Beijing 2004 session 2 Nico Heerschap The role

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

2/14 Content: 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.

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

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

Product view Old situation: Output CR 1 CRx CR 2 CR 3 Throughput Input Statistic 1 Statistic 2 Statistic 3 Statistic x BR ESB

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

4/14 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 Conclusion: The situation within SN is not in line anymore with a changing environment ESB

5/14 Main goals of SN: 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 (7 S model of Mc. Kinsey) 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 A Survey burden B Unanswered needs Desired situation Survey burden A B Unanswered needs 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 S Y S T E M CBR Data production factory 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 (1) B A C K B O N E S (BR) Administrative sources Surveydata Administrative sources

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 (Stat. Line, 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

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

10/14 Bottlenecks: 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 improvements (input driven) / quick results

Strategy for the short and medium term: o A step-by-step approach Ø gaining insight Ø optimal situation as point on the horizon Ø using already existing improvement projects as the starting point Ø no cathedral building avoided. o Strategy Ø one centralised BR for (the maintenance of ) all backbones / populations (coordination) Ø one contact centre for all input activities (coordination) Ø as less production lines as possible Ø as much standardisation and generic tools and solutions as possible Ø one output data warehouse for all business statistics Ø the optimal use of administrative sources at the cost of surveys Ø 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) 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 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

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

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