A short presentation to the Defence IT 2012 conference to provide them with a view of how data quality underpins new regulations in Financial Services, e.g. Solvency II. Data quality has a raised profile in financial services due to mandates from the European Union that companies must demonstrate good understanding and management of data quality.

1. Data Quality Dimension of
European Union Regulation -
Solvency II
David Twaddell
AGENDA
- Regulatory Requirements
- Corporate Strategy
- Data Governance
- Defining Data Quality
- Data Quality Risk Assessment
- Data Quality Management solution
- Sample Dashboard

2.  Understand the quality of data used in the
calculation of technical provisions and in
the internal model (completeness,
accuracy and appropriateness - L2DIM
Solvency II
Art 14 and 220) Data Quality
Requirements
 Understand the quality of Solvency II data
using other indicators (Consistency,
Transparency, Credibility, Comparability,
Similarity – L2DIM Art various)
 Provide evidence of effective data
governance (L2DIM Art 246)
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3. Vision:
Global Insurer: • Business ownership of data
• >50k employees worldwide • Efficient data quality procedures along
• >$50B Gross Written Premium data lifecycle
• >1,000 disparate applications • Trusted DQ operational and
• Low confidence in data quality management information
• Global solution
• High confidence in data quality
Strategy: Data
• New Data Governance Framework
(+ clear Procedures and DQ standards +
Governance &
communication strategy)
• Central management of metadata
Quality
(including definition, lineage, ownership, etc.) Strategy
• New data quality controls, re-using existing controls
where possible.
• Powerful data tools
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4.  Overall management and control
 Responsibilities and reporting lines
Governance, Policies,
 Consistency across the enterprise Standards and Procedures
 Clear policies, standards and
procedures
 Education important
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(c) 2012 Inpreci – www.inpreci.com

5. Defining Data Quality #1 – for example
Completeness
2. Reconcile data received against data expected
3. Process to assess if data is available for all relevant model variables and risk
modules
Accuracy
6. Compare directly against the source (if available).
7. Check internal consistency and coherence of the received/output data against
expected properties of the data such as age-range, standard deviation, number
of outliers, and mean.
8. Compare with other data derived from the same source, or sources which are
correlated.
Appropriateness
11. Check consistency and reasonableness to identify outliers and gaps through
comparison against known trends, historic data and external independent
sources.
12. A definition and consistent application of the rules that govern the amount and
nature of data used in the internal model.
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6. Data Quality Risk Assessment
– where to put controls?
Define Materiality
Actuaries attach a materiality level to data terms within data sets, based on how the
data would affect the internal model, and define quantitative and qualitative
tolerances for data quality.
Document Lineage
Document the target business process and the data flow from source to internal
model for each dataset:
Check Data Controls
Identify existing control points that can be re-used. For each control point, document
actual controls (i.e. governance controls and data quality checks applied.). Link
controls to data quality indicators (completeness, accuracy, etc).
Risk Assessment
Documented procedure to assess risk along lineage. Assess effectiveness of
controls. May recommend additional control points and additional governance and
quality checks.
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7. Component/Building
Summary of capabilities
Block Name
~ Define business terms
~ Associate terms to Data Domain/Owner
Data Definitions ~ Associate terms to Source, Uses, Characteristics
~ Associate terms to DQ rules, weights and metrics
~ Describe business processes that relate to the flow
of data into SII.
Data flows ~ Describe points of data governance
~ Describe risk assessment of business processes, as
it relates to data quality
~ Stores data quality business rules
DQ Rule Repository ~ Stores data control technical rules
~ Extract, transform, load data
DQ Rules Engine ~ Apply data quality rules at appropriate points
~ Write out data quality measurements
Data Quality
~ Allow business people to log data governance activities
DQ Metrics Collector
~ Allow create/modify/delete/read of governance data
~ Provide only relevant 'questions' to specific people
Management
~ Maintain history of governance data
~ Qualitative and Quantitative assessments and metrics
Architecture
~ Define Key Quality Indicators
~ Relate quality rules/measurements to KQIs
DQ Aggregator/Scoring ~ Define data quality scoring methodology
~ Aggregate data quality measurements for reporting
~ Logical data models
Components
DQ Storage ~ Physical data models
~ Physical storage
~ Present KQI dashboard
DQ Dashboard and Reports ~ Drill-down to more detailed reports
(Delivery) ~ Slide/dice by agreed dimensions
~ Provide views for Operations, Governance, Stewardship
~ Record data defects, prioritise
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DQ Defect Manager ~ Track defect resolution
~ Interface with data quality measurements (c) 2012 Inpreci – www.inpreci.com

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(c) 2012 Inpreci – www.inpreci.com

9. - Data Risk Assessment
- Data Quality
- Data Security
- Data Governance
- Data Infrastructure
- Data Complexity
- Metadata
- Policy, Standards & Procedures
- Solutions