SlideShare une entreprise Scribd logo
1  sur  12
Télécharger pour lire hors ligne
I N T E R N
I N T E R N
From Traditional Data Warehouse to
Holistic Data Integration and Faster
Data Delivery
6.5.2022
Dagfinn Røed, SpareBank 1 Forsikring
1
I N T E R N
I N T E R N
Agenda
2
Introduction
Rapid business changes and new requirements
The challenges of establishing a new data platform
The benefits with the new data platform, including anti-money laundry use case
Summary
I N T E R N
I N T E R N
Introduction
3
• Part of the SpareBank 1 Alliance, the second largest finance group in Norway
• The third largest supplier of pension products in Norway
• Approximately 130 employees
• Originally life and non-life insurance company, but re-established in 2019 as a pension only
company
SpareBank 1 Forsikring
• More than 30 years experience in DataWarehousing, analytics and data plattforms
• Various positions as data architect, advisor/consultant, project manager, department manager
and product owner/systems owner
• 11 years in SpareBank 1 Forsikring
Dagfinn Røed
I N T E R N
I N T E R N
Business
changes
New
requirements
Keeping up with rapid business
changes and new requirements
Data
Warehouse
development
199
0
2000 201
0
2020
I N T E R N
I N T E R N
2018 - from data warehouse to an agile data
platform
5
• We had a vision about a new data
platform
- Cloud-based
- Flexible
- Scaleable
- Faster data delivery
- More data self-service
- Wide range of data sources and
data types
- Supporting the AI/ML, but also
taking care of all the existing
reporting
• We started looking into Data
Virualization as a key technology
I N T E R N
I N T E R N
2019 – merge and demerge
6
Fremtind Forsikring
• New Insurance Company – Fremtind
• Merge of the non-life and life insurance businesses in
SpareBank 1 and DNB (the largest bank in Norway)
• Continue to use the existing data and analytics
solutions from SpareBank 1 Forsikring
+
Sparebank 1 Forsikring
• Pure pension company
• Still dependent on Fremtind to perform all
compliance and mandatory reporting
• Will (as soon as possible) need a completely new
data and analytics solution
I N T E R N
I N T E R N
The upside – we could now build a data
platform for the future
7
• A platform for all types of data to be shared in
the company
• Flexibility to add new data fast when they are
needed
• Make it easy for the users to find the data
they need
• The data platform can offer both processed
data and raw data
• Facilitates data self-service
• Personal information is secure and compliant
with GDPR
• The platform is open and can support both
analytical and other use-cases
We decided to build the platform on-premise using MS SQL Server, Denodo Data Virutalization and Tableau
I N T E R N
I N T E R N
The reality kicks-in
8
• The users were less advanced compared to the super users in the
old company
- They showed resistance to switching to a new end-user tool
- They wanted the data exactly in the same format they were used to
They did not want raw data or smaller «building blocks». They just wanted big
flat tables.
- They had no knowledge about topics like AI/ML
• The development team
- We did not have experiences with Denodo, primarily traditional
ETL-development
- We had to keep historical data many years back
- We were in a hurry – it was urgent to stop using the existing (shared) analytical
solutions
Most of the time and resources the first two years were spent on building a new, «hybrid» Data Warehouse inside the
data platform incapsulated by Denodo Data Virtualization.
I N T E R N
I N T E R N
First Data Platform benefit – anti-money
laundry
9
The benefits of the data platform become clear when developing new solutions such as the
anti-money laundering solution.
Customer input:
• PEP information
• Real-right holders
• Identification
External data
• PEP information
• Real-right
holders
• Nationality
I N T E R N
I N T E R N
New insurance product - EPK
• Accessing data-hub in real time
• Monitoring pension fund movements
• Creating and distributing win-back data
• Market leader for the EPK-product
Faster replacement of core system
• Integrating data from new core system only
virtually
• Keep existing data interface for consumers
• Less coding, faster development
• Reducing physical Data Warehouse
10
After providing the business with the “bread and
butter” they need, we see many gains from using the
data platform going forward
11
• Business changes happens rapidly. You need an agile
and flexible platform
• Understand your users and their needs
• Make sure you have all the competence you need on
the new tools
• Spend time on the information and solution
architecture
• Focus on virtual development. Only build the
ETL-factory where it is absolutely needed
• Your platform must support all the legacy reporting,
not only the fun and exciting new stuff
• Make realistic plans and deliver frequent business
benefits
• Data virtualization is an extremely useful technology
and makes all integration tasks much easier
Summary
Thank you!
Dagfinn Røed
dagfinn.roed@sparebank1.no
1
2

Contenu connexe

Similaire à From Traditional Data Warehouse to Holistic Data Integration and Faster Data Delivery

Novell Strategy Update June 2013
Novell Strategy Update June 2013Novell Strategy Update June 2013
Novell Strategy Update June 2013Werner Luetkemeier
 
Accelerate Cloud Migrations and Architecture with Data Virtualization
Accelerate Cloud Migrations and Architecture with Data VirtualizationAccelerate Cloud Migrations and Architecture with Data Virtualization
Accelerate Cloud Migrations and Architecture with Data VirtualizationDenodo
 
P& G case study analysis
P& G case study analysis P& G case study analysis
P& G case study analysis r-dilara
 
Accelerating Self-Service Analytics with Denodo and Tableau (Singapore)
Accelerating Self-Service Analytics with Denodo and Tableau (Singapore)Accelerating Self-Service Analytics with Denodo and Tableau (Singapore)
Accelerating Self-Service Analytics with Denodo and Tableau (Singapore)Denodo
 
OneNeck Private Equity Solutions
OneNeck Private Equity SolutionsOneNeck Private Equity Solutions
OneNeck Private Equity SolutionsScott Fitzgerald
 
¿En qué se parece el Gobierno del Dato a un parque de atracciones?
¿En qué se parece el Gobierno del Dato a un parque de atracciones?¿En qué se parece el Gobierno del Dato a un parque de atracciones?
¿En qué se parece el Gobierno del Dato a un parque de atracciones?Denodo
 
Accelerate Self-service Analytics with Universal Semantic Model
Accelerate Self-service Analytics with Universal Semantic Model Accelerate Self-service Analytics with Universal Semantic Model
Accelerate Self-service Analytics with Universal Semantic Model Denodo
 
Hadoop Twelve Predictions for 2012
Hadoop Twelve Predictions for 2012Hadoop Twelve Predictions for 2012
Hadoop Twelve Predictions for 2012Cloudera, Inc.
 
GDPR Noncompliance: Avoid the Risk with Data Virtualization
GDPR Noncompliance: Avoid the Risk with Data VirtualizationGDPR Noncompliance: Avoid the Risk with Data Virtualization
GDPR Noncompliance: Avoid the Risk with Data VirtualizationDenodo
 
O2’s Financial Data Hub: going beyond IFRS compliance to support digital tran...
O2’s Financial Data Hub: going beyond IFRS compliance to support digital tran...O2’s Financial Data Hub: going beyond IFRS compliance to support digital tran...
O2’s Financial Data Hub: going beyond IFRS compliance to support digital tran...DataWorks Summit
 
Re-orienting your business around data
Re-orienting your business around dataRe-orienting your business around data
Re-orienting your business around dataDani Solà Lagares
 
ICMIF 5-5-5 launch: Tieto presentation
ICMIF 5-5-5 launch: Tieto presentationICMIF 5-5-5 launch: Tieto presentation
ICMIF 5-5-5 launch: Tieto presentationICMIF Microinsurance
 
Big Data Everywhere Chicago: Platfora - Practices for Customer Analytics on H...
Big Data Everywhere Chicago: Platfora - Practices for Customer Analytics on H...Big Data Everywhere Chicago: Platfora - Practices for Customer Analytics on H...
Big Data Everywhere Chicago: Platfora - Practices for Customer Analytics on H...BigDataEverywhere
 
EDF2014: Nicolas Lemcke Horst, Ambassador of the Danish Basic Data Programme,...
EDF2014: Nicolas Lemcke Horst, Ambassador of the Danish Basic Data Programme,...EDF2014: Nicolas Lemcke Horst, Ambassador of the Danish Basic Data Programme,...
EDF2014: Nicolas Lemcke Horst, Ambassador of the Danish Basic Data Programme,...European Data Forum
 
Use Cases from Batch to Streaming, MapReduce to Spark, Mainframe to Cloud: To...
Use Cases from Batch to Streaming, MapReduce to Spark, Mainframe to Cloud: To...Use Cases from Batch to Streaming, MapReduce to Spark, Mainframe to Cloud: To...
Use Cases from Batch to Streaming, MapReduce to Spark, Mainframe to Cloud: To...Precisely
 
Postgres Vision 2018: Making Modern an Old Legacy System
Postgres Vision 2018: Making Modern an Old Legacy SystemPostgres Vision 2018: Making Modern an Old Legacy System
Postgres Vision 2018: Making Modern an Old Legacy SystemEDB
 
Hyperion Close Up - Hoe zet je hyperion optimaal in?
Hyperion Close Up - Hoe zet je hyperion optimaal in?Hyperion Close Up - Hoe zet je hyperion optimaal in?
Hyperion Close Up - Hoe zet je hyperion optimaal in?Finext
 
Datenvirtualisierung: Wie Sie Ihre Datenarchitektur agiler machen (German)
Datenvirtualisierung: Wie Sie Ihre Datenarchitektur agiler machen (German)Datenvirtualisierung: Wie Sie Ihre Datenarchitektur agiler machen (German)
Datenvirtualisierung: Wie Sie Ihre Datenarchitektur agiler machen (German)Denodo
 
How First to Value Beats First to Market: Case Studies of Fast Data Success
How First to Value Beats First to Market: Case Studies of Fast Data SuccessHow First to Value Beats First to Market: Case Studies of Fast Data Success
How First to Value Beats First to Market: Case Studies of Fast Data SuccessVoltDB
 
Foundational Strategies for Trust in Big Data Part 1: Getting Data to the Pla...
Foundational Strategies for Trust in Big Data Part 1: Getting Data to the Pla...Foundational Strategies for Trust in Big Data Part 1: Getting Data to the Pla...
Foundational Strategies for Trust in Big Data Part 1: Getting Data to the Pla...Precisely
 

Similaire à From Traditional Data Warehouse to Holistic Data Integration and Faster Data Delivery (20)

Novell Strategy Update June 2013
Novell Strategy Update June 2013Novell Strategy Update June 2013
Novell Strategy Update June 2013
 
Accelerate Cloud Migrations and Architecture with Data Virtualization
Accelerate Cloud Migrations and Architecture with Data VirtualizationAccelerate Cloud Migrations and Architecture with Data Virtualization
Accelerate Cloud Migrations and Architecture with Data Virtualization
 
P& G case study analysis
P& G case study analysis P& G case study analysis
P& G case study analysis
 
Accelerating Self-Service Analytics with Denodo and Tableau (Singapore)
Accelerating Self-Service Analytics with Denodo and Tableau (Singapore)Accelerating Self-Service Analytics with Denodo and Tableau (Singapore)
Accelerating Self-Service Analytics with Denodo and Tableau (Singapore)
 
OneNeck Private Equity Solutions
OneNeck Private Equity SolutionsOneNeck Private Equity Solutions
OneNeck Private Equity Solutions
 
¿En qué se parece el Gobierno del Dato a un parque de atracciones?
¿En qué se parece el Gobierno del Dato a un parque de atracciones?¿En qué se parece el Gobierno del Dato a un parque de atracciones?
¿En qué se parece el Gobierno del Dato a un parque de atracciones?
 
Accelerate Self-service Analytics with Universal Semantic Model
Accelerate Self-service Analytics with Universal Semantic Model Accelerate Self-service Analytics with Universal Semantic Model
Accelerate Self-service Analytics with Universal Semantic Model
 
Hadoop Twelve Predictions for 2012
Hadoop Twelve Predictions for 2012Hadoop Twelve Predictions for 2012
Hadoop Twelve Predictions for 2012
 
GDPR Noncompliance: Avoid the Risk with Data Virtualization
GDPR Noncompliance: Avoid the Risk with Data VirtualizationGDPR Noncompliance: Avoid the Risk with Data Virtualization
GDPR Noncompliance: Avoid the Risk with Data Virtualization
 
O2’s Financial Data Hub: going beyond IFRS compliance to support digital tran...
O2’s Financial Data Hub: going beyond IFRS compliance to support digital tran...O2’s Financial Data Hub: going beyond IFRS compliance to support digital tran...
O2’s Financial Data Hub: going beyond IFRS compliance to support digital tran...
 
Re-orienting your business around data
Re-orienting your business around dataRe-orienting your business around data
Re-orienting your business around data
 
ICMIF 5-5-5 launch: Tieto presentation
ICMIF 5-5-5 launch: Tieto presentationICMIF 5-5-5 launch: Tieto presentation
ICMIF 5-5-5 launch: Tieto presentation
 
Big Data Everywhere Chicago: Platfora - Practices for Customer Analytics on H...
Big Data Everywhere Chicago: Platfora - Practices for Customer Analytics on H...Big Data Everywhere Chicago: Platfora - Practices for Customer Analytics on H...
Big Data Everywhere Chicago: Platfora - Practices for Customer Analytics on H...
 
EDF2014: Nicolas Lemcke Horst, Ambassador of the Danish Basic Data Programme,...
EDF2014: Nicolas Lemcke Horst, Ambassador of the Danish Basic Data Programme,...EDF2014: Nicolas Lemcke Horst, Ambassador of the Danish Basic Data Programme,...
EDF2014: Nicolas Lemcke Horst, Ambassador of the Danish Basic Data Programme,...
 
Use Cases from Batch to Streaming, MapReduce to Spark, Mainframe to Cloud: To...
Use Cases from Batch to Streaming, MapReduce to Spark, Mainframe to Cloud: To...Use Cases from Batch to Streaming, MapReduce to Spark, Mainframe to Cloud: To...
Use Cases from Batch to Streaming, MapReduce to Spark, Mainframe to Cloud: To...
 
Postgres Vision 2018: Making Modern an Old Legacy System
Postgres Vision 2018: Making Modern an Old Legacy SystemPostgres Vision 2018: Making Modern an Old Legacy System
Postgres Vision 2018: Making Modern an Old Legacy System
 
Hyperion Close Up - Hoe zet je hyperion optimaal in?
Hyperion Close Up - Hoe zet je hyperion optimaal in?Hyperion Close Up - Hoe zet je hyperion optimaal in?
Hyperion Close Up - Hoe zet je hyperion optimaal in?
 
Datenvirtualisierung: Wie Sie Ihre Datenarchitektur agiler machen (German)
Datenvirtualisierung: Wie Sie Ihre Datenarchitektur agiler machen (German)Datenvirtualisierung: Wie Sie Ihre Datenarchitektur agiler machen (German)
Datenvirtualisierung: Wie Sie Ihre Datenarchitektur agiler machen (German)
 
How First to Value Beats First to Market: Case Studies of Fast Data Success
How First to Value Beats First to Market: Case Studies of Fast Data SuccessHow First to Value Beats First to Market: Case Studies of Fast Data Success
How First to Value Beats First to Market: Case Studies of Fast Data Success
 
Foundational Strategies for Trust in Big Data Part 1: Getting Data to the Pla...
Foundational Strategies for Trust in Big Data Part 1: Getting Data to the Pla...Foundational Strategies for Trust in Big Data Part 1: Getting Data to the Pla...
Foundational Strategies for Trust in Big Data Part 1: Getting Data to the Pla...
 

Plus de Denodo

Enterprise Monitoring and Auditing in Denodo
Enterprise Monitoring and Auditing in DenodoEnterprise Monitoring and Auditing in Denodo
Enterprise Monitoring and Auditing in DenodoDenodo
 
Lunch and Learn ANZ: Mastering Cloud Data Cost Control: A FinOps Approach
Lunch and Learn ANZ: Mastering Cloud Data Cost Control: A FinOps ApproachLunch and Learn ANZ: Mastering Cloud Data Cost Control: A FinOps Approach
Lunch and Learn ANZ: Mastering Cloud Data Cost Control: A FinOps ApproachDenodo
 
Achieving Self-Service Analytics with a Governed Data Services Layer
Achieving Self-Service Analytics with a Governed Data Services LayerAchieving Self-Service Analytics with a Governed Data Services Layer
Achieving Self-Service Analytics with a Governed Data Services LayerDenodo
 
What you need to know about Generative AI and Data Management?
What you need to know about Generative AI and Data Management?What you need to know about Generative AI and Data Management?
What you need to know about Generative AI and Data Management?Denodo
 
Mastering Data Compliance in a Dynamic Business Landscape
Mastering Data Compliance in a Dynamic Business LandscapeMastering Data Compliance in a Dynamic Business Landscape
Mastering Data Compliance in a Dynamic Business LandscapeDenodo
 
Denodo Partner Connect: Business Value Demo with Denodo Demo Lite
Denodo Partner Connect: Business Value Demo with Denodo Demo LiteDenodo Partner Connect: Business Value Demo with Denodo Demo Lite
Denodo Partner Connect: Business Value Demo with Denodo Demo LiteDenodo
 
Expert Panel: Overcoming Challenges with Distributed Data to Maximize Busines...
Expert Panel: Overcoming Challenges with Distributed Data to Maximize Busines...Expert Panel: Overcoming Challenges with Distributed Data to Maximize Busines...
Expert Panel: Overcoming Challenges with Distributed Data to Maximize Busines...Denodo
 
Drive Data Privacy Regulatory Compliance
Drive Data Privacy Regulatory ComplianceDrive Data Privacy Regulatory Compliance
Drive Data Privacy Regulatory ComplianceDenodo
 
Знакомство с виртуализацией данных для профессионалов в области данных
Знакомство с виртуализацией данных для профессионалов в области данныхЗнакомство с виртуализацией данных для профессионалов в области данных
Знакомство с виртуализацией данных для профессионалов в области данныхDenodo
 
Data Democratization: A Secret Sauce to Say Goodbye to Data Fragmentation
Data Democratization: A Secret Sauce to Say Goodbye to Data FragmentationData Democratization: A Secret Sauce to Say Goodbye to Data Fragmentation
Data Democratization: A Secret Sauce to Say Goodbye to Data FragmentationDenodo
 
Denodo Partner Connect - Technical Webinar - Ask Me Anything
Denodo Partner Connect - Technical Webinar - Ask Me AnythingDenodo Partner Connect - Technical Webinar - Ask Me Anything
Denodo Partner Connect - Technical Webinar - Ask Me AnythingDenodo
 
Lunch and Learn ANZ: Key Takeaways for 2023!
Lunch and Learn ANZ: Key Takeaways for 2023!Lunch and Learn ANZ: Key Takeaways for 2023!
Lunch and Learn ANZ: Key Takeaways for 2023!Denodo
 
It’s a Wrap! 2023 – A Groundbreaking Year for AI and The Way Forward
It’s a Wrap! 2023 – A Groundbreaking Year for AI and The Way ForwardIt’s a Wrap! 2023 – A Groundbreaking Year for AI and The Way Forward
It’s a Wrap! 2023 – A Groundbreaking Year for AI and The Way ForwardDenodo
 
Quels sont les facteurs-clés de succès pour appliquer au mieux le RGPD à votr...
Quels sont les facteurs-clés de succès pour appliquer au mieux le RGPD à votr...Quels sont les facteurs-clés de succès pour appliquer au mieux le RGPD à votr...
Quels sont les facteurs-clés de succès pour appliquer au mieux le RGPD à votr...Denodo
 
Lunch and Learn ANZ: Achieving Self-Service Analytics with a Governed Data Se...
Lunch and Learn ANZ: Achieving Self-Service Analytics with a Governed Data Se...Lunch and Learn ANZ: Achieving Self-Service Analytics with a Governed Data Se...
Lunch and Learn ANZ: Achieving Self-Service Analytics with a Governed Data Se...Denodo
 
How to Build Your Data Marketplace with Data Virtualization?
How to Build Your Data Marketplace with Data Virtualization?How to Build Your Data Marketplace with Data Virtualization?
How to Build Your Data Marketplace with Data Virtualization?Denodo
 
Webinar #2 - Transforming Challenges into Opportunities for Credit Unions
Webinar #2 - Transforming Challenges into Opportunities for Credit UnionsWebinar #2 - Transforming Challenges into Opportunities for Credit Unions
Webinar #2 - Transforming Challenges into Opportunities for Credit UnionsDenodo
 
Enabling Data Catalog users with advanced usability
Enabling Data Catalog users with advanced usabilityEnabling Data Catalog users with advanced usability
Enabling Data Catalog users with advanced usabilityDenodo
 
Denodo Partner Connect: Technical Webinar - Architect Associate Certification...
Denodo Partner Connect: Technical Webinar - Architect Associate Certification...Denodo Partner Connect: Technical Webinar - Architect Associate Certification...
Denodo Partner Connect: Technical Webinar - Architect Associate Certification...Denodo
 
GenAI y el futuro de la gestión de datos: mitos y realidades
GenAI y el futuro de la gestión de datos: mitos y realidadesGenAI y el futuro de la gestión de datos: mitos y realidades
GenAI y el futuro de la gestión de datos: mitos y realidadesDenodo
 

Plus de Denodo (20)

Enterprise Monitoring and Auditing in Denodo
Enterprise Monitoring and Auditing in DenodoEnterprise Monitoring and Auditing in Denodo
Enterprise Monitoring and Auditing in Denodo
 
Lunch and Learn ANZ: Mastering Cloud Data Cost Control: A FinOps Approach
Lunch and Learn ANZ: Mastering Cloud Data Cost Control: A FinOps ApproachLunch and Learn ANZ: Mastering Cloud Data Cost Control: A FinOps Approach
Lunch and Learn ANZ: Mastering Cloud Data Cost Control: A FinOps Approach
 
Achieving Self-Service Analytics with a Governed Data Services Layer
Achieving Self-Service Analytics with a Governed Data Services LayerAchieving Self-Service Analytics with a Governed Data Services Layer
Achieving Self-Service Analytics with a Governed Data Services Layer
 
What you need to know about Generative AI and Data Management?
What you need to know about Generative AI and Data Management?What you need to know about Generative AI and Data Management?
What you need to know about Generative AI and Data Management?
 
Mastering Data Compliance in a Dynamic Business Landscape
Mastering Data Compliance in a Dynamic Business LandscapeMastering Data Compliance in a Dynamic Business Landscape
Mastering Data Compliance in a Dynamic Business Landscape
 
Denodo Partner Connect: Business Value Demo with Denodo Demo Lite
Denodo Partner Connect: Business Value Demo with Denodo Demo LiteDenodo Partner Connect: Business Value Demo with Denodo Demo Lite
Denodo Partner Connect: Business Value Demo with Denodo Demo Lite
 
Expert Panel: Overcoming Challenges with Distributed Data to Maximize Busines...
Expert Panel: Overcoming Challenges with Distributed Data to Maximize Busines...Expert Panel: Overcoming Challenges with Distributed Data to Maximize Busines...
Expert Panel: Overcoming Challenges with Distributed Data to Maximize Busines...
 
Drive Data Privacy Regulatory Compliance
Drive Data Privacy Regulatory ComplianceDrive Data Privacy Regulatory Compliance
Drive Data Privacy Regulatory Compliance
 
Знакомство с виртуализацией данных для профессионалов в области данных
Знакомство с виртуализацией данных для профессионалов в области данныхЗнакомство с виртуализацией данных для профессионалов в области данных
Знакомство с виртуализацией данных для профессионалов в области данных
 
Data Democratization: A Secret Sauce to Say Goodbye to Data Fragmentation
Data Democratization: A Secret Sauce to Say Goodbye to Data FragmentationData Democratization: A Secret Sauce to Say Goodbye to Data Fragmentation
Data Democratization: A Secret Sauce to Say Goodbye to Data Fragmentation
 
Denodo Partner Connect - Technical Webinar - Ask Me Anything
Denodo Partner Connect - Technical Webinar - Ask Me AnythingDenodo Partner Connect - Technical Webinar - Ask Me Anything
Denodo Partner Connect - Technical Webinar - Ask Me Anything
 
Lunch and Learn ANZ: Key Takeaways for 2023!
Lunch and Learn ANZ: Key Takeaways for 2023!Lunch and Learn ANZ: Key Takeaways for 2023!
Lunch and Learn ANZ: Key Takeaways for 2023!
 
It’s a Wrap! 2023 – A Groundbreaking Year for AI and The Way Forward
It’s a Wrap! 2023 – A Groundbreaking Year for AI and The Way ForwardIt’s a Wrap! 2023 – A Groundbreaking Year for AI and The Way Forward
It’s a Wrap! 2023 – A Groundbreaking Year for AI and The Way Forward
 
Quels sont les facteurs-clés de succès pour appliquer au mieux le RGPD à votr...
Quels sont les facteurs-clés de succès pour appliquer au mieux le RGPD à votr...Quels sont les facteurs-clés de succès pour appliquer au mieux le RGPD à votr...
Quels sont les facteurs-clés de succès pour appliquer au mieux le RGPD à votr...
 
Lunch and Learn ANZ: Achieving Self-Service Analytics with a Governed Data Se...
Lunch and Learn ANZ: Achieving Self-Service Analytics with a Governed Data Se...Lunch and Learn ANZ: Achieving Self-Service Analytics with a Governed Data Se...
Lunch and Learn ANZ: Achieving Self-Service Analytics with a Governed Data Se...
 
How to Build Your Data Marketplace with Data Virtualization?
How to Build Your Data Marketplace with Data Virtualization?How to Build Your Data Marketplace with Data Virtualization?
How to Build Your Data Marketplace with Data Virtualization?
 
Webinar #2 - Transforming Challenges into Opportunities for Credit Unions
Webinar #2 - Transforming Challenges into Opportunities for Credit UnionsWebinar #2 - Transforming Challenges into Opportunities for Credit Unions
Webinar #2 - Transforming Challenges into Opportunities for Credit Unions
 
Enabling Data Catalog users with advanced usability
Enabling Data Catalog users with advanced usabilityEnabling Data Catalog users with advanced usability
Enabling Data Catalog users with advanced usability
 
Denodo Partner Connect: Technical Webinar - Architect Associate Certification...
Denodo Partner Connect: Technical Webinar - Architect Associate Certification...Denodo Partner Connect: Technical Webinar - Architect Associate Certification...
Denodo Partner Connect: Technical Webinar - Architect Associate Certification...
 
GenAI y el futuro de la gestión de datos: mitos y realidades
GenAI y el futuro de la gestión de datos: mitos y realidadesGenAI y el futuro de la gestión de datos: mitos y realidades
GenAI y el futuro de la gestión de datos: mitos y realidades
 

Dernier

Discover Why Less is More in B2B Research
Discover Why Less is More in B2B ResearchDiscover Why Less is More in B2B Research
Discover Why Less is More in B2B Researchmichael115558
 
Smarteg dropshipping via API with DroFx.pptx
Smarteg dropshipping via API with DroFx.pptxSmarteg dropshipping via API with DroFx.pptx
Smarteg dropshipping via API with DroFx.pptxolyaivanovalion
 
BDSM⚡Call Girls in Mandawali Delhi >༒8448380779 Escort Service
BDSM⚡Call Girls in Mandawali Delhi >༒8448380779 Escort ServiceBDSM⚡Call Girls in Mandawali Delhi >༒8448380779 Escort Service
BDSM⚡Call Girls in Mandawali Delhi >༒8448380779 Escort ServiceDelhi Call girls
 
CebaBaby dropshipping via API with DroFX.pptx
CebaBaby dropshipping via API with DroFX.pptxCebaBaby dropshipping via API with DroFX.pptx
CebaBaby dropshipping via API with DroFX.pptxolyaivanovalion
 
Invezz.com - Grow your wealth with trading signals
Invezz.com - Grow your wealth with trading signalsInvezz.com - Grow your wealth with trading signals
Invezz.com - Grow your wealth with trading signalsInvezz1
 
Call Girls Bannerghatta Road Just Call 👗 7737669865 👗 Top Class Call Girl Ser...
Call Girls Bannerghatta Road Just Call 👗 7737669865 👗 Top Class Call Girl Ser...Call Girls Bannerghatta Road Just Call 👗 7737669865 👗 Top Class Call Girl Ser...
Call Girls Bannerghatta Road Just Call 👗 7737669865 👗 Top Class Call Girl Ser...amitlee9823
 
Data-Analysis for Chicago Crime Data 2023
Data-Analysis for Chicago Crime Data  2023Data-Analysis for Chicago Crime Data  2023
Data-Analysis for Chicago Crime Data 2023ymrp368
 
VIP Model Call Girls Hinjewadi ( Pune ) Call ON 8005736733 Starting From 5K t...
VIP Model Call Girls Hinjewadi ( Pune ) Call ON 8005736733 Starting From 5K t...VIP Model Call Girls Hinjewadi ( Pune ) Call ON 8005736733 Starting From 5K t...
VIP Model Call Girls Hinjewadi ( Pune ) Call ON 8005736733 Starting From 5K t...SUHANI PANDEY
 
Midocean dropshipping via API with DroFx
Midocean dropshipping via API with DroFxMidocean dropshipping via API with DroFx
Midocean dropshipping via API with DroFxolyaivanovalion
 
April 2024 - Crypto Market Report's Analysis
April 2024 - Crypto Market Report's AnalysisApril 2024 - Crypto Market Report's Analysis
April 2024 - Crypto Market Report's Analysismanisha194592
 
Zuja dropshipping via API with DroFx.pptx
Zuja dropshipping via API with DroFx.pptxZuja dropshipping via API with DroFx.pptx
Zuja dropshipping via API with DroFx.pptxolyaivanovalion
 
Introduction-to-Machine-Learning (1).pptx
Introduction-to-Machine-Learning (1).pptxIntroduction-to-Machine-Learning (1).pptx
Introduction-to-Machine-Learning (1).pptxfirstjob4
 
Best VIP Call Girls Noida Sector 39 Call Me: 8448380779
Best VIP Call Girls Noida Sector 39 Call Me: 8448380779Best VIP Call Girls Noida Sector 39 Call Me: 8448380779
Best VIP Call Girls Noida Sector 39 Call Me: 8448380779Delhi Call girls
 
Delhi Call Girls Punjabi Bagh 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call
Delhi Call Girls Punjabi Bagh 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip CallDelhi Call Girls Punjabi Bagh 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call
Delhi Call Girls Punjabi Bagh 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Callshivangimorya083
 
100-Concepts-of-AI by Anupama Kate .pptx
100-Concepts-of-AI by Anupama Kate .pptx100-Concepts-of-AI by Anupama Kate .pptx
100-Concepts-of-AI by Anupama Kate .pptxAnupama Kate
 
Accredited-Transport-Cooperatives-Jan-2021-Web.pdf
Accredited-Transport-Cooperatives-Jan-2021-Web.pdfAccredited-Transport-Cooperatives-Jan-2021-Web.pdf
Accredited-Transport-Cooperatives-Jan-2021-Web.pdfadriantubila
 
Carero dropshipping via API with DroFx.pptx
Carero dropshipping via API with DroFx.pptxCarero dropshipping via API with DroFx.pptx
Carero dropshipping via API with DroFx.pptxolyaivanovalion
 
Call me @ 9892124323 Cheap Rate Call Girls in Vashi with Real Photo 100% Secure
Call me @ 9892124323  Cheap Rate Call Girls in Vashi with Real Photo 100% SecureCall me @ 9892124323  Cheap Rate Call Girls in Vashi with Real Photo 100% Secure
Call me @ 9892124323 Cheap Rate Call Girls in Vashi with Real Photo 100% SecurePooja Nehwal
 

Dernier (20)

Discover Why Less is More in B2B Research
Discover Why Less is More in B2B ResearchDiscover Why Less is More in B2B Research
Discover Why Less is More in B2B Research
 
CHEAP Call Girls in Saket (-DELHI )🔝 9953056974🔝(=)/CALL GIRLS SERVICE
CHEAP Call Girls in Saket (-DELHI )🔝 9953056974🔝(=)/CALL GIRLS SERVICECHEAP Call Girls in Saket (-DELHI )🔝 9953056974🔝(=)/CALL GIRLS SERVICE
CHEAP Call Girls in Saket (-DELHI )🔝 9953056974🔝(=)/CALL GIRLS SERVICE
 
Smarteg dropshipping via API with DroFx.pptx
Smarteg dropshipping via API with DroFx.pptxSmarteg dropshipping via API with DroFx.pptx
Smarteg dropshipping via API with DroFx.pptx
 
BDSM⚡Call Girls in Mandawali Delhi >༒8448380779 Escort Service
BDSM⚡Call Girls in Mandawali Delhi >༒8448380779 Escort ServiceBDSM⚡Call Girls in Mandawali Delhi >༒8448380779 Escort Service
BDSM⚡Call Girls in Mandawali Delhi >༒8448380779 Escort Service
 
CebaBaby dropshipping via API with DroFX.pptx
CebaBaby dropshipping via API with DroFX.pptxCebaBaby dropshipping via API with DroFX.pptx
CebaBaby dropshipping via API with DroFX.pptx
 
Invezz.com - Grow your wealth with trading signals
Invezz.com - Grow your wealth with trading signalsInvezz.com - Grow your wealth with trading signals
Invezz.com - Grow your wealth with trading signals
 
Delhi 99530 vip 56974 Genuine Escort Service Call Girls in Kishangarh
Delhi 99530 vip 56974 Genuine Escort Service Call Girls in  KishangarhDelhi 99530 vip 56974 Genuine Escort Service Call Girls in  Kishangarh
Delhi 99530 vip 56974 Genuine Escort Service Call Girls in Kishangarh
 
Call Girls Bannerghatta Road Just Call 👗 7737669865 👗 Top Class Call Girl Ser...
Call Girls Bannerghatta Road Just Call 👗 7737669865 👗 Top Class Call Girl Ser...Call Girls Bannerghatta Road Just Call 👗 7737669865 👗 Top Class Call Girl Ser...
Call Girls Bannerghatta Road Just Call 👗 7737669865 👗 Top Class Call Girl Ser...
 
Data-Analysis for Chicago Crime Data 2023
Data-Analysis for Chicago Crime Data  2023Data-Analysis for Chicago Crime Data  2023
Data-Analysis for Chicago Crime Data 2023
 
VIP Model Call Girls Hinjewadi ( Pune ) Call ON 8005736733 Starting From 5K t...
VIP Model Call Girls Hinjewadi ( Pune ) Call ON 8005736733 Starting From 5K t...VIP Model Call Girls Hinjewadi ( Pune ) Call ON 8005736733 Starting From 5K t...
VIP Model Call Girls Hinjewadi ( Pune ) Call ON 8005736733 Starting From 5K t...
 
Midocean dropshipping via API with DroFx
Midocean dropshipping via API with DroFxMidocean dropshipping via API with DroFx
Midocean dropshipping via API with DroFx
 
April 2024 - Crypto Market Report's Analysis
April 2024 - Crypto Market Report's AnalysisApril 2024 - Crypto Market Report's Analysis
April 2024 - Crypto Market Report's Analysis
 
Zuja dropshipping via API with DroFx.pptx
Zuja dropshipping via API with DroFx.pptxZuja dropshipping via API with DroFx.pptx
Zuja dropshipping via API with DroFx.pptx
 
Introduction-to-Machine-Learning (1).pptx
Introduction-to-Machine-Learning (1).pptxIntroduction-to-Machine-Learning (1).pptx
Introduction-to-Machine-Learning (1).pptx
 
Best VIP Call Girls Noida Sector 39 Call Me: 8448380779
Best VIP Call Girls Noida Sector 39 Call Me: 8448380779Best VIP Call Girls Noida Sector 39 Call Me: 8448380779
Best VIP Call Girls Noida Sector 39 Call Me: 8448380779
 
Delhi Call Girls Punjabi Bagh 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call
Delhi Call Girls Punjabi Bagh 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip CallDelhi Call Girls Punjabi Bagh 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call
Delhi Call Girls Punjabi Bagh 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call
 
100-Concepts-of-AI by Anupama Kate .pptx
100-Concepts-of-AI by Anupama Kate .pptx100-Concepts-of-AI by Anupama Kate .pptx
100-Concepts-of-AI by Anupama Kate .pptx
 
Accredited-Transport-Cooperatives-Jan-2021-Web.pdf
Accredited-Transport-Cooperatives-Jan-2021-Web.pdfAccredited-Transport-Cooperatives-Jan-2021-Web.pdf
Accredited-Transport-Cooperatives-Jan-2021-Web.pdf
 
Carero dropshipping via API with DroFx.pptx
Carero dropshipping via API with DroFx.pptxCarero dropshipping via API with DroFx.pptx
Carero dropshipping via API with DroFx.pptx
 
Call me @ 9892124323 Cheap Rate Call Girls in Vashi with Real Photo 100% Secure
Call me @ 9892124323  Cheap Rate Call Girls in Vashi with Real Photo 100% SecureCall me @ 9892124323  Cheap Rate Call Girls in Vashi with Real Photo 100% Secure
Call me @ 9892124323 Cheap Rate Call Girls in Vashi with Real Photo 100% Secure
 

From Traditional Data Warehouse to Holistic Data Integration and Faster Data Delivery

  • 1. I N T E R N I N T E R N From Traditional Data Warehouse to Holistic Data Integration and Faster Data Delivery 6.5.2022 Dagfinn Røed, SpareBank 1 Forsikring 1
  • 2. I N T E R N I N T E R N Agenda 2 Introduction Rapid business changes and new requirements The challenges of establishing a new data platform The benefits with the new data platform, including anti-money laundry use case Summary
  • 3. I N T E R N I N T E R N Introduction 3 • Part of the SpareBank 1 Alliance, the second largest finance group in Norway • The third largest supplier of pension products in Norway • Approximately 130 employees • Originally life and non-life insurance company, but re-established in 2019 as a pension only company SpareBank 1 Forsikring • More than 30 years experience in DataWarehousing, analytics and data plattforms • Various positions as data architect, advisor/consultant, project manager, department manager and product owner/systems owner • 11 years in SpareBank 1 Forsikring Dagfinn Røed
  • 4. I N T E R N I N T E R N Business changes New requirements Keeping up with rapid business changes and new requirements Data Warehouse development 199 0 2000 201 0 2020
  • 5. I N T E R N I N T E R N 2018 - from data warehouse to an agile data platform 5 • We had a vision about a new data platform - Cloud-based - Flexible - Scaleable - Faster data delivery - More data self-service - Wide range of data sources and data types - Supporting the AI/ML, but also taking care of all the existing reporting • We started looking into Data Virualization as a key technology
  • 6. I N T E R N I N T E R N 2019 – merge and demerge 6 Fremtind Forsikring • New Insurance Company – Fremtind • Merge of the non-life and life insurance businesses in SpareBank 1 and DNB (the largest bank in Norway) • Continue to use the existing data and analytics solutions from SpareBank 1 Forsikring + Sparebank 1 Forsikring • Pure pension company • Still dependent on Fremtind to perform all compliance and mandatory reporting • Will (as soon as possible) need a completely new data and analytics solution
  • 7. I N T E R N I N T E R N The upside – we could now build a data platform for the future 7 • A platform for all types of data to be shared in the company • Flexibility to add new data fast when they are needed • Make it easy for the users to find the data they need • The data platform can offer both processed data and raw data • Facilitates data self-service • Personal information is secure and compliant with GDPR • The platform is open and can support both analytical and other use-cases We decided to build the platform on-premise using MS SQL Server, Denodo Data Virutalization and Tableau
  • 8. I N T E R N I N T E R N The reality kicks-in 8 • The users were less advanced compared to the super users in the old company - They showed resistance to switching to a new end-user tool - They wanted the data exactly in the same format they were used to They did not want raw data or smaller «building blocks». They just wanted big flat tables. - They had no knowledge about topics like AI/ML • The development team - We did not have experiences with Denodo, primarily traditional ETL-development - We had to keep historical data many years back - We were in a hurry – it was urgent to stop using the existing (shared) analytical solutions Most of the time and resources the first two years were spent on building a new, «hybrid» Data Warehouse inside the data platform incapsulated by Denodo Data Virtualization.
  • 9. I N T E R N I N T E R N First Data Platform benefit – anti-money laundry 9 The benefits of the data platform become clear when developing new solutions such as the anti-money laundering solution. Customer input: • PEP information • Real-right holders • Identification External data • PEP information • Real-right holders • Nationality
  • 10. I N T E R N I N T E R N New insurance product - EPK • Accessing data-hub in real time • Monitoring pension fund movements • Creating and distributing win-back data • Market leader for the EPK-product Faster replacement of core system • Integrating data from new core system only virtually • Keep existing data interface for consumers • Less coding, faster development • Reducing physical Data Warehouse 10 After providing the business with the “bread and butter” they need, we see many gains from using the data platform going forward
  • 11. 11 • Business changes happens rapidly. You need an agile and flexible platform • Understand your users and their needs • Make sure you have all the competence you need on the new tools • Spend time on the information and solution architecture • Focus on virtual development. Only build the ETL-factory where it is absolutely needed • Your platform must support all the legacy reporting, not only the fun and exciting new stuff • Make realistic plans and deliver frequent business benefits • Data virtualization is an extremely useful technology and makes all integration tasks much easier Summary