© Statistisches Bundesamt, I/A Case study Federal Statistical Office Germany (Destatis) Joint...

© Statistisches Bundesamt, I/A

Case study Federal Statistical Office Germany (Destatis)

Joint UNECE/ EUROSTAT/ OECD Work Session on Statistical Metadata (METIS) Lisbon, 11 – 13 March, 2009


About lessons learned

About process models

About the systems

About usI. Who we are

II. Where we started

III. What is already there

IV. What is being implemented

V. What is planned for the future

VI. How we use process models

VII.What we have learned

Content


Who we are

Metadata management unit – 2-3 people

Established in mid-2007

Part of central coordination department

Several working groups and projects


DESTATIS

Central authority in official statistics in Germany

2710 employees (as of July 2008)

50.5% with graduate degree (16.4% with upper post-secondary degree)


Official Statistics in Germany

14 Länder offices – 1 Federal Statistical Office (Destatis)

Organizationally entirely independent offices

Many statistical activities are carried out jointly by federal and Länder offices


Work sharing in the statistical system

Federal Statistical Office (Destatis) organizes federal statistical activities …

… and is responsible for the methodology

Länder offices carry out the core statistical processes


Joint decision making in the statistical system

Several decision making bodies and working groups on different levels

… applies also to IT

Statistical offices in Germany share existing and develop new IT-systems together


Where we started

Some systems in place, when we started

New project to standardize business procedures and IT-systems: “SteP – Standardisierung der Produktion”

No common metadata strategy – no agreed metadata model

Several requirements by governing committees

Metadata management for Census 2011


GENESIS-Online

RDC Metadata-system

Survey Database

Statistical activitiesdatabase

QualityreportsIn-house

systemsSAS

Excel

???

Collect Process Analyze Disseminate Design

Internet

data editing tool

What is already there

edr tool

electronic questionnaire tool


What is already there GENESIS-database

output database, cube based, extensive data and metadata model, stores metadata internally (about 1,300 cube-variables), supports sdmx-queries

RDC-metadata systemtechnically a spin-off from GENESIS to store metadata that describes micro data files for researchers, successfully implemented, great level of detail (currently 5,600 variables used in data files for 33 statistical activities)


What is already there .BASE

in-house production system to support an end-to-end workflow, consists of a central database for metadata (in the form of XML-files), current tools that run on this metadata: data editing tool, edr-tool, electronic questionnaire tool, no (standard) conceptual model, metadata on a rather technical level


What is being implemented Klass-Service

Neuchâtel-based classification server, carried out in collaboration with Destatis’ classification unit, in fact a (very thorough) reengineering of an older system, collaboration with Statistics Norway, implementation: Bavarian State Office for Statistics and Data Processing

Reengineering of Statistikdatenbankinternal information system, registers all statistical activities (on a higher level)


What is planned Metadata systems for the Census 2011,

among them:

Variable serveras part of the census project, designed after consultation with the Swiss Statistical Office, ought to be Neuchâtel-based as well, would ideally provide a connection between output systems (e.g. GENESIS) and processing systems (e.g. .BASE)


What is planned Also upcoming:

Organizational matters

Organizational implementation of the systems

Drafting of a strategy

Making conceptual model and process model known in other parts of the organization


How we use process models

Management processes

Support processes

Collect Process AnalyzeCore

processes

Development Production Interpretation

Manage statistical activities

Develop methodology

Design Disseminate

Supply infrastructure

Supply resources

Manage human

resourcesOrganize

Communicate internally

Manage operationsManage Marketing and

Public RelationsManage people

Devise strategy

Desta

tis P

rocess M

od

el


How we use process models

1. Print questionnaire

3. Monitor responses and follow-up non-respondents

2. Send questionnaire

4. Manage interviewers

5. Import data

6. Update registers and other reference files

7. Draw sample

8. Initial Check

9. Code and classify responses

10. Derive new variables

11. Perform micro editing

12. Calculation

13a. Estimation + 13b. Estimate sampling error

14. Check data protection

15. Finalise output tables

16. Exchange output data

17. Archive (micro) data

18. Analyse micro data further

19. Promote products

20. Manage respondents

SteP Process Model

3.Survey

Preparation

4.Survey Execution

Specify NeedsBuild

2Design

1Disseminate

6Analyse

5

Define business processes

0.5

Establish objectives

0.3

Check data availability

0.4

Consult and confirm

0.2

Determine need for information

0.1

Load data into electronic (standard)

format4.2

Run data collection

4.1

Calculate weights4.7

Impute missing data4.5

Generate new variables and

statistical units4.6

Finalise data files4.9

Integrate data4.8

Edit data4.4

Classify and code4.3

Prepare statistics for dissemination

5.4

Check quality of statistics

5,2

Interpret and explain statistics

5.3

Finalise statistics5.5

Specify and produce statistics

5.1

Promote products6.5

Release products6.3

Manage customers

6.4

Produce products6.2

Manage output database

6.1

Design Output1.1

Design IT-requirements

1.6

Design methodology

1.3

Define populations and variables

1.2

Configure workflow

3.1

Finalise IT-system for production

2.4

Run field test2.5

Test IT-systems2.3

Define survey processes

1.5

Produce new / (Re-)Configure old

IT-systems2.2

Configure and test collection

instruments 2.6

Archive 7

Dispose of data and metadata

7.3

Archive data and metadata

7.2

Manage archive7.1

Collect & Process3 & 4

Prepare data collection

3.4

Manage data providers

3.3

Draw sample / Produce lists

3.2

Draft laws1.4

Evaluate 8

Execute evaluation survey

8.1

Design IT-systems2.1

Validate metadata

Validate metadata

Validate metadata

Validate metadata

1.Business Case Development

2.IT-

Development

5.Analysis

Census 2011 Processes

Sub

-Pro

cess

Process

Disseminate6

Analyse5

Load data into electronic (standard) format

4.2

Run data collection4.1

Calculate weights4.7

Impute missing data4.5

Generate new variables and statistical units

4.6

Finalise data files4.9

Integrate data4.8

Edit data4.4

Classify and code4.3

Prepare statistics for dissemination

5.4

Check quality of statistics

5,2

Interpret and explain statistics

5.3

Finalise statistics5.5

Specify and produce statistics

5.1

Promote products6.5

Release products6.3

Manage customers6.4

Produce products6.2

Manage output database

6.1

Configure workflow 3.1

Archive 7

Dispose of data and metadata

7.3

Archive data and metadata

7.2

Manage archive7.1

Collect & Process3 & 4

Prepare data collection

3.4

Manage data providers

3.3

Draw sample / Produce lists

3.2

Sub

-Pro

cess

Process

3.4.1 = 1. Print questionnaires (applies to all surveys with collection

instrument = paper questionnaire)

3.4.2 = 2. Send questionnaires (applies to all surveys with collection instrument = self administered paper

questionnaire)

4.1.1 = 3. Monitor responses and follow-up non-respondents

4.1.1 = 5. Import data (applies to all surveys with collection instrument =

electronic data transmission

Not included = 4. Manage interviewers

3.3.1 = 6. Update registers and other reference files

3.2.1 = 7. Draw sample (applies to all surveys with sampling method =

probability sampling)

4.1.1 = 8. Initial check (there are no details on this process)

4.3 = 9. Classify and code

4.4 = 11. Edit data

4.5 = 10. Generate new variables and statistical units

Not included = 12. Calculations

= 13a. Projection (applies to all surveys with sampling method =

probability sampling)

5.2 = 13b. Estimate sampling error (applies to all surveys with sampling

method = probability sampling)

5.4 = 14. Check data protection (can also be part of 4. Process depending on method of data

protection)

5.5 = 15. Finalise output tables

Not included = 16. Exchange output data

7.2 = 17. Archive (micro) data

Not included = 18. Analyse micro data further

6.5 = 19. Promote products

6.5 = 20. Manage respondents


What we have learned

Communication is important

Strategy is needed for coordination

Learn from previous implementations

Use existing models like Neuchâtel/ISO 11179

Cooperate internationally


Thank You For Your Attention!

© Statistisches Bundesamt, I/A Case study Federal Statistical Office Germany (Destatis) Joint...

Documents

Transcript of © Statistisches Bundesamt, I/A Case study Federal Statistical Office Germany (Destatis) Joint...