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16.
Landsbankinn
26
• Leading financial institution in Iceland
• 40% Market share Individual Banking
• 33% Market share Corporate Banking
• Best ESG risk ratings amongst European
banks (Sustainalytics 2021)
• Best bank at the Icelandic consumer
satisfaction ratings (Ánægjuvogin / Stjórnvísi 2021)

17.
SAS environment
Year Zero - Before Data Virtualization
27
▪ Too many query points
▪ Heterogenous technologies
▪ Complex source systems
▪ Scattered business rules
▪ Semantic layers in BI
▪ Business logics in DB views
▪ Many points of access control
▪ Audit points all over the place
▪ Each system has its own access control
KPI DB Source DBs New DWH Old DWH Markets DB
Views
BO
reporting
Self-service
BI
PDF
statements
MS Office
Integration
Views
Views
Views
General Reporting
KPI
Self-Service
data
Analytics
Reports
Analytics
Server
Risk Reporting
Monitoring / Audit Business security
Business rules
Board
Other DBs
SAP BO Semantic Layer
Data
Sources
Semantic
Layer

18.
Year 1 - The Logical Data Warehouse
▪ Unique point of query
▪ “Need data? LDW has the answer!”
▪ For reporting, analytics, APIs, …
▪ Unique point of truth
▪ Business logic repository
▪ Lineage available
▪ Unique point of access control
▪ Unified access to the data
▪ Unique point of auditing
KPI DB Source DBs New DWH Old DWH Markets DB
BO
reporting
Self-service
BI
MS Office
Integration
General Reporting
KPI Self-Service
data
Analytics
Reports
Analytics
Server
Risk Reporting
Board
Other DBs
Data
Sources
Logical Data Warehouse w/ Denodo
Monitoring / Audit Business security
Business rules
PDF
statements

19.
Years 2 and 3 - Expansion and Modernization
29
▪ Addition of data consumers
▪ Tableau
▪ REST / Restful APIs
▪ Addition of more data sources
▪ Where ETL is not required
▪ When history is provided in source
▪ Logical data pipelines
▪ Reduces the number of ETL jobs
▪ EDW gets data from LDW
BO
Reporting Tableau
RestWS to
Excel
General Reporting
KPI
Self-Service
data
Analytics
Reports
Analytics
Server
Risk Reporting
Board
Data
Sources
Logical Data Warehouse w/ Denodo
KPI DB
Source
DBs
New
DWH
Old
DWH
Markets
DB
Other
DBs
Flat files
Excel
SaaS
REST
SOAP
WWW
Customers
Domains
Operational
systems
Monitoring / Audit Business security
Business rules
Customer
statements

20.
Year 4 - A flawed model
30
▪ Source data is cryptic
▪ Data comes from software vendors
▪ Lots of meetings needed to establish the data mapping
▪ Domains know their source
▪ How to find data in the source
▪ When source changes
▪ Domains resort to creating views in the source
▪ Loss of lineage and governance
▪ What we wanted to get rid of in the first place
LDW
Source
DBs
Domains
Operational
systems
Views

21.
Year 4 - Implementing a Data Mesh model
31
▪ A simplified process
1. Domains provided with a development space
2. LDW developers combine views
3. Domains publish data
4. Operational systems access the data
▪ Top-down modelling
▪ Using interface views (data contracts)
Source
system
Base
Data Mesh
Domain A
developer
Business
systems
LDW
developer
LDW
Requests
(interface contracts)
Shares Combines
LDW
Source
DBs
Operational
systems
Domains
Domain B
developer
Requests
(interface contracts)
Publication
Data Mesh
Publishes
LDW

22.
Year 4 - Benefits of the Data Mesh w/ Denodo
32
▪ Delegate the ownership of data to the domains
▪ Data is in the hands of its creator
▪ Give better overview of the pipeline
▪ Views lifecycle managed by the source developer
▪ Reduce data pipelines
▪ Fewer ETL jobs when available
LDW
Source
DBs
Operational
systems
Domains
Savings
domain
Loans
domain
Cards
domain
Claims
domain
EDW
domain
LDW
developer
CRM Loan Online bank

23.
33
KPI DB
New
DWH
Markets
DB
Other
DBs
Flat files SaaS
REST
SOAP
WWW
Source
DBs
Customers Risk
Reporting
Business
Board
▪ Data Mesh conquers the bank
▪ Self-service for business
▪ Data API
Year 5 - What lies ahead
Logical Data Warehouse w/ Denodo
API
Self-
Service
Self-
Service
Self-
Service
Data
Mesh
Data
Mesh
Data
Mesh
Data
Mesh
Data
Mesh
Data
Mesh
Data
Mesh
Data
Mesh
Data
Mesh
Op.
Systems
Data
Mesh