1. Challenges for ONS Modernisation
Dayantha Joshua

2. IM Vision (2001-2006) Diversity Scalability High Low Low High
Poor Vision
Uncertainty
Solutions for specific requirements
Too many options - difficult to fulfil new needs
Applications NT
Servers NT
Servers
Storage
Enterprise
Servers
Enterprise
Servers
Transition
High
Applications
Diversity
Network
Infrastructure
Infrastructure
Desktop
Low
Voice
Constraint
Growth
Inability to meet new demands
Coherent Strategy
Low
Scalability
High

3. ONS – what do we do ? ONS is actually 2 businesses
Responsible for a large amount of statistical outputs in England & Wales
Responsible for managing the registration of births, deaths, marriages, adoptions & civil partnerships
The modernisation programme
Started in 2004
Examining the business process on both businesses
With a view to achieving efficiencies
Increasing the quality of outputs
Increasing the number of outputs

4. G2C eBusiness – General Registrars Office

5. Drivers for Statistical Modernisation
Creation of ONS in 1996
Diverse range of statistical tools and computer systems
2,000 pieces of software
live application systems
150 application development languages and software packages
Different methods, processes and practices
Initial lack of integration and subsequent lack of investment
Led to:
increasing costs through duplication, licence costs etc
inefficiencies in production processes
increased risk to quality of statistics

6. Aims of ONS Modernisation Programme
Improve quality and timeliness of, and access to, key ONS statistical systems,
Creation of a more robust and comprehensive body of statistical evidence
Increase efficiency of producing statistics
SMP, a key element of ONS Modernisation:
Create a metadata driven, corporate database (CORD)
Transfer of all ONS statistical sources into CORD
Introduce standardisation of systems, processes and technology using a set of standard statistical tools
Re-engineer key statistical systems with shared use of metadata, methods and processes
Provide direct access to all official UK statistics via the NS web site

7. ONS of the Future Single repository holding all data
Systematised and reproducible processes – same results every time
World class website built on modernised data store
Modern technical environment
Easier/cheaper to maintain systems
Lower costs for new developments
Reduced staff training costs/greater staff flexibility

8. “C O R D” Metadata ADI Life Events National Accounts Business Surveys
Social
Surveys
Labour
Market
Prices/CSPI
Population
Estimates
CPI
Business Surveys Framework
Social Surveys Framework
IDBR
Admin Data
Portal
Collection &
Validation
Results
Processing
Collection &
Validation
Results
Processing
Dissemination
X12
GES
Canceis
Banff
Index No
Constr
Analytical
Tools
SAS
Superstar
Tau-Argus
GRI
ACTR
Metadata “C O R D”
Unit
Level
Data
Aggregate
Level
Data
Time
Series
Data
Longitudinal
Data

9. Benefits To ONS: Reduced risks Reduced processing costs
Improved quality
Increased value of outputs
Reduced development time and cost
To Users:
Datasets more robust and reliable
Reduced errors and inconsistencies
More timely outputs
Access to greater range of coherent statistics
More responsive ONS

10. What’s been achieved so far?
Oracle and Java as basis of new infrastructure
Selected and integrated set of standard statistical tools
Proved that Oracle, JAVA and the tools can work together as planned
Cardiff Bay new operational environment
Tools in place for web infrastructure (Tridion, Fast)
Totally refreshed LAN and WAN
New methodologies and business processes for key statistical systems such as NA and LFS
CORD being delivered incrementally, along with working functionality for NA, LFS, Life Events, etc

11. Initial approach Generic data repository Configurable systems
Several statistical tools operational
Extensive metadata expectations
Fully systematised processing
Broad span of ONS’s business
3 year delivery plan
Business transformation

12. New plans based on lessons learned
More incremental approach to delivery of vision
Extended delivery period
Focusing on delivering production quality systems that deliver specific business benefits,
At the same time contribute towards building infrastructure
Build on the work done to date on key systems (Labour Force Survey and National Accounts)
Business transformation

13. Current Challenges These include:
Business critical legacy hardware support and licence issues (particularly Ingres/ NUMA environment)
Security and resilience issues for Website
Urgent business continuity issues e.g. frail TDE system, secure file transfer
Some application systems high risk and need urgent attention e.g. population

14. The way forward Labour Force Survey processing National Accounts
weighting and micro data outputs
National Accounts
central systems
local systems
Vital Events (births, deaths, etc.)
data portal
Survey Case Management
work allocation
telephony
management information

15. Survey Case Management System (SCMS)
Automated allocation
About 65,000 cases taken from 10 survey types to be allocated every 6 weeks.
Approximately 800 Field Interviewers available.
SCMS combines business rules and spatial analysis to select the most suitable interviewer.
Field Managers use SCMS mapping interface to adjust automated allocation (if necessary).
Field Managers confirm allocations and the cases are distributed to the appropriate Interviewer.

16. SCMS - Automated Allocation Process
Mandatory Allocation Rules
(sequential evaluation to reduce
number of possible interviewers
for each case)
Spatial Analysis
(calculate distance between case and
Interviewer along road network)
Conditional Allocation Rules
(parallel evaluation to calculate
overall “severity weighting” for each
remaining candidate)
Allocated to Interviewer
(Selection of most suitable candidate)

17. HQ Review
The results of the automated allocation process are reviewed by HQ Staff.
If satisfactory, the allocation is released to Field Managers.
If unsatisfactory, the allocation parameters can be changed and the automated allocation process re-run.
Results from original allocation and re-run allocation can be compared
This can be repeated until satisfactory results are achieved

18. Release to Field Managers
Results compared and most suitable automated allocation released to Field Managers
Automated processes with capability for manual override where needed.

19. Mapping Interface Map Tools Interviewer Locations Case Locations
Spatial analysis tools used to display the location of cases selected for interview and the available interviewers.
Case
Locations

20. Mapping Interface - Zoomed view
Interviewer details can be queried
Cases can be selected and assigned to an interviewer
Map tools allow for cases to be displayed by allocation status i.e. whether an interviewer has been assigned, or by survey type so that the manager of the survey can monitor progress on their cases.
Cases or interviewers displayed on the map can be selected individually and more detailed information displayed.
Cases can be classified by allocation status or survey type

21. MIS reports to manage performanceb

22. Labour Force Survey Microdata collection
Weighting – reduce current extended elapse times and standardise through Generalized Estimation System (GES) weighting tool
Production of Microdata outputs

23. Metadata – variable definition

24. LFS Reweighting

25. National Accounts
Processing aggregated time series data to produce the core measures of the UK economy (e.g. GDP).
Structured classification of data (i.e. metadata)
Processing of data (import, calculation, copy, export).
Support for data analysis.

26. Metadata - classificationsb

27. Processing

28. Analysis

29. The pressures on the IT Organisation

30. IM Provision in ONS – A Pictorial View
Strategic Direction:
Driving technology leadership
centralised to ensure coherence across the organisation
Business Development:
Delivering change
Business Infrastructure:
Supporting infrastructure

31. Evolving picture for IM – dealing with reducing resources
Embedded in
Business
Outsourced
Registration
Statistical
Modernisation
ONS IM
Managed services
Corporate Systems
Modernisation
Data Center
Shared Services

32. Resource reductions so far…

33. High level view – Skills Transfer
In-house capability is increasing
Categories
Trained
Skilled
Fully Capable/Expert
Reliance on contract staff is reducing
ONS is becoming self-sufficient (but more to do)
Has attended training - is consolidating
Operating at an acceptable level
Operating at a very high standard

34. In-house skills Q1/05 – Q1/06 Q1 2005 Q1 2006
Skilled ONS staff were mainly in construction
No in-house designers
Q1 2006
Analysis and Construction staff reassigned/left ONS (circa 25)
Construction still predominant specialism
Slight growth in the other specialisms
Still no in-house designers

35. In-house v Contractor Skill Pool @ Q3/06
Construction
Majority in-house staff
Design
No in-house staff
IT Workpackage Managers
Testers
Majority contract staff
Transformation Platform Support
Function Heads
In-house staff
Architects