Statistics%20New%20Zealand

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Statistics New Zealands Case StudyCreating
a New Business Model for a National Statistical
Office if the 21st CenturyCraig Mitchell, Gary
Dunnet, Matjaz Jug

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Overview

• Introduction organization, programme, strategy
• The Statistical Metadata Systems and the
  Statistical Cycle description of the
  metainformation systems, overview of the process
  model, description of different metadata groups
• Statistical Metadata in each phase of the
  Statistical Cycle metadata produced used
• Systems and Design issues IT architecture,
  tools, standards
• Organizational and cultural issues user groups
• Lessons learned

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Business model Transformation Strategy

• 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.
• A disciplined approach to data and metadata
  management, using a standard information
  lifecycle
• An agreed enterprise-wide technical architecture

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BmTS Metadata

• The Business Model Transformation Strategy (BmTS)
  is designing a metadata management strategy that
  ensures metadata
• fits into a metadata framework that can
  adequately describe all of Statistics New
  Zealand's data, and under the Official Statistics
  Strategy (OSS) the data of other agencies
• documents all the stages of the statistical life
  cycle from conception to archiving and
  destruction
• is centrally accessible
• is automatically populated during the business
  process, where ever possible
• is used to drive the business process
• is easily accessible by all potential users
• is populated and maintained by data creators
• is managed centrally

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A - 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/termi
  nology
• 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

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B - 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)

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How to come from A to B?

1. Identified the key (10) components of our
   information model.
2. Service Oriented Architecture.
3. Developed Generic Business Process Model.
4. Development approach from stove-pipes to
   components and core teams.
5. Governance Architectural Reviews Staged
   Funding Model.
6. Re-use of components.

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10 Components within BmTS
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Statistics New Zealand Current Information
Framework
Need
Design/ Build
Collect
Process
Analyse
Disseminate
Generic Business Process
Time Series Store ( INFOS)
QMS, Ag
Range of information stores by subject area
(silos)
HES etc.
ICS Store
Web Store
Metadata Store (statistical, e.g. SIM)
Reference Data Store (e.g. BF, CARS)
Software Register
Document Register
Management Information - HR Finance Data Stores
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Statistics New Zealand Future Information
Framework
Need
Design/ Build
Collect
Analyse
Disseminate
Process
Generic Business Process

TS
Raw Data
Output Data Store (confidentialised copy of IDS
- Physically separated)
Clean Data
Summary Data
Input Data Store
ICS
WEB
Metadata Store (statistical/process/knowledge)
Reference Data Store
Software Register
Document Register
Management Information - HR Finance Data Stores
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CMF gBPM Mapping
CMF Lifecycle Model Statistics NZ gBPM (sub-process level)
1 - survey planning and design Need (sub-processes 1.1 - 1.5) Develop Design (sub-processes 2.1 - 2.6)
2 - survey preparation Build (sub-processes 3.1 - 3.7) Collect (sub-process 4.1)
3 - Data collection Collect (sub-processes 4.2 - 4.4)
4 - Input processing Collect (sub-process 4.5) Process (sub-processes 5.1 - 5.3)
5 - Derivation, Estimation, Aggregation Process (sub-processes 5.4 - 5.7)
6 - Analysis Analyse (sub-processes 6.1 - 6.6)
7 - Dissemination Disseminate (sub-processes 7.1 - 7.5)
8 - Post survey evaluation Not an explicit process, but seen as a vital feedback loop.
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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

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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.

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Metadata End-to-End - Worked Example

• Question Text Are you employed?
• Need
• Concept discussed with users
• Check International standards
• Assess existing 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)

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Metadata End-to-End - Worked Example

• Question Text Do you live in Wellington?
• 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

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Conceptual View of Metadata

• Anything related to data, but not dependent on
  data metadata
• There are four types of metadata in the model
  Conceptual (including contextual), Operational,
  Quality and Physical
• defined by MetaNet

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Implementation Dimensional Model
Metadata
FACT
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Dimensional Model
Metadata
FACT
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Architecture
User access
INFORMATION PORTAL
Metadata
Service layer
Input Data Environment
FACT
FACT
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Fact definitions
Versioning
Time
Questions Variables
Dimensions Hiearchies
Units of Interest
Collections Instruments
Respondents
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Goal Overall Metadata Environment
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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)
• IDE Metadata tool
• Currently s/sheet based
• Audience Model
• Public, professional, technical added system

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SOA
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Standards Models - The MetaNet Reference ModelTM

• Two Level Model based on
• Concepts basic ideas, core of model
• Characteristics elements, attributes, make
  concepts unique

• Terms and descriptions can be adapted
• Concepts must stay the same
• Concepts should be distinct and consistent
• Concepts have hierarchy and relationships

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Collection
Eg. Census Frequency 5 yearly
Eg. Census 2006
Classification CITY Category
WGTN Classification NZ Island Category NTH
ISL
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Defining Metadata Concepts Example
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How will we use MetaNet?

1. Use to guide the development of a Stats NZ model
2. Another model (SDMX) will be used for additional
   support in gaps
3. Provides the base for consistency across systems
   and frameworks
4. Will allow for better use and understanding of
   data
5. Will highlight duplications and gaps in current
   storage

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Metainformation systems
Concept Based Model
SIM
Other Metadata stored in
IDE
CARS

• Business Frame
• Survey Systems
• BmTS components
• etc

Classifications
Domain Value
Data Collections
Variables
Fact Classification
Categories
Statistical Units
Response
Sample Design
Concordance
Collection
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Metadata Users - External

• Government,
• Public,
• External Statisticans (incl. Intl Orgs)

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Metadata Users - Internal

• Statistical Analysts
• IT Personnel (business analysts, IT designers
  technical leads, developers, testers etc.)
• Management
• Data Managers / Custodians / Archivists
• Statistical Methodologists
• External Statisticians (researchers etc.)
• Architects - data, process application
• Respondent Liaison
• Survey Developers
• Metadata and Interoperability Experts
• Project Managers Teams
• IT Management
• Product Development and Publishing
• Information Customer Services

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Lessons Learnt Metadata Concepts

• Apart from 'basic' principles, metadata
  principles are quite difficult. To get a good
  understanding of and this makes communication of
  them even harder.
• Every-one has a view on what metadata they need -
  the list of metadata requirements / elements can
  be endless. Given the breadth of metadata - an
  incremental approach to the delivery of storage
  facilities is fundamental.
• Establish a metadata framework upon which
  discussions can be based that best fits your
  organisation - we have agreed on MetaNet,
  supplemented with SDMX.

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Lessons Learnt BPM

• To make data re-use a reality there is a need to
  go back to 1st principles, i.e. what is the
  concept behind the data item. Surprisingly it
  might be difficult for some subject matter areas
  to identify these 1st principles easily,
  particularly if the collection has been in
  existence for some time.
• Be prepared for survey-specific requirements the
  BPM exercise is absolutely needed to define the
  common processes and identify potentially
  required survey-specific features.

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Lessons Learnt Implementation

• Without significant governance it is very easy to
  start with a generic service concept and yet
  still deliver a silo solution. The ongoing
  upgrade of all generic services is needed to
  avoid this.
• Expecting delivery of generic services from input
  / output specific projects leads to significant
  tensions, particularly in relation to added scope
  elements within fixed resource schedules.
  Delivery of business services at the same time as
  developing and delivering the underlying
  architecture services adds significant complexity
  to implementation.

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Lessons Learnt Implementation

• Well defined relationship between data and
  metadata is very important, the approach with
  direct connection between data element defined as
  statistical fact and metadata dimensions proved
  to be successful because we were able to test and
  utilize the concept before the (costly)
  development of metadata management systems.

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Lessons Learnt SOA

• The adoption and implementation of SOA as a
  Statistical Information Architecture requires a
  significant mind shift from data processing to
  enabling enterprise business processes through
  the delivery of enterprise services.
• Skilled resources, familiar with SOA concepts and
  application are very difficult to recruit, and
  equally difficult to grow.

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Lessons Learnt Governance

• The move from silo systems to a BmTS type model
  is a major challenge that should not be
  under-estimated.
• Having an active Standards Governance Committee,
  made up of senior representatives from across the
  organisation (ours has the 3 DGSs on it), is a
  very useful thing to have in place. This forum
  provides an environment which standards can be
  discussed agreed and the Committee can take on
  the role of the 'authority to answer to' if need
  be.

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Lessons Learnt Other

• There is a need to consider the audience of the
  metadata.
• Some metadata is better than no metadata - as
  long as it is of good quality.
• Do not expect to get it 100 right the very first
  time.

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Questions?