Statistics New Zealand’s End-to-End Metadata Life-Cycle

”Creating a New Business Model for a National Statistical Office if the 21st Century”

Gary DunnetManager, Business Solutions

gary.dunnet@stats.govt.nz

BmTS Scope1. A number of standard, generic end-to end processes for

collection, analysis and dissemination of statistical data and information

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

BmTS 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:

To:

ProcessNeed

Design/Build

Collect Analyse Disseminate

NeedDesign/Build

Collect Process Analyse Disseminate

Need1

Design2

Build3

Collect4

Process5

Analyse6

Disseminate7

Determine information requirement

1.1

Determine and confirm need

1.2

Develop budget and plan

1.3

Obtain financial support

1.4

Develop detailed project plan

2.1

Develop survey methodology

2.2

Questionnaire design and testing

2.3

Design operational requirements

2.4

Design computer system

2.5

Obtain ministerial approval

2.6

Build collection vehicle

3.1

Build technology solution

3.2

Test technology solution

3.3

Implement solution3.4

Identify postout population and data

services4.1

Manage respondents

4.2

Post out4.3

Acquire data4.4

Close off collection4.5

Capture data into electronic form

5.1

Perform macro editing

5.2

Run imputations/ estimations

5.3

Produce clean dataset

5.4

Examine source data6.1

Produce Statistical Results

6.2

Validate Statistical Results

6.3

Interpret Statistical Results

6.4

Prepare Content for Dissemination

6.5

Conduct Quality Control

6.6

Receive and validate draft

content7.1

Manage and load dissemination repositories

7.2

Prepare pre-release for publishing

7.3

Manage first release7.4

Handle customer enquiries

7.5

2. Output Data Store

CleanData

AggregateData

1. Input Data Store

3. Metadata StoreStatistical

ProcessKnowledge Base

9. Reference Data Stores

4. Analytical Environment

5. Information Portal

6. Transformations

RawData

7. Respondent Management 8. Customer Management

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10. Workflow

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 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

Search and Discovery

Metadata and Data Access

Schema - SDMX

Frames/Reference Stores

Data

Definition

Passive metadata store/s

Question LibraryClassification Management

Business Logic

Data

Metadata: End-to-End Need

– capture requirements eg usage of data, quality requirements – access existing data element concept definitions to clarify requirements

Design– 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– 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) Process

– capture metadata about operation of processes– access procedural metadata, eg edit parameters– create and/or reuse derivation definitions and imputation parameters

Analyse– 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?” Need

– Concept discussed with users– Check International standards– Assess exisiting collections & questions

Design– 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?” 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– 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 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’ – Based around s/sheet– Working with a workflow engine to improve (BPM based)

Audience Model– Public, professional, technical – added system

Questions?