United Nations Economic Commission for EuropeStatistical DivisionUnited Nations Economic Commission for EuropeStatistical Division

Towards a Generic Statistical Business Process Model

Steven Vale, UNECE

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Contents

Background Modelling statistical business processes Applicability Structure and key features Relevance to SDMX Next steps

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Background

Defining and mapping business processes in statistical organisations started at least 10 years ago• “Statistical value chain”• “Survey life-cycle”• “Statistical process cycle”• “Business process model”

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Background

Defining and mapping business processes in statistical organisations started at least 10 years ago• “Statistical value chain” X• “Survey life-cycle” X• “Statistical process cycle” X• “Business process model” X

Generic Statistical BusinessProcess Model

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Modelling Statistical Business Processes Reached a stage of maturity where a

generic international standard is the logical next step

Many drivers for a generic model:• “End-to-end” metadata systems development• Harmonization of terminology• Software sharing• Process-based organization structures• Process quality management requirements• ...

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Why do we need a model?

To define, describe and map statistical processes in a coherent way

To standardize process terminology To compare / benchmark processes within

and between organisations To identify synergies between processes To inform decisions on systems

architectures and organisation of resources

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History of the Current Model

Based on the business process model developed by Statistics New Zealand

Added phases for:• Archive (inspired by Statistics Canada)• Evaluate (Australia and others)

Three rounds of comments Terminology and descriptions made

more generic Wider applicability?

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Applicability (1)

All activities undertaken by producers of official statistics which result in data outputs

National and international statistical organisations

Independent of data source, can be used for:• Surveys / censuses• Administrative sources / register-based statistics• Mixed sources

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Applicability (2)

Producing statistics from raw data(micro or macro-data)

Revision of existing data / re-calculation of time-series

Development and maintenance of statistical registers

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Structure of the Model (1)

Process

Phases

Sub-processes

(Descriptions)

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Structure of the Model (2)

National implementations may need additional levels

Over-arching processes• Quality management• Metadata management• Statistical framework management• Statistical programme management• ........ (8 more – see paper)

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Key features (1)

Not a linear model Sub-processes do not have to be followed

in a strict order It is a matrix, through which there are

many possible paths, including iterative loops within and between phases

Some iterations of a regular process may skip certain sub-processes

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Key Features (2)

In theory the model is circular:• Evaluation can lead to

modified needs and design In practice it is more like

a multiple helix:• There may be several

iterations of a process underway at any point in time

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Mapping to OtherModels

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Relevance to SDMX

Process modelling already mentioned in:• SDMX User Guide• V2 Technical Standards• Euro SDMX Metadata Structure

Common terminology If inputs and outputs use SDMX formats,

why not the intermediate processes?

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Standardized process descriptions

Harmonised processes

Rationalization of software

Use of open source and shared components

SDMX between components

Convergence of business architectures

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Next steps

Several organisations are implementing this model or similar ones

Gather implementation experiences and other comments as input for Part C of the “Common Metadata Framework”

Present to the Bureau of the Conference of European Statisticians

Role in SDMX?

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Questions and Comments?

steven.vale@unece.org

For more information see the METIS wiki:

www1.unece.org/stat/platform/display/metis