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The Use of Metadata Standards and Tools for 

Greater Transparency of Official Statistics

Highlights of CNSTAT Report on 

Transparency in Statistical Information :

Dan Gillman

US Bureau of Labor Statistics

FCSM 2022

Session G2

October 27, 2022
2 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Transparency

 In the report, transparency is defined as

 Transparency is the provision of sufficiently detailed documentation of all the 

processes of producing official estimates.

 The goal of transparency is to enable consumers of federal statistics to accurately 

understand and evaluate how estimates are generated

 From this, there is need for documentation

 Documentation and metadata

2 sides of the same coin
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Metadata

 Data used to describe some resource(s)

Role for data, not a kind

 Same as documentation, only more formal

Documentation – typically in text form

– Word, PDF, HTML documents

Metadata – typically in a database (repository)

– RDBMS (relational), XML (hierarchical), RDF (graph)

 Not all documentation can be formalized

Rationales – reasoning supporting some decision
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Metadata Schema

 Organized by a schema

Framework for structuring and organizing

Similar to a model

Contains bins (elements) for entering metadata

 Schema is a template for metadata

 Filled in schema is an instance
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Technical Specifications

 Schema is a kind of technical specification

 Formalized set of requirements

 Conform to specification

Satisfy all requirements

 Standards are examples

Technical specifications developed under Open, Fair, 

Balanced, Transparent, Consensus Process
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Standards

 Metadata Standards

Technical specifications

Define how metadata are organized, usually with a 

schema

Systems designed to implement standards

– Achieve conformance by satisfying requirements

– Guarantees enough metadata is available

Transparency, necessary condition

 Many metadata standards in statistics

DDI, SDMX, GSIM, GSBPM

Other statistical and generic standards
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Value of Metadata Standards

 Fit-for-purpose best practices from official statistics community

 Increase compatibility, interoperability of processes and systems

 Reduce development cost and maintenance burden

 Improve time-to-market with existing tools, methodology

 Improve quality with tried-and-tested methods, systems, 

processes

 Increase collaboration with international statistics community

 Use existing capacity building, staff with existing knowledge can 

be operational quicker
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Standards Explained 1/5

 GSBPM: Generic Statistical Business Process 

Model 

UNECE – developed and maintained

Describes the activities and processes of official 

statistics offices

Some uses – classifying survey design or production 

systems, and system development activities

Adapted by the Census Bureau and BLS

Broad worldwide adoption
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Standards Explained 2/5

 GSIM: Generic Statistical Information Model

UNECE – developed and maintained

Conceptual, reference framework for statistical 

information

Describes inputs/outputs (e.g., data set, variable) for 

GSBPM processes

Used for designing and standardizing data architectures

Not directly implementable

Examples – National statistical offices, especially in 

Europe and Australia
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Standards Explained 3/5

 DDI: Data Documentation Initiative

DDI Alliance – developed and maintained

Suite of metadata standards for social and 

behavioral science data

All have an XML implementable representation

Codebook (2000), Lifecycle (2008), Cross-Domain 

Integration (late 2022)
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Standards Explained 4/5

 DDI: Data Documentation Initiative

 Codebook – description of a data set or study, contains variables, 

questions, data structure

– Example 1 – International Household Survey Network (IHSN)

– Example 2 – Documentation of archived data sets at ICPSR (University of Michigan)

 Lifecycle – Supports GSBPM, like GSIM, provides linkages across surveys 

and time

– Example 1 – BLS Consumer Expenditure Survey public use microdata

– Example 2 – MIDUS (Mid-life in the US) study at the University of Wisconsin

 Cross-Domain Integration (still a draft, expected release early 2022)

– Supports multiple data structures; linkages across variables, time, data sets; supports 

data integration

– Independent of statistical domain; gaining usage in scientific and social data 

communities, including BLS and DOL
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Standards Explained 5/5

 SDMX: Statistical Data and Metadata eXchange

 Mainly used to describe data and metadata sets and how they are 

exchanged/reported

 Directly implementable in XML, CSV, JSON

 Automate exchange/dissemination through standard web service 

interfaces

 Has a metadata repository standard to allow distributed metadata storage

 Numerous open-source tools available

 Focus was on aggregated, international exchange. New version has more 

microdata features

– Examples – many around the world, especially national and 

international banks, national statistical offices

– Example in US – federal statistical agencies report national indicators 

to IMF DSBB via SDMX
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Metadata Systems

 Repository is the database for metadata

 Interface is the means to interact

 Combination is metadata system

 System can be combined with others

Makes metadata useful

Improves user experience
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Building Systems

 Obtain upper management support

Without this, long term success is unlikely

 Select technical specification

Existing standard is preferable

No reason to reinvent the wheel

Increase interoperability and consistency

 Don’t try to build a cathedral at the start

But use long-term plan as a guide
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Iterative Approach

 Build slowly, use iterative approach

Add useful new functionality at each stage

Easier to get funding for well-defined, small steps

 At each step

Build

Test

Deploy

Get feedback

Plan new functionality (based on feedback)

Repeat
Contact Information

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Dan Gillman

Information Scientist

Office of Survey Methods Research

www.bls.gov/osmr

(w) 202-691-7523

(c) 410-624-9582

Gillman.Daniel@BLS.Gov