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Tim scottkoenverheyenpresentation

The central information provision layer within Argenta is the name for the central data hub, based on near real time Data Vault, which on one hand answers the information needs of the bank but also feeds applications such as MIFID. This layer is also the base from which the data governance is enforced. For this purpose they use Oracle Enterprise Metadata Manager and Collibra.

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Data Hub for the financial market
Case: Argenta implements new data warehouse
Koen Verheyen and Tim Scott
17 October 2019
Agenda
1. Introduction and context
2. Programme approach
3. Architecture
4. Technical background
5. Closing + Q&A
Introduction and Context
3
5
Argenta
1,72 mio
customers
in Belgium and
the Netherlands
29,8 bn
€ in loans to families
and individuals in
Belgium and the
Netherlands
467
branch offices
in Belgium
2.575
collaborators in
Belgium,
Netherlands
and Luxemburg
Necessity for a data driven organisation has a triple origin
• Customer focus is key for Argenta
• Increasing amount of data to be processed
• Augmented reporting pressure from the various regulators
Programme launched to implement data management and a new data
warehouse (CIVL) using market standard methodology and technology
 Esperanto
6
Argenta context of the Esperanto programme
• Data management
• Fully in line with DAMA-DMBOK principles (11 principles)
• Introducing data stewardship in the business organisation as a central role
• New data warehouse
• DataVault 2.0
• Oracle technology as a platform
• Collibra and OEMM to support data lineage and data documentation
• Galvanize as centralised data quality tool
• Result: standardized and automated company wide data
management for both business as IT
• Single purpose: generate value for the organisation
7
Data management at Argenta

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Tim scottkoenverheyenpresentation

  • 1. Data Hub for the financial market Case: Argenta implements new data warehouse Koen Verheyen and Tim Scott 17 October 2019
  • 2. Agenda 1. Introduction and context 2. Programme approach 3. Architecture 4. Technical background 5. Closing + Q&A
  • 4. 5 Argenta 1,72 mio customers in Belgium and the Netherlands 29,8 bn € in loans to families and individuals in Belgium and the Netherlands 467 branch offices in Belgium 2.575 collaborators in Belgium, Netherlands and Luxemburg
  • 5. Necessity for a data driven organisation has a triple origin • Customer focus is key for Argenta • Increasing amount of data to be processed • Augmented reporting pressure from the various regulators Programme launched to implement data management and a new data warehouse (CIVL) using market standard methodology and technology  Esperanto 6 Argenta context of the Esperanto programme
  • 6. • Data management • Fully in line with DAMA-DMBOK principles (11 principles) • Introducing data stewardship in the business organisation as a central role • New data warehouse • DataVault 2.0 • Oracle technology as a platform • Collibra and OEMM to support data lineage and data documentation • Galvanize as centralised data quality tool • Result: standardized and automated company wide data management for both business as IT • Single purpose: generate value for the organisation 7 Data management at Argenta
  • 8. Esperanto 10 High-level operation CIVL Logical Info.Model Business Glossary B U S I T DAO Data stewards CC D&A CIVL + DWH R D V B D V P L DWH SAFe4.6 Through the data stewards’ operation • Logical information model • Business Glossary • Data definition • Data quality rules • Business Rules • Logical source mapping Other projects or programmes Reporting or data need Throught the IT Data & Analytics operation • DV modelling • PL modelling • Source connectivity • Model implementation + DQ rules • Testing • Release management S O U R C E S
  • 10. 12 Data management = Tools, processes and people
  • 11. Issue : Architectuur - As is 13 Output Boekhouding Model Wet en Regelgeving Model Commercieel Model Solvency II Rapportering Basle II Rapportering Overeenkomst Persoon Transactie Profiel Inzichten Management Rapportering Finance Model Wettelijke Rapportering Client beeld model Input Risk Monitoring Model Klanten Service Model Client Hoedanigheid Produkt Zekerheid Voorwerp Waardering Gebeurtenis Risico meting Model Interactie Klant Regel Gevers FMP WERA GDPR MIFID II METRO DIM BCBS 239 KYC
  • 13. Based on Data Integration Concepts 15 • Data Warehouse Automation • The Data Warehouse Institute defines data warehouse automation as "using technology to gain efficiencies and improve effectiveness in data warehousing processes. Data warehouse automation is much more than simply automating the development process. It encompasses all of the core processes of data warehousing including design, development, testing, deployment, operations, impact analysis, and change management.” • Data Warehouse Automation & Data Vault 2.0 • Perfect combination to deliver Business Value. • Faster Time to Market • Supports Agile Business • Higher Quality • Reduce Risk (Testing not required - proven logic) • Reduce Cost ( -40% based on existing customers)
  • 14. Based on Data Integration Concepts 16 • Raw Data Vault = IT-project • Integrate Source by Source (Technical Track) • Source Analysis required. • Only choose functional relevant tables. • Build integrated “Data Lake” of your structured Data. • Business Data Vault - Presentation Layer - Access Layer = Business-project • Based on Functional Requirements. • Business Data Vault • Information Management Office (Business) delivers conceptual models that get translated to DV 2.0 models • Bridges & Pits for Virtualisation of the Presentation Layer
  • 15. Technical background Extra slides to be confirmed 17