Elaborating on the Business Architecture of SN

1. Elaborating on the Business Architecture of SN Standard Process Steps Robbert Renssen rrnn@cbs.nl Statistics Netherlands

2. Present situation • IAF • The Business Architecture of SN • Relation with GSBPM • New Ideas • Why • Outline Outline of the Presentation

3. Security Governance Information Systems IAF Business Information Technical Infrastructure Vision & Strategy Principles Standards Trends & developments Contextual Functionality & Usability Conceptual Business concepts Information needs Technical facilities Logical Organisational Structure & Procedures Communication patterns Application Components Hardware &System Software Components Physical Operations InformationStorage and Exchange BespokeSoftware & Settings Products & Tools Standard Process Steps

4. Business Architecture of SN Design Chain Management Statistics Production Central Storage of Steady States Business Architecture SN

5. Relation GSBPM GSBPM 1 till 3 Not covered GSBPM 4 till 7 Central Storage of Steady States Not covered Business Architecture SN

6. Internal users of selections of steady states External customer External supplier New Ideas Data collection Data processing Data dissemination Internal suppliers of steady states Pre-input base Post- output base Input base Micro base Stat base Output base Standard Process Steps

7. Why new ideas about data processing • Top-down (general) • Courses on Design and Implementation of Surveys, see e.g. Willeboordse • Generic Statistical Business Process Model (GSBPM) • Bottom-up (specific) • Process descriptions in view Business analysis, Business architecuture, VIR • Gap between top-down and botton-up • Top-down  driven by statistical methodology that underlies a process • Bottom-up  driven by (physical) activities that are needed to carry out a process • Something is missing: Ideas about standard process steps try to complete the picture Standard Process Steps

8. Outline of the ideas Making an operational set D Steady state D Measured Property: Operational Variables Real World Property: Conceptual Variables Standard Process Steps

9. Making an Operational Set under Closer Scrutiny(Example) Real World Property Measured Property Steady state A Primary data collection A Estimating Matching Steady state C Steady state D Secundary data collection B Steady state B Standard Process Steps

10. Closer Scrutiny; Matching, what is going on? • Two datasets concerning the same population • Having common unique identifyable variables • Are put together (matched) on the criterium of identical ‘keys’. • In reality, two datasets that are to be matched • do not concern (exactly) the same population • do not have (exactly) common unique identifyable variables • have missing data and measurement errors in these (vitually) common unique identifyable variables Standard Process Steps

11. We Need Some Preparation • To obtain the same population, we need to Derive units, Delineate populations, … • To obtain common unique identifyable variables, we need to Derive variables, Code variables, … • To handle missing data or errors in the identifyable variables, we need value checks, … • Like Matching, the preparing steps Derive units, Derive Variables, Code Variables, are elementary (statistical) functions • As a combination, they work together for matching purposes Standard Process Steps

12. Matching Reconsidered • Our idea distinguishes between • An (elementary) statistical function such as matching that can only be applied when the pre-conditions with respect to design of the inputs are fulfilled • A statistical function flow, e.g. on behalf of matching, that consists of a flow of statistical functions and decision rules • Characteristics of a statistical function: • Based on a (statistical) method or subject matter knowledge • Managed by so-called method rules, i.e. specifications of statistical methods or subject matter knowledge • Characteristics of a statistical function flow: • Consists of one or more statistical functions and decision rules • On behalf of a statistical target (reason of designing this flow) Standard Process Steps

13. Statistical Targets: Why Matching Data Sets • To enrich a dataset with additional variables • To observe and estimate relations (e.g. cross tabulations) • As a preperation step to delineate populations by means of the additional variables • As a preperation step to improve data (editing) with additional auxiliary information • As a preperation step to estimate population totals with additional auxiliary information • … • To measure under- or overcoverage wrt the instance of a population • To correct for under- or overcovarage wrt the instance of a population Standard Process Steps

14. Completing the picture: an impression Statistical Strategy Set of Conceptual Attribute Variables Statistical Target Design of Statistical Function Flow Statistical Function real-world property (statistical methode) Statistical Methods Statistical Function data quality (statistical method) Standard Decision Function Non-Statistical Function Design of Process Flow Planning Set of Physical Attribute Variables Design of Statistical Process Standard Process Steps

15. Thank You Standard Process Steps