Beijing, 2004, session 2
Nico Heerschap
The role of the
Business Register
in a changing environment
at Statistics Netherlands
2/14
Content:

Old / current situation - changing environment

Ideal situation

Strategy for the short and medium term

The role of the Business Register
25 min.
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
Product view
Old situation:



Output
CR1
CRx
CR2
CR3
Throughput
Input
Statistic 1 Statistic 2 Statistic 3 Statistic x
BR
   
ESB
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
4/14
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
Conclusion:
The situation within SN is not in line anymore
with a changing environment
ESB
5/14
Main goals of SN:

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)
ES
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
Old situation
E
N
T
E
R
P
R
I
S
E
S
E
N
T
E
R
P
R
I
S
E
S
A
Survey
burden
B
Unanswered
needs
Desired situation
Survey
burden
A
B
Unanswered
needs
C
U
S
T
O
M
E
R
S
C
U
S
T
O
M
E
R
S

  

One window for data dissemination services
Merge
Output
Merge
Throughput
Merge
Input
Merge
Theme 1
Theme 2
Theme 3
Theme x
One window for data-collection services
    
ESB
Customers / data-users
Internal analists
One window for all output services
Output
L
Information
development
Transfer data to
Data warehouse
L
Checking, editing
and micro-integration
Throughput
Input
L
Output driven
process
Output for
customer
Making of datamarts (selection,
aggregation etc.)
Coupling data
to the backbone(s)
Knowledge institute
- (integrated) publication
- information development
- customer base
W
O
R
K
F
L
O
W
M
A
N
A
G
E
M
E
N
T
B
A
MData warehouse
C
E
K
T
B
A
O
Transactional dbase N
D
E
A
S
T
A
Data
repository
Data production
factory
S
Y
S
T
E
M
CBR
External
SBR
One window for all data-collection
All input, primary and secondary
   
ESB
7/14
Dimensions of the data repository:
(3) TIME
(2) VARIABLES
Administrative
sources
(1)
B
A
C
K
B
O
N
E
S
(BR)
Surveydata
Surveydata
Surveydata
Administrative sources
Administrative sources
8/14
Main 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
8/14
Main advantages, business case (2):
 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
ESB
10/14
Bottlenecks:
 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 improvements (input driven) / quick results
Strategy for the short and medium term:
 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
 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
Customers
ESB-Basis
Publication layer
(incl. statistical disclosure control
Data manipulation layer
Integration layer
Statistical
Backbones
Input layer
Approach registration holders
SBR
Registrations
Functional
statistics
Clean (micro) Clean (micro)
data (meta)
data (meta)
Institutional
statistics
(Impect)
(secundair)
Baseline
BR
Clean (micro)
data (meta)
Approach companies
Multi-channel
Enterprises
Metasystem
Data repository layer
Process meta system
Information
development
Determine
Statistical needs
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
Old situation
 Mainly a sampling frame
 Existence of decentralised BRs
 No overall coordination
 Processing mainly within SN
Desired situation
 Crucial role in coordination / unambigious backbones / no decentralised
BRs
 Matching frame. Integration
 No units of functional statistics
 Information to follow businesses over
time (longitidinal / transversal)
 No metadata and quality indic.
 Attention bigger businesses
 Basis economic demography
 Regional aspects
 Survey burden
 A bridge between adminstrative and
statistical data
 Less accessible
 Processing also outside SN (SBR)
 Units functional statistics included
 Metadata and quality indicators
 User friendly access
 Basis economic demography
 Survey burden
Open questions:
 Timeliness of updates of the BR
 Inclusion of functional statistics
 The connection between BR and CPR
Thank you for your attention