The Adoption of METIS GSBPM in Statistics Denmark Agenda 1. 2. 3. 4. 5. 6. Background and context Working with business processes An example of documentation Results of process analysis Metadata coverage Lessons.
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The Adoption of METIS GSBPM in Statistics Denmark Agenda 1. 2. 3. 4. 5. 6. Background and context Working with business processes An example of documentation Results of process analysis Metadata coverage Lessons learned Agenda 1. 2. 3. 4. 5. 6. Background and context Working with business processes An example of documentation Results of process analysis Metadata coverage Lessons learned Working group on standardisation 1. Multi-annual corporate strategy as basis (”Strategy 2015”) 2. Working group, that refers to Board of Directors 3. METIS GSBPM adopted as common frame 4. Dual focus • • Process analysis and documentation Coverage of metadata systems Quality management / Metadata Management 1 Specify Needs 2 Design 3 Build 4 Collect 5 Process 1.1 Determine need for information 2.1 Design outputs 3.1 Build data collection instrument 4.1 Select sample 5.1 Integrate data 6.1 Prepare draft outputs 7.1 Update output systems 8.1 Define archive rules 9.1 Gather evaluation inputs 1.2 Consult & confirm need 2.2 Design variable descriptions 3.2 Build or enhance process comp. 4.2 Set up collection 5.2 Classify & code 6.2 Validate outputs 7.2 Produce dissemination products 8.2 Manage archive repository 9.2 Conduct evaluation 1.3 Establish output objectives 2.3 Design data collection methodology 3.3 Configure workflows 4.3 Run collection 5.3 Validate & edit 6.3 Scrutinize & explain 7.3 Manage release of dissem. prod. 8.3 Preserve data & associated metadata 9.3 Agree action plan 1.4 Identify concepts & variables 2.4 Design Frame & sample methodology 3.4 Test production systems 4.4 Finalize collection 5.4 Impute 6.4 Apply disclosure control 7.4 Promote dissemination products 8.4 Dispose of data & assoc. metadata 1.5 Check data availability 2.5 Design stat. processing methodology 3.5 Test statistical business process 5.5 Derive new variables & stat. units 1.6 Prepare business case 2.6 Design prod. systems / workflows 3.6 Finalize production system 5.6 Calculate weights 5.7 Calculate aggregates 5.8 Finalize data files 6 Analyse 6.5 Finalize outputs 7 Disseminate 7.5 Manage user support 8 Archive 9 Evaluate Reference document – ”SD’s METIS” – METIS: confirmed standard for official statistical production – Adopted by some of our peers – Translation of document – Approach for SD version – Testing the extent to which the model apply to SD – An ”SD METIS” would be a milestone for business process- and architectural maturity – Necessary to move ahead according to our corporate objective of increasing standardisation – Initial focus on phases 4-7 Agenda 1. 2. 3. 4. 5. 6. Background and context Working with business processes An example of documentation Results of process analysis Metadata coverage Lessons learned Model/template for statistical business processes – METIS level (“which phases do we open”?) – Control-flow level (phases, input, output, time) – Functional level (”who does what, and in what order?”) – ”AS-IS” and/or ”TO-BE” – BPMN: Standardized notation – Collect ideas and convert them into action (standardisation, efficiency and quality) – Form • Workshop • Facilitated by working group • Ownership of results to the statistical team • Needs a mandate! Selection of pilot cases • Social Statistics: – Population register – Student register (register updates) • Business Statistics – – – – – – – • General account statistics (SBS) Employment in construction industries Retail Trade Index Industrial commodity statistic Farm Structure Survey Car register and associated statistics Use of ICT in enterprises Economic Statistics – Consumer price index – Foreign trade in services • Sales and Marketing – Interview task: Yearly survey on safety – Key figures in housing (standardized product from SDs Customer Services Centre) • User Services – Data collection-processes/-systems (XIS, CEMOS) Selection of cases in Business Statistics Dimension Values Cases Frequency - Short term vs. - Structural statistics - ECS - SBS Standardised system (if any) - Statistics in standardised systems vs. - Statistics in stand-alone systems - ECS - SBS Complexity - Simple vs. - Complex - RTI - SBS Type of Statistical Unit - Statistics based on SBR vs. - Statistics with other units - SBS - C-Reg Method for error detection - Micro-based error detection vs. - Macro-based error detection - SBS - ECS Coverage - Sample vs. - Cut-off vs. - Population - ECS - ICS - FSS Confidentiality scheme - Positive confidentiality vs. - Negative confidentiality - SBS - ICS Cost - Statistics with high cost vs. - Statistics with low cost - SBS - RTI Stability - Few changes by each iteration vs. - Many changes by each iteration - ECS - UIE Maturity - Well established statistic in SD - New statistic in SD - SBS - (RII) ”Type” - Primary statistic vs. - Derived statistic - ICS - C-Reg Agenda 1. 2. 3. 4. 5. 6. Background and context Working with business processes An example of documentation Results of process analysis Metadata coverage Lessons learned Example: METIS level Kvalitetsstyring / Håndtering af metadata 1 Behov 2 Design 3 Udvikl 4 Indsaml 5 Behandl 6 Analysér 7 Formidl 8 Arkivér 9 Evaluér 1.1 Identificér brugerbehov 2.1 Design output 3.1 Udvikl dataindsamlingsinstrument 4.1 Udvælg stikprøve 5.1 Integrer data 6.1 Forbered statistikprodukt 7.1 Opdatér data i formidlingssystemer 8.1 Definér Arkiveringsregler 9.1 Indsaml data / input til evalueringen 1.2 Konsultér og bekræft behov 2.2 Beskriv variable 3.2 Udvikl produktionssystem 4.2 Forbered dataindsamling 5.2 Kod data 6.2 Kvalitetssikr Statistikprodukt 7.2 Udarbejd statistikprodukt 8.2 Opsaml / gem rådata 9.2 Gennemfør evaluering 1.3 Skitsér output/tabeller 2.3 Design dataindsamlingsmetode 3.3 Definér workflows 4.3 Gennemfør dataindsamling 5.3 Gennemgå, fejlsøg og ret data 6.3 Gransk og forklar 7.3 Håndtér udgivelsen 8.3 Gem fejlsøgte data og metadata 9.3 Beslut handlingsplan 1.4 Identificér Begreber 2.4 Design udtræksramm e og stikprøvemetode 3.4 Test systemet 4.4 Afslut dataindsamling 5.4 Imputér manglende data 6.4 Applicér statistisk fortrolighed 7.4 Markedsfør statistikprodukt 8.4 Aflevér data og metadata 1.5 Undersøg datakilder 2.5 Design databehandlingsmetode 3.5 Gennemfør pilot-test 5.5 Afled nye stat. enheder og variable 6.5 Afslut analyse 7.5 Håndtér brugersupport 1.6 Start projekt 2.6 Design prod. system; kravspecifikation 3.6 Sæt system i drift 5.6 Beregn vægte 5.7 Beregn aggregater 5.8 Færdiggør aggregerede datasæt Example: Control flow level Trigger Phases Input • Regulations • Data • etc. Output • Intermediate • Final Time Example: Functional level Who does what Start condition End condition Note that… Agenda 1. 2. 3. 4. 5. 6. Background and context Working with business processes An example of documentation Results of process analysis Metadata coverage Lessons learned Results of process analysis (an overview) • Focus on processes is useful and has immediate effect in some cases • Improvements for statistical teams – Quality (documentation, new quality measures, etc.) – Standardisation (Use of standardised systems) – Efficiency (Eliminate manual processes) • Improvements in communication – Many project managers regarding digitalisation – Coordinator function • Improvements in efficiency for data collection – Focus on areas of responsibility • Huge difference in degree of standardisation – Dissemination – Data collection – Data processing Agenda 1. 2. 3. 4. 5. 6. Background and context Working with business processes An example of documentation Results of process analysis Metadata coverage Lessons learned Metadata coverage 4. Collect 5. Process 6. Analyse Integrate Data FTP PUK PX Publ Classify and code Virk.dk Statistics bank XIS2 Business-toBusiness Review, validate and edit Statistical database IBS Impute Papir forms Dst.dk Statistical database 1 CEMOS Web-services 7. Disseminate Statistical database n CRM Scan Data Warehouse IDV Calulate weigths and aggregate Metadata og documentation Begrebsdatabase ? ? ? Times Times Klassifikationer Varedeklarationer Højkvalitet Metadata coverage • Dissemination phase is very well covered • Although dissemination phase is covered by four different applications the overlap is very limited • The vision for the future is to create a single metadata system • The data model should be based on three data stages (raw data, micro data, macro data) Metadata coverage 4. Collect 5. Process 6. Analyse Integrate Data (std) FTP 7. Disseminate PUK Dst.dk Statistical database 1 PX Publ Classify and code (std) Virk.dk Datacollection system Inputdata archive Review, validate and edit (std) Web-services Bussiness-tobusiness Statistics bank Statistical database Impute (std) Statistical database n CRM Paper forms Calulate weigths and aggregate (std) Metadata og documentation Statistics Denmark Metadatasystem Data Warehouse IDV Agenda 1. 2. 3. 4. 5. 6. Background and context Working with business processes An example of documentation Results of process analysis Metadata coverage Lessons learned Lessons learned • Planning a strategy for further development is better using GSBPM • Identify areas of interest for improvement initiatives. • Major challenges regarding steps where data is processed • Further standardization of methods is necessary • A clearer view of the different need for metadata and documentation • A better overview of the strong and the weak areas of our metadata applications