Panel discussion questions for Session ii

Panellists

• Dan Gilman (BLS)– BLS are an associate member of the DDI Alliance

• Eric Rodriguez (INEGI)• Achim Wackerow (DDI Alliance)• Arofan Gregory (Invited Expert)– Metadata Technology, Open Data Foundation

• Jeremy Iverson & Dan Smith (Invited Experts)– Algenta Technologies (developed Colectica)

• Plus…all METIS participants!

Starting points• Focus– Primarily DDI-L rather than DDI-C

• Implementations– Very early

• Statistics New Zealand (SNZ)• Australian Bureau of Statistics (ABS)

– Project / team established with DDI in scope• French National Institute for Statistics and Economic Studies

(INSEE)– Considering

• Many - including Statistics Sweden, Statistics Norway, Statistics Canada, ONS

Q1 :To what extent is DDI really implemented within the statistical production process?

• Early days for NSIs in terms of DDI-L• SNZ have used DDI-C for Archiving since 2006– Data & metadata disseminated to statistical output areas

& researchers via microdata access facilities• Positive experience in this regard led to decision to

pursue “all of lifecycle” rather than “end of lifecycle” approach via DDI-L– Replace & enhance existing metadata management

processes• RFT (Request for Tender) process (completed 2011)

included definition of business needs & strategy, testing of market etc

continued (1.2)• ABS– REEM (Remote Execution Environment for Microdata)

in production use on restricted basis• Microdata described using DDI-L for input to environment

– Aggregate tabulations can be returned using SDMX• Next phase of development (analytical capabilities beyond

simple tabulation) is underway– Proof of Concept (PoC) stage of Metadata Registry /

Repository (MRR) development implemented elements of DDI model (delivered June 2011)

– Extensive mapping between DDI-L and ABS Questionnaire Development Tool

continued (1.3)

• Question may also warrant reference to implementation of DDI-L to support statistical production by organisations other than NSIs– Several are further along the path than NSIs, eg• University of Michigan Survey Research Center

– Several applications for different phases of life cycle

• Canadian RDC Network

Q2 : What are the based-DDI tools used in practice by the statistical offices (Colectica, others...) ?

• SNZ plan to implement Colectica next year to support documentation & data management processes– Work underway to extract content from existing

systems as DDI-L (eg household survey platform) via custom development• Investigating StatTransfer application which now

supports DDI-L• Interested in DDI/Blaise interoperability

continued (2.2)

• ABS– REEM• Developed by ABS in partnership with vendor

– MRR PoC• Developed by ABS harnessing design expertise from

consultants– Evaluating Colectica– Customised utilities/applications• eg extract data and metadata for REEM, in accordance

with DDI-L specification, from existing repositories

Q3 : Are there applications or tools which communicate with DDI (Blaise, others...)?

V0.2 of diagram – requires further quality assurance

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continued (3.2)• Plus– MQDS (Michigan Questionnaire Documentation

System)• extract comprehensive metadata from Blaise survey

instruments & render as DDI– Assorted applications internal to agencies that

developed them– OpenDDI (beta)

• global catalog of DDI documented surveys • Inclusion of support for DDI-L in StatTransfer is

seen by other vendors as a signal of its ongoing prominence

Q4a : Is there a repository of variables, questions ?

• DDI Alliance has a major focus on re-use of metadata– a cornerstone for the design of DDI-L– practical work underway on business practices &

processes (eg via case studies) to support re-use• Technical design of standard & availability of repositories &

applications are “necessary but not sufficient” to achieve re-use in practice

• Case studies will influence further technical support • Colectica Repository can be used for this role– eg, SNZ’s current reference metadata library to be

replaced by Collectica • Various banks for variables, questions etc have been

built by various agencies

Q4b : To what extent is a variable repository restrictive for the user? For example if he uses a name of a variable repository is he

obliged to use the code list associated in the repository?

• Very modular model to support reuse• Strong support for relating different objects– eg Variable X is the same as Variable Y in terms of concept,

universe and response categories but the codes used to denote the response categories are different

• Applications working with the DDI-L can harness this modularity, eg– User : I need a variable identical to that one except I need

different codes – System : New Variable created which

• reuses majority of “building blocks” for Existing Variable• has explicitly defined & recorded relationship to Existing Variable

Q5 What is the strategy to implement statistical standards with a view of covering the life cycle of statistical operations from an

end-to-end perspective?

• SNZ– implement improved metadata management in a

staged manner across the end-to-end lifecycle• Full support for DDI across all of the statistical business

process is not planned to be complete until at least 2020.

– have focused on the documentary needs of the organisation• ensure reference metadata available to identify the

studies SNZ undertake and to provide staff with key contextual information.

continued (5.2)

• now expanding focus to include data-centric needs– including management of classifications and

variables with DDI.

• Strategy is to begin to apply DDI across a wide range of information objects across the statistical business process in order to better understand needs and the coverage of DDI.

continued (5.3)• In the next 12 months plan to start using DDI to describe

Concepts, Variables, Classifications, Questions.• Ten year roadmap for metadata indicates systems

implementations of classification, question and variable libraries, building upon initial implementation, to expand and enhance functionality and meet long term needs.

• It is intended DDI-L will be the primary metadata standard within SNZ– Supplemented by others where required

• (e.g. SDMX codelists for classifications) – Externally use a mix of DDI and SDMX depending on which is

best suited to a particular use case.

continued (5.4) • ABS similar to SNZ– Aiming to achieve transformation by 2017– Relatively greater early emphasis on “machine

actionable” aspects for metadata driven processes– Planning to apply DDI and SDMX internally in

accordance with “industry standard” practice (once that emerges)• Emphasis on facilitating, in practice, international

collaboration & sharing regarding new methods and IT components

• Currently expect GSIM (Generic Statistical Information Model) to be operationalised via DDI & SDMX in future.

• ABS Transitional Model in meantime spans DDI & SDMX

Q5a Is it to implement only DDI awaiting the results of the SDMX/DDI dialogue in progress?

• No• SNZ is using SDMX as part of dissemination platform and

now starting to implement DDI across the business process.• During the next 12 months will map the metadata captured

throughout the statistical business process with dissemination metadata needs.– Data dissemination uses an SDMX based system (OECD.stat)– By default will map DDI to SDMX for a particular use case.– Within 18 months expect metadata flows between DDI & SDMX

based systems regardless of outcome of SDMX/DDI dialogue.• ABS : Similar

– Recognise a range of community benefits if agencies flow metadata between DDI & SDMX on a consistent basis

Q5b : Is it to implement DDI and SDMX from the very beginning in the objective covering the life cycle of

statistical operation end to end ?

• Yes

Q5c Other strategy?• 5b is key strategy

Q6: What are perspectives in term of versioning for DDI : frequency of important changes?

• Good balance between– responsiveness to identified additional requirements and bug

fixes, and– Stability

• Similarly to SDMX– when there is a new release agencies do not need to upgrade

unless & until they have a business driver– emphasis on testing, backwards compatibility

• Advantages– Dual product line (C vs L) helps limit conflict of interest between

users with simple needs & with advanced needs– DDI 3.0, 3.1, 3.2 more responsive than SDMX 2.0, 2.1 gap– New release framework is even more responsive

Q7: Which institute has to deliver metadata in DDI 2 ? DDI3 ? Which institute has to deliver metadata to data

archives working with DDI2 ? DDI3 ?

• SNZ does not have any requirement to disseminate data and metadata in DDI– Aim to encourage other government agencies to

use DDI-L where possible to describe their data.• DDI Usage Map

• SDMX/DDI combined usage map

Bank NSI UniversityGovernment

Agency Archive Other TOTALSDMX only 173 33 0 2 0 4 212DDI only 0 55 43 9 16 3 126SDMX and DDI 1 8 2 0 0 1 12Total 174 96 45 11 16 8 350

Q8: Is there an implementation of metadata using Blaise and DDI ?

• SNZ– No use of Blaise and DDI together.– Intention Blaise should be able to be generated

from DDI based metadata repositories.

• CF– Q3– Colectica– MQDS

Q9 : New Are there any evaluations of the DDI at statistical offices that show how well the DDI addresses

their statistical metadata needs?• SNZ

– Didn’t see question in advance– May have more information than ABS (eg to support RFT)

• ABS– High level information written for ABS specific audience

(jargon!)– Detailed examination in particular areas (eg questionnaires,

microdata record relationships) positive, sometimes requiring minor extensions

• Excellent idea to share general information on evaluations.– Aim for well balanced factual information

• not written specifically to advocate a business case– Seek examples from other NSIs & other agencies