The role of the Business Register

in a changing environment at Statistics Netherlands

Beijing, 2004, session 2 Nico Heerschap

Old / current situation - changing environment

Ideal situation

Strategy for the short and medium term

The role of the Business Register

25 min.

2/14Content:

Organisation of SN:

Business statistics (BES)

Social statistics (SRS)

Macro economic statistics (MSP)

Technology and facilities (TNF)

Two distant locations

Types of statistics in the Division of Business Statistics:

Production (mainly for NA)

Short term (mainly turnover)

Investments

International trade

Thematical

Energy, Technology, Environment, Health, Agriculture,

Transport, Crime, Culture, Tourism

Business Register / Baseline

ESB

Input

Throughput

Output

Statistic 1 Statistic 2 Statistic 3 Statistic x

Product view

BR

CR3CR2

CR1 CRx

Old situation:

Changing environment:

Changing needs of customers: more integrated, coherent and quicker. New theme’s emerge

Growing competition in the market place

Pressure to reduce the survey burden on enterprises

Smaller budget: pressure to be more efficient: less staff but same or more output

New developments in IT and methodology

ESB

Disadvantages current situation:

No co-ordination between statistics / separate worlds

No integration of the data overall (quality and consistency)

Sometimes different figures for the same phenomenon

Overlapping customer bases

Same data suppliers approached by different statistics

Little documentation of processes / hardly any mobility

Inefficient processes (e.g. not invented here syndrome) /

high business costs

4/14

Conclusion: The situation within SN is not in line anymore

with a changing environment ESB

ESB

Main goals of SN:5/14

Strengthen the relationship with the customer: integrated, consistent, quicker, flexibility, one window

New position in the market place: integrating crossroad on the information highway, knowlegde institute (networks)

Reducing the survey burden by:

- Optimising the use of secondary sources

- Approaching the respondent in its own environment

More efficiency by redesigning the processes and applying new IT and methodology

Adapt the organisational structure, culture and skills (7S model of McKinsey)

In business terms:

• better and quicker output • lower input costs (SN / Enterprises) • and lower process costs (higher productivity)

Meaning:

• another way of making statistics • with less but more professional staff

ESB

ENTERPRISES

burden

Unanswered Survey needs

Old situation

CUSTOMERS

A

B

burden

UnansweredSurvey

needs

Desired situationENTERPRISES

CUSTOMERS

AB

Input

Throughput

Output

Theme 1 Theme 2 Theme 3 Theme x

One window for data dissemination services

One window for data-collection services

Merge

Merge

Merge

Merge

ESB

Throughput

Output

Input

One window for all data-collection

All input, primary and secondary

CBR

BACKBONES

Making of data-marts (selection, aggregation etc.)

Internal analists

Information development

Coupling data to the

backbone(s)

Transfer data toData warehouse

Data warehouse

Transactional dbase

Datarepository

Checking, editing and micro-inte-

gration

One window for all output services

Customers / data-users

Output forcustomer

L

L

L

WORK FLOW

MANAGEMENT

ESB

ExternalSBR

Output driven process

Data production factory

Knowledge institute- (integrated) publication- information development- customer base

META

DATA

SYSTEM

(1) B A C K B O N E S(BR)

(2) VARIABLES

(3) TIME

Survey-data

Survey-data

Survey-data

7/14

Administrative sources

Administrative sourcesAdministra-tive sources

Dimensions of the data repository:

8/14Main advantages, business case (1):

A uniform and consistent archive and output database for all business statistics (one window) with:

- standardised definitions and concepts / structured metadata

- all data in one database, micro-data, aggregates, historical data

- data manipulation / output facilities (StatLine, Eurostat etc.)

- flexible, reproducible and better accessibility data users

Knowledge base for expert groups (tools for analysis, production)

Integration frame

- optimal use of secondary sources

- quality

- coordinated

- less and smaller surveys

- quicker output

ESB

8/14Main advantages, business case (2):

ESB

Tool for analysis:

- longitudinal research

- timeseries

- follow big enterprises or a panel of enterprises

- consistency micro-data and corresponding aggregates

Documented

Basis for an output driven process

In line with organisational developments (hybrid organisation)

Reduced survey burden

Customer database

Efficient process (in potentie groot, lange en korte termijn)

- IT / methodology

- Organisational

Little experience with integration / very complex process of checking, editing, imputation and micro-integration

No coordinated backbones

Still limited use of administrative sources

No centralised meta-data systems

No real experience with consistent weighing of data-marts

Controle of data disclosure

No experience with new technologies like dataware houses

Is it possible to control the total process?

Already made investments in short term process improve- ments (input driven) / quick results

10/14

Bottlenecks:

A step-by-step approach

gaining insight

optimal situation as point on the horizon

using already existing improvement projects as the starting point

no cathedral building avoided.

Strategy for the short and medium term:

Strategy

one centralised BR for (the maintenance of ) all backbones / populations (coordination)

one contact centre for all input activities (coordination)

as less production lines as possible

as much standardisation and generic tools and solutions as possible

one output data warehouse for all business statistics

the optimal use of administrative sources at the cost of surveys

one centralised metadata – infrastructure

ESB-Basis

Data repository layer

Data manipulation layer

Publication layer (incl. statistical disclosure control

Pro

cess

met

a sy

stem

Met

asys

tem

Approach companiesMulti-channel

Inst

itu

tion

al

stat

isti

cs

(Im

pec

t)

Approach regis-tration holders

Bas

elin

e(s

ecu

nd

air)

Fu

nct

ion

al

stat

isti

cs

Input layer

Clean (micro)data (meta)

Clean (micro)data (meta)

Clean (micro)data (meta)

Customers

Determine Statistical needs

Information development

Enterprises

Registrations

BR

StatisticalBackbones

SBR

Integration layer

The (changing?) role of the BR

Determination and derivation of statistical backbones / populations

Sampling and weighting frame for all business statistics

Matching frame (e.g. micro-integration)

The bridge between administrative and statistical data

A benchmark

Source for economic demography

Information source

Mainly a sampling frame

Existence of decentralised BRs

No overall coordination

Processing mainly within SN

No units of functional statistics

No metadata and quality indic.

Basis economic demography

Survey burden

Less accessible

Crucial role in coordination / unam- bigious backbones / no decentralised BRs

Matching frame. Integration

Information to follow businesses over time (longitidinal / transversal)

Attention bigger businesses

Regional aspects

A bridge between adminstrative and statistical data

Processing also outside SN (SBR)

Units functional statistics included

Metadata and quality indicators

User friendly access

Basis economic demography

Survey burden

Old situation Desired situation

Thank you for your attention

Open questions:

Timeliness of updates of the BR

Inclusion of functional statistics

The connection between BR and CPR