Business model Transformation Strategy (BmTS) John Pearson and Tracey Savage Statistics NZ’s

2 Overview Introduction to the BmTS 3 key themes: 1.People, process, methods…then software 2.Advances in Stats NZ methodology 3.Evolutionary change - business, cultural & programme Questions

3 BmTS Objectives Better service Operational excellence Attractive workplace

4 BmTS Deliverables 1.Standard processes 80/20 2.Disciplined approach Data and metadata 3.Enterprise-wide technical architecture

5 BmTS Approach PeopleProcessMethodsSoftware

6 BmTS Approach PeopleProcessMethodsSoftware Process Methods Software People Time

7 Business Process Model (BPM) Need BuildCollectProcessAnalyse Design Disseminate

8 Business Process Model Need BuildCollectProcessAnalyse Design Disseminate Establish population Generate sample Validate And Q.A. Maintain sample Identify sample Manage providers Setup collection Run Collection Load data

9 Business Process Model Establish population Generate sample Validate And Q.A. Maintain sample Identify sample Manage providers Setup collection Run Collection Load data Need BuildCollectProcessAnalyse Design Disseminate Establish population Generate sample Validate And Q.A. Maintain sample Identify sample Manage providers Setup collection Run Collection Load data Need BuildCollectProcessAnalyse Design Disseminate

10 Business Process Model Establish population Generate sample Validate And Q.A. Maintain sample Identify sample Manage providers Setup collection Run Collection Load data Need BuildCollectProcessAnalyse Design Disseminate

11 Business Process Model BuildCollectProcessDesign CORPORATE STATISTICAL MANAGE Current generic BPM (gBPM) Methodology NeedAnalyseDisseminate

12 Business Process Model Need DesignCollectProcess Analyse Build Disseminate CORPORATE STATISTICAL MANAGE Future gBPM Methodology

13 Process - Progress & successes gBPM - for all collections - developed, agreed and used Detailed business processes - documented for – Collect, Analyse, Disseminate – Administrative data, data integration, & feasibility projects

14 Methods – Establishment surveys Advances in: longitudinal Business Frame size measures on our Business Frame modelled tax data for the "small" strata regular reselection record linkage methodology research into sample rotation p% rule for confidentialisation of tables

15 Methods – Case study Editing & Imputation E&I Strategy & Principles E&I Standards E&I Plans Standard E&I tools New E&I methods E&I Training generic E&I Processes

16 Methods - Progress & successes Standard methods – being developed and/or documented Standard tools – examples: –BANFF (Statistics Canada) for editing and imputation –INTERP (in-house) for benchmarking and interpolation –QualityStage (IBM) for data integration –GREGWT (ABS) for integrated weighting – X12-ARIMA (US Census Bureau) / SADJ (in- house)

17 Software Our current system… Survey Processing Template

18 Software evolution BmTS builds on SProceT foundations metadata driven systems common look and feel re-use of 'best practice' availability of management information dynamic nature of views interactive processing fully integrated desktop processing

19 Software evolution BmTS: The next generation not a template that is iteratively improved; not in Lotus Notes wider scope - end-to-end; used by all Statistics NZ collections generic & standard business processes, methods, tools workflows, centralised data & metadata; service oriented architecture (SOA)

20 Collect Future Software - BmTS Components ProcessAnalyse Disseminate 10. Dashboard / Workflow Need Build Design 2. Output Data Store Clean Data 1. Input Data Store Raw Data RADL Web Output Channels Multi - Modal Collection CURFS INFOS E - Form CAI Imaging Admin.Data Official Statistics System & Data Archive Summary Data ‘UR’ Data 2. Output Data Envt. 1. Input Data Environment 9. Reference Data Stores 7. Respondent Management8. Customer Management RADL Web Output Channels Multi - Modal Collection CURFS INFOS E - Form CAI Imaging Admin. Data Official Statistics System & Clean Data Aggregate Data Raw Data Summary Data ‘UR’ Data Data Archive 3. Metadata Store Statistical Process Knowledge Base 3. Metadata Environment Statistical Process Knowledge Base 4. Analytical Environment 5. Information Portal 6. Transformations

21 BmTS Components - Dashboard

22 BmTS Components - Workflow

23 BmTS Components - Progress 10. Dashboard / Workflow 2. Output Data Store Clean Data Aggregate Data 1. Input Data Store Raw Data RADL Web Output Channels Multi - Modal Collection CURFS INFOS E - Form CAI Imaging Admin.Data Official Statistics System & Data Archive Summary Data ‘UR’ Data 2. Output Data Envt. 1. Input Data Environment 9. Reference Data Stores 7. Respondent Management8. Customer Management RADL Web Output Channels Multi - Modal Collection CURFS INFOS E - Form CAI Imaging Admin. Data Official Statistics System & Clean Data Aggregate Data Raw Data Summary Data ‘UR’ Data Data Archive 3. Metadata Store Statistical Process Knowledge Base 3. Metadata Environment Statistical Process Knowledge Base 4. Analytical Environment 5. Information Portal 6. Transformations Admin Data T’form Customer CRM System Metadata Link to Analytics Link to Portal UR T’form Category EI Aggregate Area Clean Area BF SAS BI Graphical Analysis TS Analysis Confid T’form Output cubes RM portal CRM T’form library Logi+ D/boardworkflow IDC Imaging Q stage Datalab Statis phere Table builder BANFF Call Mgmt MS Excel

24 Software – Progress & successes Strategy & Broad Logical Design for 7/10 BmTS components Proof of concept / prototype solutions for: – National Accounts / time series data – dissemination products – unit record data: collect to clean Standardised collection phase in production Fact table approach utilised for all data Reuse of components is happening User Interface guide developed and utilised Service oriented architecture in place

25 Changes and challenges Cultural change required business processes as the driver (not ICT) focus on commonalities between business areas support for and use of standards culture of analysis

26 Changes and challenges Ownership - of processes, methods, and tools / software Monitoring progress Clarifying future statistical architecture Impact on data quality Determining the impact on specific outputs

27 Lessons learned People > process > method >… systems –Collection areas focus on their differences –Compromise: development vs BAU –Long-term gain has short-term cost Evolutionary transformation: –many minor successes and failures Do not expect to get it 100% right the first time

28 Contacts BmTS Metadata BPM