business model transformation strategy (bmts) john pearson and tracey savage statistics nz’s

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

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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

People Process Methods Software

6

BmTS Approach

People Process Methods Software

Process

Methods

Software

People

Time

7

Business Process Model (BPM)

Need Build Collect Process AnalyseDesign Disseminate

8

Business Process Model

Need Build Collect Process AnalyseDesign Disseminate

Establishpopulation

Generatesample

ValidateAnd Q.A.

Maintainsample

Identify sample

Manageproviders

Setupcollection

RunCollection

Loaddata

9

Business Process Model

Establishpopulation

Generatesample

ValidateAnd Q.A.

Maintainsample

Identify sample

Manageproviders

Setupcollection

RunCollection

Loaddata

Need Build Collect Process AnalyseDesign Disseminate

BDSS Business Process

Ed

itor

An

aly

stS

up

erv

iso

rS

yste

m

Create Base Version

Extract Data &Apply

Derivations,imputations

Produce report

Manual EditingMake changes to existing data and add new records

No

Set Status of Dataset

Yes

Create Analytical Version

Analyse Dataset

Generate Analytical Report

Create Final Version

Yes

Generate Final Dataset

No

Delete Dataset

Generate Report

Create Output

Re-runDerivations,imputations

Set Base Version as Clean Version

Generate Report

Reset StatusTo Base

No

Yes

Set Status to Base

Generate Report

Assess Quality of Dataset

Discard Dataset

YesSet StatusTo Discard

Generate Report

Generate Output Files

Set Final to Published

Generate Report

Set Final to Published

Generate Report

Establishpopulation

Generatesample

ValidateAnd Q.A.

Maintainsample

Identify sample

Manageproviders

Setupcollection

RunCollection

Loaddata

Need Build Collect Process AnalyseDesign Disseminate

10

Business Process Model

Establishpopulation

Generatesample

ValidateAnd Q.A.

Maintainsample

Identify sample

Manageproviders

Setupcollection

RunCollection

Loaddata

Need Build Collect Process AnalyseDesign Disseminate

11

Business Process Model

Build Collect ProcessDesign

CORPORATE

STATISTICAL

MANAGE

Current generic BPM (gBPM)

Methodology

Need Analyse Disseminate

12

Business Process Model

Need Design Collect Process AnalyseBuild 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

generic E&I

Processes

E&I Training

New E&Imethods

Standard E&I tools

E&I Plans

E&I Standards

E&I Strategy

& Principles

Editing & Imputation

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 ComponentsProcess Analyse Disseminate10. Dashboard / WorkflowNeed

Build

Design

2. Output Data Store

CleanData Data

1. Input Data Store

RawData

RADL

Web

Ou

tpu

t C

ha

nn

els

Mu

lti-Mo

da

l Co

llec

tion

CU

RFS

INFO

S

E-Form

CAI

Imaging

Admin.

Data

Off

icia

l Sta

tistic

s S

yste

m &

D

ata

Arc

hiv

e

SummaryData

‘UR’Data

2. Output Data Envt.1. Input Data Environment

9. Reference Data Stores

7. Respondent Management 8. Customer Management

RA

DL

Web

Ou

tpu

t C

ha

nn

els

Mu

lti-Mo

da

l Co

llec

tion

CU

RFS

INFO

S

E-F

ormC

AI

Imaging

Adm

in.D

ataO

ffic

ial S

tatis

tics

Sys

tem

&

CleanData

AggregateData

RawData

SummaryData

‘UR’Data

Da

ta A

rch

ive

3. Metadata StoreStatistical

Process

Knowledge Base

3. Metadata EnvironmentStatistical

Process

Knowledge Base

4. Analytical Environment

5. Information Portal

6. Transformations

23

BmTS Components - Progress10. Dashboard / Workflow

2. Output Data Store

CleanData

AggregateData

1. Input Data Store

RawData

RADL

Web

Ou

tpu

t C

ha

nn

els

Mu

lti-Mo

da

l Co

llec

tion

CU

RFS

INFO

S

E-Form

CAI

Imaging

Admin.

Data

Off

icia

l Sta

tistic

s S

yste

m &

D

ata

Arc

hiv

e

SummaryData

‘UR’Data

2. Output Data Envt.1. Input Data Environment

9. Reference Data Stores

7. Respondent Management 8. Customer Management

RA

DL

Web

Ou

tpu

t C

ha

nn

els

Mu

lti-Mo

da

l Co

llec

tion

CU

RFS

INFO

S

E-F

ormC

AI

Imaging

Adm

in.D

ataO

ffic

ial S

tatis

tics

Sys

tem

&

CleanData

AggregateData

RawData

SummaryData

‘UR’Data

Da

ta A

rch

ive

3. Metadata StoreStatistical

Process

Knowledge Base

3. Metadata EnvironmentStatistical

Process

Knowledge Base

4. Analytical Environment

5. Information Portal

6. Transformations

AdminData

T’form CustomerCRM

SystemMetadata

Link toAnalytics

Link toPortal

URT’form

CategoryEI

AggregateArea

CleanArea

BF

SASBI

GraphicalAnalysis

TSAnalysis

ConfidT’formOutput

cubes

RMportal

CRM

T’formlibrary Logi+

D/board workflow

IDCImaging

Qstage

Datalab

Statisphere

Tablebuilder

BANFF

CallMgmt

MSExcel

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