where required.
* Multi-sector statistical analysis and predictive scoring (illustrated by a household vulnerability index use case and a climate NDC-tracking use case).
* Support for climate and environmental data types — greenhouse-gas inventory and indicator time series, sectoral activity data, and geospatial / Earth-observation (raster and vector) assets — when the Building Block is deployed for a climate use case.
* Secure, role-aware query execution and result delivery via API.
* AI-powered natural-language to SQL query synthesis with mandatory security enforcement, plus advisory AI recommendations for data quality and reconciliation.
* Federated identity integration with an external Identity / Access Management Building Block.

The following capabilities are explicitly outside scope for the current version and are addressed by adjacent Building Blocks:

* Physical data extraction and Change Data Capture (CDC) from source systems — handled by Integration / Ingestion Building Blocks.
* PII depersonalisation, masking, and hashing — performed by upstream ingestion and registry layers and the Information Mediator before data enters the analytical environment. The Consent Building Block manages consent records and status; it does not perform anonymisation.
* Transactional data as a primary source of truth — Digital Registries remain authoritative for transactional records.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://experience-digital-transform.gitbook.io/experience-digital-transformation/analytics/technical-specifications/2.-description.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.