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Statistics New Zealand’s End-to-End Metadata Life-Cycle ”Creating a New Business Model for a National Statistics New Zealand’s End-to-End Metadata Life-Cycle ”Creating a New Business Model for a National Statistical Office if the 21 st Century” Gary Dunnet Manager, Business Solutions gary. [email protected] govt. nz

Bm. TS Scope 1. A number of standard, generic end-to end processes for collection, Bm. TS Scope 1. A number of standard, generic end-to end processes for collection, analysis and dissemination of statistical data and information l l l Includes statistical methods Covering business process life-cycle To enable statisticians to focus on data quality and implemented best practice methods, greater coordination and effective resource utilisation. 2. A disciplined approach to data and metadata management, using a standard information lifecycle 3. An agreed enterprise-wide technical architecture

Bm. TS Success Criteria - Financial • A reduction in the operating cost to Bm. TS Success Criteria - Financial • A reduction in the operating cost to produce a statistical output (that are operating on a separate subject matter system) by between 10 – 20% after moving to the new business model • A reduction of 50% in the investment (of time and money) required to implement the end to end processes and systems required for a new statistical output

Generic Business Process Model From: Need Design/ Build Collect Process Analyse Disseminate To: Need Generic Business Process Model From: Need Design/ Build Collect Process Analyse Disseminate To: Need Design/ Build Collect Process Analyse Disseminate

E-Form Raw Data Clean Data Aggregate Data 2. Output Data Store ‘UR’ Data Summary E-Form Raw Data Clean Data Aggregate Data 2. Output Data Store ‘UR’ Data Summary Data Official Statistics System & Data Archive 1. Input Data Store Web 6. Transformations RADL 5. Information Portal Output Channels CAI Multi-Modal Collection Imaging Admin. Data 4. Analytical Environment INFOS CURFS 10. Workflow 8. Customer Management 7. Respondent Management 3. Metadata Store Statistical Process Knowledge Base 9. Reference Data Stores

Existing Metadata Issues • • • metadata is not kept up to date metadata Existing Metadata Issues • • • metadata is not kept up to date metadata maintenance is considered a low priority metadata is not held in a consistent way relevant information is unavailable there is confusion about what metadata needs to be stored the existing metadata infrastructure is being under utilised there is a failure to meet the metadata needs of advanced data users it is difficult to find information unless you have some expertise or know it exists there is inconsistent use of classifications/terminology in some instances there is little information about data, where it came from, processes it has been under or even the question to which it relates

Target Metadata Principles • • • metadata is centrally accessible metadata structure should be Target Metadata Principles • • • metadata is centrally accessible metadata structure should be strongly linked to data metadata is shared between data sets content structure conforms to standards metadata is managed from end-to-end in the data life cycle. there is a registration process (workflow) associated with each metadata element capture metadata at source, automatically ensure the cost to producers is justified by the benefit to users metadata is considered active metadata is managed at as a high a level as is possible metadata is readily available and useable in the context of client's information needs (internal or external) track the use of some types of metadata (eg. classifications)

Metadata Logical Model Metadata Logical Model

Metadata: End-to-End l Need – – l capture requirements eg usage of data, quality Metadata: End-to-End l Need – – l capture requirements eg usage of data, quality requirements access existing data element concept definitions to clarify requirements Design – – l capture constraints, basic dissemination plans eg products capture design parameters that could be used to drive automated processes eg stratification capture descriptive metadata about the collection - methodologies used reuse or create required data definitions, questions, classifications Build – – l capture operational metadata about selection process eg number in each stratum access design metadata to drive selection process Collect – – – capture metadata about the process access procedural metadata about rules used to drive processes capture metadata eg quality metrics

Metadata: End-to-End (2) l Process – – – l capture metadata about operation of Metadata: End-to-End (2) l Process – – – l capture metadata about operation of processes access procedural metadata, eg edit parameters create and/or reuse derivation definitions and imputation parameters Analyse – – l capture metadata eg quality measures access design parameters to drive estimation processes capture information about quality assurance and sign-off of products access definitional metadata to be used in creation of products Disseminate – – – capture operational metadata access procedural metadata about customers Needed to support Search, Acquire, Analyse (incl; integrate), Report capture re-use requirements, including importance of data - fitness for purpose Archive or Destruction - detail on length of data life cycle.

Metadata: End-to-End - Worked Example Question Text: “Are you employed? ” l Need – Metadata: End-to-End - Worked Example Question Text: “Are you employed? ” l Need – – – l Concept discussed with users Check International standards Assess exisiting collections & questions Design – – l Design question text, answers & methodologies Align with output variables (e. g. ILO classifications) Data model, supported through meta-model Develop Business Process Model – process & data / metadata flows Build – – – Concept Library – questions, answers & methods ‘Plug & Play’ methods, with parameters (metadata) the key System of linkages (no hard-coding)

Metadata: End-to-End - Worked Example Question Text: “Are you employed? ” l l – Metadata: End-to-End - Worked Example Question Text: “Are you employed? ” l l – – – l Collect Question, answers & methods rendered to questionnaire Deliver respondents question Confirm quality of concept Process Draw questions, answers & methods from meta-store Business logic drawn from ‘rules engine’ Analyse – – – l Deliver question text, answers & methods to analyst Search & Discover data, through metadata Access knowledge-base (metadata) Disseminate – – Deliver question text, answers & methods to user Archive question text, answers & methods

Metadata: Recent Practical Experiences l l – – l Generic data model – federated Metadata: Recent Practical Experiences l l – – l Generic data model – federated cluster design Metadata the key Corporately agreed dimensions Data is integrateable, rather than integrated Blaise to Input Data Environment Exporting Blaise metadata ‘Rules Engine’ – – l Based around s/sheet Working with a workflow engine to improve (BPM based) Audience Model – Public, professional, technical – added system

Questions? Questions?