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Developing and applying business process models in practice. Statistics Norway Jenny Linnerud and Anne Gro Hustoft. Business Process Model (BPM) for Statistics Norway. Project within our programme on improvement and standardisation of statistical production (FOSS) Progress

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developing and applying business process models in practice

Developing and applying business process models in practice

Statistics Norway

Jenny Linnerud and Anne Gro Hustoft

business process model bpm for statistics norway
Business Process Model (BPM)for Statistics Norway

Project within our programme on improvement and

standardisation of statistical production (FOSS)

Progress

  • BMP project started in March 2008 and ended mid-August 2008

Resources

  • 520 man-hours were used
bpm project group
BPM project group

The project group consisted of 9 members

of the FOSS coordination group, who

represent different professional areas within

the process:

management support, data processing, IT

industry, labour market statistics, registers

IT development, metadata, sample surveys,

population statistics and statistical methods.

statistics norway s business process model
Statistics Norway’sBusiness Process Model

Specify

needs

1

Develop

and

design

2

Build

3

Collect

4

Process

5

Analyse

6

Disseminate

7

slide5

Specify needs

1

Develop and design

2

Build

3

Collect

4

Process

5

Analyse

6

Disseminate

7

Prepare data for dissemination database

7.1

Determine need for information

1.1

Outputs

2.1

Build and enhance process components

3.1

Establish frame and registers, select sample

4.1

Classify and code

5.1

Acquire domain intelligence

6.1

Integrate production system with other systems

3.2

Set up collection

4.2

Produce product

7.2

Consult and confirm need

1.2

Frame, register and sample methodology

2.2

Micro-edit

5.2

Produce statistics

6.2

Establish output objectives

1.3

Data collection methodology

2.3

Test production system

3.3

Run collection

4.3

Macro-control

5.3

Quality assure statistics

6.3

Release and promote product

7.3

Check dataavailability

1.4

Process and analysis methodology

2.4

Finalise production system

3.4

Finalise collection

4.4

Impute for partial non-response

5.4

Interpret and explain statistics

6.4

Manage user queries

7.4

Calculate weights and derive

variables

5.5

Prepare business case

1.5

Production system

2.5

Prepare statistics for dissemination

6.5

Finalise content

6.6

Business Process Model

slide6

Data

ready for

processing

Classify

and code

5.1

Micro-

edit

5.2

Macro-

control

5.3

Imputation

for partial

non-response

5.4

Calculate

weights and

derive

variables

5.5

Link data sources

and establish

statistical registers

5.1.1

Run automated

control and

correction routines

5.2.1

Identify and

investigate outliers

and critical values

5.3.1

Run imputation

routines for partial

non-response

5.4.1

Impute

for unit

non-response

5.5.1

Identify and

establish

statistical units

5.1.2

Perform

manual editing

5.2.2

Perform

controls at

macro-level

5.3.2

Evaluate

imputations

5.4.2

Calculate

weights

5.5.2

Code

and store

micro-data

5.1.3

Supplement

statistical

registers

5.5.3

Prepare

derived

variables

5.5.4

Store

micro-data

5.5.5

Data

ready for

analysis

Phase 5. Process

slide7

Comparison with

Generic Statistical Business Process model

Specify needs

1

Develop anddesign

2

Build

3

Collect

4

Process

5

Analyse

6

Disseminate

7

Data collection

instyument

3.1

Standardise

and anonymise

5.1

Prepare data for dissemination database

Update output

systems

7.1

Determine need for information

1.1

Outputs

2.1

Establish frame and registers, select sample

4.1

Acquire domain intelligence

6.1

Set up collection

4.2

Integrate

data

5,.2

Consult and confirm need

1.2

Frame, registerand sample methodology

2.2

Build and enhance process components

3.2

Produce statistics

Prepare

draft outputs

6.2

Produce products

7.2

Integrate production system with other systems

Configure workflows

3.3

Establish output objectives

1.3

Data collection methodology

2.3

Run collection

4.3

Quality assure statistics

Verify outputs

6.3

Classify and code

5.3

Release, 7.3

market and promote product 7.4

7.3

Check dataavailability

1.4

Process and analysismethodology

2.4

Finalise collection

Load data into

processing

environment

4.4

Interpret and explain statistics

6.4

Micro-edit

5.2

Test production system

3.4

Manage user

customer queries

7.5

Prepare business case

1.5

Production system

Processing systems

and workflow

2.5

Prepare statistics for dissemination

Disclosure control

6.5

Macro-control

5.3

Finalise production systems

3.5

Finalise content

outputs for

dissemination

6.6

Edit and

impute

5.4

Impute for partial non-response

5.4

Calculate

aggregates

5.7

Calculate weights 5.6and derive new

variables

5.5

slide8

This process is associated with, among other things:

  • Quality control in every processes
  • Identify and propose process-related improvements
  • Collection, follow-up and analysis of process data
  • Identify and propose product-related improvements
  • Collection, follow-up and analysis of user and customer feedback
  • - Quality indicators
slide9

Examples of resources under this:

Legal acts

Control documents e.g. IT-strategy

Systems and associated documentation

Templates, guidelines and handbooks

Committees, fora, expert groups

Support processes, e.g. ITIL (IT Infrastucture Library)

Data storage and administration

Population administration

Cross cutting:

Security

International activities

Financial matters

Competence and development

Last but not least:

Business Process Model

recommendations from the bpm development project
Recommendations from the BPM development project
  • The business process model will need to be reviewed and updated to ensure that it reflects the real state of affairs at any time.
  • The model originally in Norwegian was translated into English for international use.
  • A process guide for the model should be made available on Statistics Norway’s intranet.
case study
Case study

- Description of the production process for Price index for legal services with emphasis on the use of metadata throughout the process.

  • Description of the process for a new statistic and for future publishing of the same statistic.
  • Creation of a metadata checklist that can be used whenever this type of statistics is produced.

- 7 participants: statistics, IT, metadata

- 435 man-hours used.

slide15

Metadata systems &

Statistics Norways Statistical Business Process Model

slide16

Different actors &

Statistics Norways Statistical Business Process Model

slide17

Conclusions - case study

  • Process improvements were suggested and made
  • - Include metadata documentation and linking of metadata in formal approval procedure
  • - Suggestions for improved functionality in systems were identified and improvements made.
conclusions bp model
Conclusions – BP model

- The method of documenting a statistic based on the Statistical Business Process Model, can be used for other statistics.

- Documentation of new and established statistics is useful for training new employees and for rotation of current employees

slide19

Conclusions – BP model – cont.

  • The business process model is an important
  • tool in planning, standardising and improving
  • work processes in statistical production, and
  • for training purposes.
  • The business process model is also a
  • communication tool for standardisation and
  • cooperation between statistical agencies
  • and government departments.