Enterprise Data Quality
Continuous delivery, performance e compliance
New control facilities and extremely short processing times, with an easy-to-use frontend
Monica Ripoldi
Head of Data Quality
Customer since
2019
Requirements
A multi-field, metadata driven, user oriented system
Enterprise Data Quality
A single Data Quality system to manage the needs of multiple offices, processes, and business functions
A multi-legal entity system
Management of inputs common to multiple areas
A multi-legal entity system
Management of inputs common to multiple areas
Continuous Delivery
Parallel configuration of multiple control streams
Incremental integration of input data
Quick and low-impact changes to data sets and controls to be performed
Incremental integration of input data
Quick and low-impact changes to data sets and controls to be performed
Regulatory Compliance
Availability of dashboards for analyzing input data, processes, and outcomes
Versioning/auditing of inputs, controls, and outcomes
Centralized dictionary of controls
Versioning/auditing of inputs, controls, and outcomes
Centralized dictionary of controls
Challenges
Continuous delivery, performance e compliance
Areas
Registry
IFRS9
Liquidity
Interest Rate Risk
IFRS9
Liquidity
Interest Rate Risk
Time to Solution
Go live in 3 months for each area
Driver
130 input files
632 business checks, of which approximately
140 are performance checks on rolling data
4600 automatically configured technical checks
15 billion technical checks performed
250 GB of data processed monthly
632 business checks, of which approximately
140 are performance checks on rolling data
4600 automatically configured technical checks
15 billion technical checks performed
250 GB of data processed monthly
Solution
A system that meets Data Governance policy guidelines
Capabilities
Some of the Irion EDM features used:
- Metadata Driven Datastore Definition
- Metadata Driven Data Validation
- Rule User Lab
- Automatic Rule Documentation
Results obtained
- A virtuous cycle of continuous improvement and relationships between those who produce, consume, govern, and manage data
- Comprehensive and dynamic view of data quality, linked to Dataflow
- Proper balance and coverage of controls exercised at different levels by different actors
- Culture of measurement and analysis, both retrospective and proactive
- Lower risks, greater awareness, increased compliance
- Very short timeframes (three months) for putting controls into production for each area
- A strong but restrained presence
- Huge and diverse data landscape
- Priority-based and cost-benefit approach
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