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TRANSCRIPT
3 APRIL 2019
DATA IMPROVEMENT PLANS –CABINET OFFICE CASE STUDY
VIRGINIA BURKE
SHAUN BIGG
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SYNOPSIS
ATTENDEES WILL HEAR:
Why data quality is
important and what
schemes should be doing
What should
be included in a
Data Improvement Plan
How the Cabinet Office
and ITM are engaging
with employers to cleanse
data
About ITM
• Leading independent data, systems and administration services provider
• Strong track record in delivering major projects for the UK’s largest private and public sector schemes, TPAs, tPR, PPF & leading insurance companies
• Public sector work includes Police, Fire, LGPS, Civil Service, Teachers as well as private sector schemes
Data Quality and Administration Support
• GMP reconciliation / rectification
• Data quality scoring / reporting
• Data Improvement Plans
• Data Cleanse
• Payroll / Administration System reconciliation
• Backlogs
• Migration support
ITM’s Services (LGPS, Police, Fire)
Data Quality
Compliance
• The Pensions Regulator
– Greater scrutiny
• Scheme Annual Report
– Common Data Score
– Scheme Specific Data Score
General
• Member experience
• Annual Benefit Statements
– Only 32% issued on time for Firefighters*
– 54% for Police*
– 45% for LGPS*
– Requirement is 100%
• Administration efficiency
• Dashboard
• Administration Team morale
• Retrospective scheme changes
Why is data a hot topic?
* Source LGA. Percentage is proportion of schemes where all active members received ABS by statutory deadline
Data should be:
• PRESENT
(e.g. is Date of Leaving recorded for deferred and pensioner members?)
• CONSISTENT
(e.g. is Date of Leaving after date joined scheme?)
• ACCURATE
(e.g. is the DOL recorded that date the member actually left service? Check sample leaver notifications to validate)
See LGA guide to data scoring
What does good data look like?
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DATA MANAGEMENT: CASE STUDY
Typical approach
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LGPS DATA AUDIT
✚ Data objectives agreed
✚ Relevant data sources identified (admin system, payroll system, spreadsheets, files, etc.)
✚ Run initial tests, review, run final tests
✚ Data Quality Audit Report
✚ Data Improvement Plan agreed
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Compliance (tPR & Record Keeping Regs)
Accurate valuation
High quality service standards
Ability to calculate and pay accurate benefits
Improve member communication, e.g. online access
Cost efficiency
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EXAMPLES
OBJECTIVES
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Facilitate change in admin system or Administrator
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DATA QUALITY AUDIT REPORT
Common and
scheme specific data
scores
Quality of data to
meet objectives
Recommended data
cleanse activities
Risk assessment
1010
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WHAT NEXT?
If scores are less than 100% Scheme Manager should put in place a Data Improvement Plan
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–DATA
IMPROVEMENT PLAN
Objectives, measurable outcomes,
timescales and how they will be met Scope / Prioritisation Resourcing
Ongoing data improvement Timeframes and timelines
Governance and reporting Dependencies
EXAMPLE DATA IMPROVEMENT PLAN ACTIVITIES
Member tracing(mortality and addresses)
Pensioner payroll to administration system
reconciliation
Review data processing framework
Data cleanse Obtaining data from employer
Cabinet Office case study
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DATA MANAGEMENT: CASE STUDY
DATA IMPROVEMENT AND CLEANSE PROJECT
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CABINET OFFICE CASE STUDY
✚ An NAO report in 2016 identified high volume of issues with the quality and accuracy of Civil Service Pension Scheme (CSPS) active member data.
✚ A baseline count of 3.7m Data Validation Failures were initially identified across 340 active employers within the scheme.
✚ CSPS, the Administrator and ITM worked together on a data improvement plan which reduced the 3.7m to 1.2.m through a series of bulk data fixes.
2016 NAO report
3.7m data issues found
2017Data Improvement Plan commences
2.5m data issues resolved
2018 Precise Portal launched
Employer cleanse commences
2019 Phase 2 cleanse to start – May
Phase 1 cleanse to complete – Oct3.2 m data issues resolved
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✚ Stopping the issues at source is as important as cleansing the issues that exist in the legacy data.
✚ CSPS and the Scheme Administrator implemented a new Interface process which streamlined the data capture and set tough targets for Employer compliance.
✚ Take up and compliance rates are hugely positive with only a handful of Employers remaining on the old interface process.
✚ Those on the new process are already submitting interfaces with less than 1% error rates and resolving those errors with in the four week interface cycles.
✚ Highly evident that those Employers remaining on the old interface process see a continual month on month increase in Data Validation Failures.
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CABINET OFFICE CASE STUDY
DATA IMPROVEMENT PLAN
DATA MANAGEMENT: CASE STUDY
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✚ ITM and Cabinet Office launched an online portal in October 2017 and began engaging with contributing Employers immediately.
✚ 18 months later over 200 Employers are live on the portal and using it to cleanse their data on a daily basis.
✚ Member data is presented to the users along with the ask (the failure) and they are guided to answer (the cleanse) within the system. Fixes are interfaced back to the Scheme Administrator once a month via a robust, controlled and auditable process.
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CABINET OFFICE CASE STUDY
EMPLOYER LED CLEANSE – PHASE 1
Total DVFs Cleansed
Total DVFs CreatedRemaining DVFs (end of Feb 19)
Average cleansed per month Average per user per month
0.837m
1.455m0.561m
62,000 138
The best user has cleansed over 15,000 DVFs, that’s nearly 1,000 per month
DATA MANAGEMENT: CASE STUDY
