Statistics production

Use case
Statistics production

Statistics production benefits greatly from the introduction of metadata-driven processes and tools. This benefit increases even further if validation, transformation, and aggregation processes are metadata driven.

Repeatable and systematic metadata-driven statistics production

For statisticians, data collectors, data reporters and data publishers.

While Statistics Dissemination is essential to ensuring relevance of statistical outputs, it is statistics production where the complex and highly specialised tasks of validating and aggregating data takes place along with the production and analysis of high quality statistics.

Statistics production benefits greatly from the introduction of metadata-driven processes and tools. This benefit increases even further if validation, transformation, and aggregation processes are metadata driven.

Here you will find information about flexible data processing tools and integrations for data validation, data transformation (mapping), converting data between transmission formats, and more, in support of the statistics production process.

Article

Article

Creating balance equality validation rules using hierarical codelists in FMR

Author: BIS

Learn how Hierarchical Codelists can be used to define balance equality validation rules.

Creating balance equality validation rules using hierarical codelists in FMR

Course

Course

Explore how datasets can be converted from one SDMX format to another.

Sponsor: BIS

Explore how datasets can be converted from one SDMX format to another.

Course

Recoding: Transforming Data into Different Structures

Sponsor: BIS

Recoding: Transforming Data into Different Structures

Event

Event

IFC and Bank of Italy Workshop on "Data Science in Central Banking" - Part 1

Sponsor: IFC, BoI

Part 1: Data Science in Central Banking: Machine learning applications, 19-22 October 2021, virtual event hosted by the Bank of Italy

IFC and Bank of Italy Workshop on "Data Science in Central Banking" - Part 1

Event

IFC Workshop on "Data Science in Central Banking" - Part 2

Sponsor: IFC, BIS

Part 2: Data Science in Central Banking: Applications and tools , 14-17 Feb 2022, virtual event hosted by the Bank for International Settlements.

IFC Workshop on "Data Science in Central Banking" - Part 2

Event

SDMX Tools and implementations - Knowledge sharing workshop

Sponsor: Istat, Eurostat

In this 3 day workshop, participants will be given an introduction of the available SDMX implementations followed by demonstrations, with real life examples, on how the different tools are used within the organizations.

SDMX Tools and implementations - Knowledge sharing workshop

Event

3rd IFC Workshop on Data Science in Central Banking

Sponsor: IFC, BoI

In this 3 day workshop, participants will review the ongoing adoption of data analytics and business intelligence techniques and developments in the big data ecosystem. The main objective is to showcase projects and share experience that can help to develop in-house knowledge and reduce the reliance on external services providers.

3rd IFC Workshop on Data Science in Central Banking

Event

9th SDMX Global Conference

Sponsor: SDMX Sponsors, IGA

The Global Conference is a bi-annual event for the official statistics community worldwide to share information on recent and upcoming SDMX developments.

9th SDMX Global Conference

Event

SDMX + AI: Unlocking the Potential of AI to Enhance Data Access - workshop

Sponsor: OECD, BIS

The primary objective of this workshop is to assess the main use case(s) identified in the area of enhanced access to data, delve into emerging solutions, and identify priority areas for collaboration and co-investment

SDMX + AI: Unlocking the Potential of AI to Enhance Data Access - workshop

Event

SDMX Tools and implementations - Knowledge sharing workshop

Sponsor: Istat, Eurostat

In this 4 day workshop, participants will be given an introduction of the available SDMX implementations followed by demonstrations, with real life examples, on how the different tools are used within the organizations.

SDMX Tools and implementations - Knowledge sharing workshop

Event

ADB Statistical Data and Metadata eXchange for Enhanced Data Management Webinar

Sponsor: Asian Development Bank (ADB)

Decision makers need to analyze, exchange, and disseminate statistical information effectively in today’s data-driven world. See how Statistical Data and Metadata eXchange (SDMX) standards are helping create robust data links across the globe.

ADB Statistical Data and Metadata eXchange for Enhanced Data Management Webinar

Event

12th SDMX Experts Workshop

The Experts Meeting is a biannual event that brings together SDMX experts from national statistical offices, central banks, international agencies and the private sector, from around the world.

12th SDMX Experts Workshop

Event

4th IFC Workshop on Data Science in Central Banking

Sponsor: IFC, BoI

For this 3 day workshop particular emphasis is placed on the exploration of generative Artificial Intelligence (AI) and its potential applications in central banking

4th IFC Workshop on Data Science in Central Banking

Event

10th SDMX Global Conference

Sponsor: SDMX Sponsors, Bank of Italy

The SDMX Global Conference is a premier biennial event dedicated to producers and users of structured data providing a unique platform to share knowledge, developments, and best practices centered on the SDMX international standard.

10th SDMX Global Conference

Past Webinar

Past Webinar

Fusion Metadata Registry's data validation functions help improve data quality and streamline the process of data exchange.

Sponsor: BIS

Fusion Metadata Registry's data validation functions help improve data quality and streamline the process of data exchange.

Past Webinar

Leveraging SDMX for machine learning in economics with the BIS open-source library gingado

Sponsor: BIS

Leveraging SDMX for machine learning in economics with the BIS open-source library gingado

Past Webinar

Introduction to pysdmx, key use cases and hands-on demo

Sponsor: BIS

Introduction to pysdmx, key use cases and hands-on demo

Software

Software

SDMX Metadata AI Assistant (MAIA): enhancing statistical metadata

The SDMX Metadata AI Assistant (MAIA) is a Python-based tool powered by OpenAI GPT, specifically designed to address the challenges of managing and editing statistical metadata. Leveraging AI, SDMX MAIA simplifies metadata workflows by automating key tasks such as checking syntax, consistency checks and formatting adjustments.

SDMX Metadata AI Assistant (MAIA): enhancing statistical metadata

Software

SDMX lab

The SDMX lab is the ideal starting point for anyone looking to explore and work hands-on with SDMX. Whether you’re just getting started with the standards or diving into practical implementation, the lab gives you a personal, isolated SDMX environment of key tools from the sdmx.io ecosystem – ready to go, with no setup or installation required: Hands-on SDMX. Instantly.

SDMX lab

Software

SDMX Connectors

The SDMX Connectors is a set of plugins that can be installed in the most popular data tools, providing functions for easily and seamlessly downloading data from SDMX providers implementing the SDMX REST API.

SDMX Connectors

Software

Trevas

Trevas is a Java engine for the Validation and Transformation Language (VTL), an SDMX standard that allows the formal definition of algorithms to validate statistical data and calculate derived data. VTL is user oriented and provides a technology-neutral and standard view of statistical processes at the business level.

Trevas

Software

VTL Engine & Editor

VTL Engine & Ediotor (VTL E&E) is an interpreter engine and an editor for the Validation and Transformation Language, a declarative, metadata-driven language developed in the context of the SDMX standard, with the goal of standardizing data validations and transformations.

VTL Engine & Editor

Software

VTL Engine & VTL Suite

The VTL Engine is a Python library implementing a Validation and Transformation Language (VTL). It provides a simple interface to run VTL on data. It has a full implementation of VTL 2.1.

VTL Engine & VTL Suite

Software

gingado, a LinkageX resource

gingado is a machine learning library focused on economics and finance use cases. This package aims to be suitable for beginners and advanced users alike. Use cases may range from programmatic data retrievals using SDMX to experimentation with machine learning-based econometric estimators to more complex forecasting pipelines used in production.

gingado, a LinkageX resource

Software

pysdmx, a LinkageX resource

pysdmx is a pragmatic and opinionated library written in Python. It focuses on simplicity, providing a subset of SDMX functionalities without requiring advanced knowledge of SDMX.

pysdmx, a LinkageX resource

Software

Fusion Transformer

Fusion Transformer (FT) is a command line application for converting SDMX data between transmission formats.

Fusion Transformer

Software

SDMX Test Compatibility Kit

The SDMX TCK is a tool for measuring the compliance and coverage of an SDMX RESTful endpoint against the available SDMX REST API versions.

SDMX Test Compatibility Kit

Software

Project LinkageX

Project ‘LinkageX’ is the Swiss Army knife for SDMX. Aimed at data scientists and developers, LinkageX simplifies the use of SDMX data and metadata, enabling seamless integration and full utilization of the SDMX metamodel. This powerful Python-based toolkit streamlines SDMX-related workflows, enhancing data synchronization and harmonization efficiency and effectiveness.

Project LinkageX

Software

Fusion Metadata Registry

FMR structural metadata registry helps official statistics organisations externalise and gain control of their statistical structural metadata with the benefits of maintainability, re-use, standardisation, harmonisation and improved metadata and data governance.

Fusion Metadata Registry
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This Service uses generative AI to assist with statistical analysis and research . While the Service strives to deliver useful information, the output (“Output”) may contain inaccuracies, omissions, or biases. The Output is provided for informational purposes only and should not be considered professional advice. You remain responsible for how you interpret and use the Output.

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