1. The role of the Business Register in a changing environment at Statistics Netherlands Beijing, 2004, session 2 Nico Heerschap

2. o Old / current situation - changing environment o Ideal situation o Strategy for the short and medium term o The role of the Business Register 25 min. 2/14 Content:

3. Organisation of SN: o Business statistics (BES) o Social statistics (SRS) o Macro economic statistics (MSP) o Technology and facilities (TNF) o Two distant locations

4. Types of statistics in the Division of Business Statistics: o Production (mainly for NA) o Short term (mainly turnover) o Investments o International trade o Thematical Energy, Technology, Environment, Health, Agriculture, Transport, Crime, Culture, Tourism o Business Register / Baseline

5. ESB Input Throughput Output Statistic 1 Statistic 2Statistic 3Statistic x    Product view BR CR3 CR2 CR1 CRx Old situation:

6. Changing environment: o Changing needs of customers: more integrated, coherent and quicker. New theme’s emerge o Growing competition in the market place o Pressure to reduce the survey burden on enterprises o Smaller budget: pressure to be more efficient: less staff but same or more output o New developments in IT and methodology ESB

7. Disadvantages current situation: o No co-ordination between statistics / separate worlds o No integration of the data overall (quality and consistency) o Sometimes different figures for the same phenomenon o Overlapping customer bases o Same data suppliers approached by different statistics o Little documentation of processes / hardly any mobility o 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

8. Main goals of SN: 5/14 o Strengthen the relationship with the customer: integrated, consistent, quicker, flexibility, one window o New position in the market place: integrating crossroad on the information highway, knowlegde institute (networks) o Reducing the survey burden by: - Optimising the use of secondary sources - Approaching the respondent in its own environment o More efficiency by redesigning the processes and applying new IT and methodology o Adapt the organisational structure, culture and skills (7S model of McKinsey)

9. 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

10. ENTERPRISESENTERPRISES burden Unanswered Survey needs Old situation CUSTOMERSCUSTOMERS A B burden Unanswered Survey needs Desired situation ENTERPRISESENTERPRISES CUSTOMERSCUSTOMERS A B

11. Input Throughput Output Theme 1 Theme 2Theme 3Theme x One window for data dissemination services One window for data-collection services Merge ESB

12. Throughput Output Input One window for all data-collection All input, primary and secondary CBR BACKBONESBACKBONES Making of data- marts (selection, aggregation etc.) Internal analists Information development Coupling data to the backbone(s) Transfer data to Data warehouse Transaction al d base Data repository Checking, editing and micro-inte- gration One window for all output services Customers / data-users Output for customer L L L WORKFLOWMANAGEMENTWORKFLOWMANAGEMENT ESB External SBR Output driven process Data production factory Knowledge institute - (integrated) publication - information development - customer base METADATASYSTEMMETADATASYSTEM

13. (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 sources Administra- tive sources Dimensions of the data repository:

14. 8/14 Main advantages, business case (1): o 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 o Knowledge base for expert groups (tools for analysis, production) o Integration frame - optimal use of secondary sources - quality - coordinated - less and smaller surveys - quicker output ESB

15. 8/14 Main advantages, business case (2): ESB o Tool for analysis: - longitudinal research - timeseries - follow big enterprises or a panel of enterprises - consistency micro-data and corresponding aggregates o Documented o Basis for an output driven process o In line with organisational developments (hybrid organisation) o Reduced survey burden o Customer database o Efficient process (in potentie groot, lange en korte termijn) - IT / methodology - Organisational

16. o Little experience with integration / very complex process of checking, editing, imputation and micro-integration o No coordinated backbones o Still limited use of administrative sources o No centralised meta-data systems o No real experience with consistent weighing of data-marts o Controle of data disclosure o No experience with new technologies like dataware houses o Is it possible to control the total process? o Already made investments in short term process improve- ments (input driven) / quick results 10/14 Bottlenecks:

17. o 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: o 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

18. ESB-Basis Data repository layer Data manipulation layer Publication layer(incl. statistical disclosure control Process meta system Metasystem Approach companiesMulti-channel Institutional statistics (Impect) Approach regis-tration holders Baseline (secundair) Functional statistics Input layer Clean (micro)data (meta) Clean (micro)data (meta) Clean (micro)data (meta) Customers DetermineStatistical needs Informationdevelopment Enterprises Registrations BRBR StatisticalBackbones SBR Integration layer

19. The (changing?) role of the BR o Determination and derivation of statistical backbones / populations o Sampling and weighting frame for all business statistics o Matching frame (e.g. micro-integration) o The bridge between administrative and statistical data o A benchmark o Source for economic demography o Information source

20. o Mainly a sampling frame o Existence of decentralised BRs o No overall coordination o Processing mainly within SN o No units of functional statistics o No metadata and quality indic. o Basis economic demography o Survey burden o Less accessible o Crucial role in coordination / unam- bigious backbones / no decentralised BRs o Matching frame. Integration o Information to follow businesses over time (longitidinal / transversal) o Attention bigger businesses o Regional aspects o A bridge between adminstrative and statistical data o Processing also outside SN (SBR) o Units functional statistics included o Metadata and quality indicators o User friendly access o Basis economic demography o Survey burden Old situation Desired situation

21. Thank you for your attention Open questions: o Timeliness of updates of the BR o Inclusion of functional statistics o The connection between BR and CPR