SlideShare a Scribd company logo

Is it sensible to use Data Vault at all? Conclusions from a project.

Capgemini
Capgemini

The presentation focuses on the question “Is it sensible to use Data Vault at all?” The author outlines the impact of Data Vault on the architecture, the implementation and on the project.

1 of 27
Download to read offline
Is it sensible to use
Data Vault at all?
Conclusions from a project.
Mainz, 15th March 2016
11. Oracle DWH Community Treffen
Alexander Mendle (Insights & Data, Capgemini)
Our customers’ business model.
Project setup.
Green field DW with 3 source systems – ERP, CMS, and a transaction system.
Data Vault was preset by our customers group who also provided a Data Vault architect.
Capgemini supported the project in implementation and testing.
With over 11,000 professionals across 40+ countries …
… and being part of a multi-faceted group …
180,000 employees(1)
in more than 40 countries
A promise that expresses
our brand philosophy
Revenues(2)
€10.573 billion
Operating margin
€486 million
Operating profit
€447 million
Net cash and
cash equivalents
€1,464 million
6 strategic alliances
EMC2, HP, IBM, Microsoft,
Oracle, SAP
7 values shared
since the company’s
creation in 1967
honesty/boldness/
trust/freedom/
team spirit/modesty/fun
A wide range of
cutting-edge
expertise for all
our clients
Five strategic sectors
Expertise in
Automotive ,Banking
Consumer Products & Retail
Energy and Utilities Insurance
A unique way
1 Headcount including IGATE
2 For the FY15-16
Capgemini Insights & Data Services Model
Copyright © Capgemini 2013. All Rights Reserved
5DATAVAULT IST DER EINSATZ VON DATA VAULT SINNVOLL-V10.PPTX
Industry
verticalization
Automotive
Consumer Products
& Retail
Public Sector
Financial Services
Telco
Energy & Utilities
Life Sciences
Media &
Entertainment
Core capabilities and offers
Data & Info
Management
Master Data
Management
Big Data
(Hadoop/NoSQL)
Optimized data
warehouse
EPM
(Enterprise Performance
Management)
BI & Data
Visualization
Predictive +
data science
Real-time
analytics
Delivery
models
BI Service Center
Cloud
Application
management
Agile
as-a-service &
BPO
IP Solutions
Rapid prototyping/
POC
Business engagement
Strategic
customer
partnership
Digital
transformation
Specific
alliance
initiatives
Performance
management
& strategy
Risk sharing
Governance, architecture and strategy
Data and
information
governance
Architecture
Privacy &
security
Information
strategy
Business
Data Lake
Copyright © Capgemini 2013. All Rights Reserved
6DATAVAULT IST DER EINSATZ VON DATA VAULT SINNVOLL-V10.PPTX
Agenda
 What is Data Vault? – just a quick glimpse
 Impact on Architecture
 Impact on Implementation
 Impact on Project
 Summary

Recommended

Data Vault Vs Data Lake
Data Vault Vs Data LakeData Vault Vs Data Lake
Data Vault Vs Data LakeCalum Miller
 
A Practical Guide to Anomaly Detection for DevOps
A Practical Guide to Anomaly Detection for DevOpsA Practical Guide to Anomaly Detection for DevOps
A Practical Guide to Anomaly Detection for DevOpsBigPanda
 
Gain Deep Visibility into APIs and Integrations with Anypoint Monitoring
Gain Deep Visibility into APIs and Integrations with Anypoint MonitoringGain Deep Visibility into APIs and Integrations with Anypoint Monitoring
Gain Deep Visibility into APIs and Integrations with Anypoint MonitoringInfluxData
 
Building Data Products with BigQuery for PPC and SEO (SMX 2022)
Building Data Products with BigQuery for PPC and SEO (SMX 2022)Building Data Products with BigQuery for PPC and SEO (SMX 2022)
Building Data Products with BigQuery for PPC and SEO (SMX 2022)Christopher Gutknecht
 
Architecting Snowflake for High Concurrency and High Performance
Architecting Snowflake for High Concurrency and High PerformanceArchitecting Snowflake for High Concurrency and High Performance
Architecting Snowflake for High Concurrency and High PerformanceSamanthaBerlant
 
Qlik View Corporate Overview Ppt Presentation
Qlik View Corporate Overview Ppt PresentationQlik View Corporate Overview Ppt Presentation
Qlik View Corporate Overview Ppt Presentationpdalalau
 

More Related Content

What's hot

From Insights to Action, How to build and maintain a Data Driven Organization...
From Insights to Action, How to build and maintain a Data Driven Organization...From Insights to Action, How to build and maintain a Data Driven Organization...
From Insights to Action, How to build and maintain a Data Driven Organization...Amazon Web Services Korea
 
Best Practices for Building a Data Lake with Amazon S3 - August 2016 Monthly ...
Best Practices for Building a Data Lake with Amazon S3 - August 2016 Monthly ...Best Practices for Building a Data Lake with Amazon S3 - August 2016 Monthly ...
Best Practices for Building a Data Lake with Amazon S3 - August 2016 Monthly ...Amazon Web Services
 
Pipelines and Data Flows: Introduction to Data Integration in Azure Synapse A...
Pipelines and Data Flows: Introduction to Data Integration in Azure Synapse A...Pipelines and Data Flows: Introduction to Data Integration in Azure Synapse A...
Pipelines and Data Flows: Introduction to Data Integration in Azure Synapse A...Cathrine Wilhelmsen
 
VisiQuate: Azure cloud migration case study
VisiQuate: Azure cloud migration case studyVisiQuate: Azure cloud migration case study
VisiQuate: Azure cloud migration case studyLeonid Nekhymchuk
 
2021 04-20 apache arrow and its impact on the database industry.pptx
2021 04-20  apache arrow and its impact on the database industry.pptx2021 04-20  apache arrow and its impact on the database industry.pptx
2021 04-20 apache arrow and its impact on the database industry.pptxAndrew Lamb
 
글로벌 기업들의 효과적인 데이터 분석을 위한 Data Lake 구축 및 분석 사례 - 김준형 (AWS 솔루션즈 아키텍트)
글로벌 기업들의 효과적인 데이터 분석을 위한 Data Lake 구축 및 분석 사례 - 김준형 (AWS 솔루션즈 아키텍트)글로벌 기업들의 효과적인 데이터 분석을 위한 Data Lake 구축 및 분석 사례 - 김준형 (AWS 솔루션즈 아키텍트)
글로벌 기업들의 효과적인 데이터 분석을 위한 Data Lake 구축 및 분석 사례 - 김준형 (AWS 솔루션즈 아키텍트)Amazon Web Services Korea
 
Elasticsearch in Netflix
Elasticsearch in NetflixElasticsearch in Netflix
Elasticsearch in NetflixDanny Yuan
 
Introduction to AWS Lake Formation.pptx
Introduction to AWS Lake Formation.pptxIntroduction to AWS Lake Formation.pptx
Introduction to AWS Lake Formation.pptxSwathiPonugumati
 
Google Cloud Machine Learning
 Google Cloud Machine Learning  Google Cloud Machine Learning
Google Cloud Machine Learning India Quotient
 
AWS Lake Formation을 통한 손쉬운 데이터 레이크 구성 및 관리 - 윤석찬 :: AWS Unboxing 온라인 세미나
AWS Lake Formation을 통한 손쉬운 데이터 레이크 구성 및 관리 - 윤석찬 :: AWS Unboxing 온라인 세미나AWS Lake Formation을 통한 손쉬운 데이터 레이크 구성 및 관리 - 윤석찬 :: AWS Unboxing 온라인 세미나
AWS Lake Formation을 통한 손쉬운 데이터 레이크 구성 및 관리 - 윤석찬 :: AWS Unboxing 온라인 세미나Amazon Web Services Korea
 
Azure BI Cloud Architectural Guidelines.pdf
Azure BI Cloud Architectural Guidelines.pdfAzure BI Cloud Architectural Guidelines.pdf
Azure BI Cloud Architectural Guidelines.pdfpbonillo1
 
Modeling Big Data with the ArchiMate 3.0 Language
Modeling Big Data with the ArchiMate 3.0 LanguageModeling Big Data with the ArchiMate 3.0 Language
Modeling Big Data with the ArchiMate 3.0 LanguageIver Band
 
Deep Dive on Microservices and Amazon ECS
Deep Dive on Microservices and Amazon ECSDeep Dive on Microservices and Amazon ECS
Deep Dive on Microservices and Amazon ECSAmazon Web Services
 
data platform on kubernetes
data platform on kubernetesdata platform on kubernetes
data platform on kubernetes창언 정
 
Pipelines and Data Flows: Introduction to Data Integration in Azure Synapse A...
Pipelines and Data Flows: Introduction to Data Integration in Azure Synapse A...Pipelines and Data Flows: Introduction to Data Integration in Azure Synapse A...
Pipelines and Data Flows: Introduction to Data Integration in Azure Synapse A...Cathrine Wilhelmsen
 
AWS re:Invent 2016: Strategic Planning for Long-Term Data Archiving with Amaz...
AWS re:Invent 2016: Strategic Planning for Long-Term Data Archiving with Amaz...AWS re:Invent 2016: Strategic Planning for Long-Term Data Archiving with Amaz...
AWS re:Invent 2016: Strategic Planning for Long-Term Data Archiving with Amaz...Amazon Web Services
 
Amazon Redshift의 이해와 활용 (김용우) - AWS DB Day
Amazon Redshift의 이해와 활용 (김용우) - AWS DB DayAmazon Redshift의 이해와 활용 (김용우) - AWS DB Day
Amazon Redshift의 이해와 활용 (김용우) - AWS DB DayAmazon Web Services Korea
 

What's hot (20)

From Insights to Action, How to build and maintain a Data Driven Organization...
From Insights to Action, How to build and maintain a Data Driven Organization...From Insights to Action, How to build and maintain a Data Driven Organization...
From Insights to Action, How to build and maintain a Data Driven Organization...
 
Best Practices for Building a Data Lake with Amazon S3 - August 2016 Monthly ...
Best Practices for Building a Data Lake with Amazon S3 - August 2016 Monthly ...Best Practices for Building a Data Lake with Amazon S3 - August 2016 Monthly ...
Best Practices for Building a Data Lake with Amazon S3 - August 2016 Monthly ...
 
Pipelines and Data Flows: Introduction to Data Integration in Azure Synapse A...
Pipelines and Data Flows: Introduction to Data Integration in Azure Synapse A...Pipelines and Data Flows: Introduction to Data Integration in Azure Synapse A...
Pipelines and Data Flows: Introduction to Data Integration in Azure Synapse A...
 
VisiQuate: Azure cloud migration case study
VisiQuate: Azure cloud migration case studyVisiQuate: Azure cloud migration case study
VisiQuate: Azure cloud migration case study
 
2021 04-20 apache arrow and its impact on the database industry.pptx
2021 04-20  apache arrow and its impact on the database industry.pptx2021 04-20  apache arrow and its impact on the database industry.pptx
2021 04-20 apache arrow and its impact on the database industry.pptx
 
Ml ops on AWS
Ml ops on AWSMl ops on AWS
Ml ops on AWS
 
글로벌 기업들의 효과적인 데이터 분석을 위한 Data Lake 구축 및 분석 사례 - 김준형 (AWS 솔루션즈 아키텍트)
글로벌 기업들의 효과적인 데이터 분석을 위한 Data Lake 구축 및 분석 사례 - 김준형 (AWS 솔루션즈 아키텍트)글로벌 기업들의 효과적인 데이터 분석을 위한 Data Lake 구축 및 분석 사례 - 김준형 (AWS 솔루션즈 아키텍트)
글로벌 기업들의 효과적인 데이터 분석을 위한 Data Lake 구축 및 분석 사례 - 김준형 (AWS 솔루션즈 아키텍트)
 
Elasticsearch in Netflix
Elasticsearch in NetflixElasticsearch in Netflix
Elasticsearch in Netflix
 
Introduction to AWS Lake Formation.pptx
Introduction to AWS Lake Formation.pptxIntroduction to AWS Lake Formation.pptx
Introduction to AWS Lake Formation.pptx
 
Azure Synapse Analytics
Azure Synapse AnalyticsAzure Synapse Analytics
Azure Synapse Analytics
 
Google Cloud Machine Learning
 Google Cloud Machine Learning  Google Cloud Machine Learning
Google Cloud Machine Learning
 
AWS Lake Formation을 통한 손쉬운 데이터 레이크 구성 및 관리 - 윤석찬 :: AWS Unboxing 온라인 세미나
AWS Lake Formation을 통한 손쉬운 데이터 레이크 구성 및 관리 - 윤석찬 :: AWS Unboxing 온라인 세미나AWS Lake Formation을 통한 손쉬운 데이터 레이크 구성 및 관리 - 윤석찬 :: AWS Unboxing 온라인 세미나
AWS Lake Formation을 통한 손쉬운 데이터 레이크 구성 및 관리 - 윤석찬 :: AWS Unboxing 온라인 세미나
 
Azure BI Cloud Architectural Guidelines.pdf
Azure BI Cloud Architectural Guidelines.pdfAzure BI Cloud Architectural Guidelines.pdf
Azure BI Cloud Architectural Guidelines.pdf
 
Modeling Big Data with the ArchiMate 3.0 Language
Modeling Big Data with the ArchiMate 3.0 LanguageModeling Big Data with the ArchiMate 3.0 Language
Modeling Big Data with the ArchiMate 3.0 Language
 
Deep Dive on Microservices and Amazon ECS
Deep Dive on Microservices and Amazon ECSDeep Dive on Microservices and Amazon ECS
Deep Dive on Microservices and Amazon ECS
 
Machine Learning Operations & Azure
Machine Learning Operations & AzureMachine Learning Operations & Azure
Machine Learning Operations & Azure
 
data platform on kubernetes
data platform on kubernetesdata platform on kubernetes
data platform on kubernetes
 
Pipelines and Data Flows: Introduction to Data Integration in Azure Synapse A...
Pipelines and Data Flows: Introduction to Data Integration in Azure Synapse A...Pipelines and Data Flows: Introduction to Data Integration in Azure Synapse A...
Pipelines and Data Flows: Introduction to Data Integration in Azure Synapse A...
 
AWS re:Invent 2016: Strategic Planning for Long-Term Data Archiving with Amaz...
AWS re:Invent 2016: Strategic Planning for Long-Term Data Archiving with Amaz...AWS re:Invent 2016: Strategic Planning for Long-Term Data Archiving with Amaz...
AWS re:Invent 2016: Strategic Planning for Long-Term Data Archiving with Amaz...
 
Amazon Redshift의 이해와 활용 (김용우) - AWS DB Day
Amazon Redshift의 이해와 활용 (김용우) - AWS DB DayAmazon Redshift의 이해와 활용 (김용우) - AWS DB Day
Amazon Redshift의 이해와 활용 (김용우) - AWS DB Day
 

Similar to Is it sensible to use Data Vault at all? Conclusions from a project.

Introduction to Modern Data Virtualization 2021 (APAC)
Introduction to Modern Data Virtualization 2021 (APAC)Introduction to Modern Data Virtualization 2021 (APAC)
Introduction to Modern Data Virtualization 2021 (APAC)Denodo
 
The Essentials Of Project Management
The Essentials Of Project ManagementThe Essentials Of Project Management
The Essentials Of Project ManagementLaura Arrigo
 
Open Source DWBI-A Primer
Open Source DWBI-A PrimerOpen Source DWBI-A Primer
Open Source DWBI-A Primerpartha69
 
Microsoft Fabric Intro D Koutsanastasis
Microsoft Fabric Intro D KoutsanastasisMicrosoft Fabric Intro D Koutsanastasis
Microsoft Fabric Intro D KoutsanastasisUni Systems S.M.S.A.
 
A Logical Architecture is Always a Flexible Architecture (ASEAN)
A Logical Architecture is Always a Flexible Architecture (ASEAN)A Logical Architecture is Always a Flexible Architecture (ASEAN)
A Logical Architecture is Always a Flexible Architecture (ASEAN)Denodo
 
Big data presentation, explanations and use cases in industrial sector
Big data presentation, explanations and use cases in industrial sectorBig data presentation, explanations and use cases in industrial sector
Big data presentation, explanations and use cases in industrial sectorNicolas Sarramagna
 
Data virtualization an introduction
Data virtualization an introductionData virtualization an introduction
Data virtualization an introductionDenodo
 
Real-life Customer Cases using Data Vault and Data Warehouse Automation
Real-life Customer Cases using Data Vault and Data Warehouse AutomationReal-life Customer Cases using Data Vault and Data Warehouse Automation
Real-life Customer Cases using Data Vault and Data Warehouse AutomationPatrick Van Renterghem
 
Accelerate and Scale Big Data Analytics and Machine Learning Pipelines with D...
Accelerate and Scale Big Data Analytics and Machine Learning Pipelines with D...Accelerate and Scale Big Data Analytics and Machine Learning Pipelines with D...
Accelerate and Scale Big Data Analytics and Machine Learning Pipelines with D...Alluxio, Inc.
 
HP Moonshot. Progettato per i Data Center, costruito per il pianeta.
HP Moonshot. Progettato per i Data Center, costruito per il pianeta.HP Moonshot. Progettato per i Data Center, costruito per il pianeta.
HP Moonshot. Progettato per i Data Center, costruito per il pianeta.HP Enterprise Italia
 
Why Data Virtualization? An Introduction
Why Data Virtualization? An IntroductionWhy Data Virtualization? An Introduction
Why Data Virtualization? An IntroductionDenodo
 
IBM TS7610 ProtecTIER Deduplication Appliance Express – Enterprise Level Tech...
IBM TS7610 ProtecTIER Deduplication Appliance Express – Enterprise Level Tech...IBM TS7610 ProtecTIER Deduplication Appliance Express – Enterprise Level Tech...
IBM TS7610 ProtecTIER Deduplication Appliance Express – Enterprise Level Tech...IBM India Smarter Computing
 
Sadas Engine + QlikView
Sadas Engine + QlikViewSadas Engine + QlikView
Sadas Engine + QlikViewSadas
 
Bridging the Last Mile: Getting Data to the People Who Need It
Bridging the Last Mile: Getting Data to the People Who Need ItBridging the Last Mile: Getting Data to the People Who Need It
Bridging the Last Mile: Getting Data to the People Who Need ItDenodo
 
Unify Data at Memory Speed
Unify Data at Memory SpeedUnify Data at Memory Speed
Unify Data at Memory SpeedAlluxio, Inc.
 
Data Virtualization – Gateway to a Digital Business - Barry Devlin
Data Virtualization – Gateway to a Digital Business - Barry DevlinData Virtualization – Gateway to a Digital Business - Barry Devlin
Data Virtualization – Gateway to a Digital Business - Barry DevlinDenodo
 
Primendi Visiooniseminar 2014 - Simplivity Omnicube
Primendi Visiooniseminar 2014 - Simplivity OmnicubePrimendi Visiooniseminar 2014 - Simplivity Omnicube
Primendi Visiooniseminar 2014 - Simplivity OmnicubePrimend
 

Similar to Is it sensible to use Data Vault at all? Conclusions from a project. (20)

Introduction to Modern Data Virtualization 2021 (APAC)
Introduction to Modern Data Virtualization 2021 (APAC)Introduction to Modern Data Virtualization 2021 (APAC)
Introduction to Modern Data Virtualization 2021 (APAC)
 
The Essentials Of Project Management
The Essentials Of Project ManagementThe Essentials Of Project Management
The Essentials Of Project Management
 
Open Source DWBI-A Primer
Open Source DWBI-A PrimerOpen Source DWBI-A Primer
Open Source DWBI-A Primer
 
4SubseaEngLR
4SubseaEngLR4SubseaEngLR
4SubseaEngLR
 
Microsoft Fabric Intro D Koutsanastasis
Microsoft Fabric Intro D KoutsanastasisMicrosoft Fabric Intro D Koutsanastasis
Microsoft Fabric Intro D Koutsanastasis
 
A Logical Architecture is Always a Flexible Architecture (ASEAN)
A Logical Architecture is Always a Flexible Architecture (ASEAN)A Logical Architecture is Always a Flexible Architecture (ASEAN)
A Logical Architecture is Always a Flexible Architecture (ASEAN)
 
Big data presentation, explanations and use cases in industrial sector
Big data presentation, explanations and use cases in industrial sectorBig data presentation, explanations and use cases in industrial sector
Big data presentation, explanations and use cases in industrial sector
 
Data virtualization an introduction
Data virtualization an introductionData virtualization an introduction
Data virtualization an introduction
 
Real-life Customer Cases using Data Vault and Data Warehouse Automation
Real-life Customer Cases using Data Vault and Data Warehouse AutomationReal-life Customer Cases using Data Vault and Data Warehouse Automation
Real-life Customer Cases using Data Vault and Data Warehouse Automation
 
Accelerate and Scale Big Data Analytics and Machine Learning Pipelines with D...
Accelerate and Scale Big Data Analytics and Machine Learning Pipelines with D...Accelerate and Scale Big Data Analytics and Machine Learning Pipelines with D...
Accelerate and Scale Big Data Analytics and Machine Learning Pipelines with D...
 
HP Moonshot. Progettato per i Data Center, costruito per il pianeta.
HP Moonshot. Progettato per i Data Center, costruito per il pianeta.HP Moonshot. Progettato per i Data Center, costruito per il pianeta.
HP Moonshot. Progettato per i Data Center, costruito per il pianeta.
 
Why Data Virtualization? An Introduction
Why Data Virtualization? An IntroductionWhy Data Virtualization? An Introduction
Why Data Virtualization? An Introduction
 
IBM TS7610 ProtecTIER Deduplication Appliance Express – Enterprise Level Tech...
IBM TS7610 ProtecTIER Deduplication Appliance Express – Enterprise Level Tech...IBM TS7610 ProtecTIER Deduplication Appliance Express – Enterprise Level Tech...
IBM TS7610 ProtecTIER Deduplication Appliance Express – Enterprise Level Tech...
 
Sadas Engine + QlikView
Sadas Engine + QlikViewSadas Engine + QlikView
Sadas Engine + QlikView
 
The new EDW
The new EDWThe new EDW
The new EDW
 
SAP vs SAS - Comparison
SAP vs SAS - ComparisonSAP vs SAS - Comparison
SAP vs SAS - Comparison
 
Bridging the Last Mile: Getting Data to the People Who Need It
Bridging the Last Mile: Getting Data to the People Who Need ItBridging the Last Mile: Getting Data to the People Who Need It
Bridging the Last Mile: Getting Data to the People Who Need It
 
Unify Data at Memory Speed
Unify Data at Memory SpeedUnify Data at Memory Speed
Unify Data at Memory Speed
 
Data Virtualization – Gateway to a Digital Business - Barry Devlin
Data Virtualization – Gateway to a Digital Business - Barry DevlinData Virtualization – Gateway to a Digital Business - Barry Devlin
Data Virtualization – Gateway to a Digital Business - Barry Devlin
 
Primendi Visiooniseminar 2014 - Simplivity Omnicube
Primendi Visiooniseminar 2014 - Simplivity OmnicubePrimendi Visiooniseminar 2014 - Simplivity Omnicube
Primendi Visiooniseminar 2014 - Simplivity Omnicube
 

More from Capgemini

Top Healthcare Trends 2022
Top Healthcare Trends 2022Top Healthcare Trends 2022
Top Healthcare Trends 2022Capgemini
 
Top P&C Insurance Trends 2022
Top P&C Insurance Trends 2022Top P&C Insurance Trends 2022
Top P&C Insurance Trends 2022Capgemini
 
Commercial Banking Trends book 2022
Commercial Banking Trends book 2022Commercial Banking Trends book 2022
Commercial Banking Trends book 2022Capgemini
 
Top Trends in Payments 2022
Top Trends in Payments 2022Top Trends in Payments 2022
Top Trends in Payments 2022Capgemini
 
Top Trends in Wealth Management 2022
Top Trends in Wealth Management 2022Top Trends in Wealth Management 2022
Top Trends in Wealth Management 2022Capgemini
 
Retail Banking Trends book 2022
Retail Banking Trends book 2022Retail Banking Trends book 2022
Retail Banking Trends book 2022Capgemini
 
Top Life Insurance Trends 2022
Top Life Insurance Trends 2022Top Life Insurance Trends 2022
Top Life Insurance Trends 2022Capgemini
 
キャップジェミニ、あなたの『RISE WITH SAP』のパートナーです
キャップジェミニ、あなたの『RISE WITH SAP』のパートナーですキャップジェミニ、あなたの『RISE WITH SAP』のパートナーです
キャップジェミニ、あなたの『RISE WITH SAP』のパートナーですCapgemini
 
Property & Casualty Insurance Top Trends 2021
Property & Casualty Insurance Top Trends 2021Property & Casualty Insurance Top Trends 2021
Property & Casualty Insurance Top Trends 2021Capgemini
 
Life Insurance Top Trends 2021
Life Insurance Top Trends 2021Life Insurance Top Trends 2021
Life Insurance Top Trends 2021Capgemini
 
Top Trends in Commercial Banking: 2021
Top Trends in Commercial Banking: 2021Top Trends in Commercial Banking: 2021
Top Trends in Commercial Banking: 2021Capgemini
 
Top Trends in Wealth Management: 2021
Top Trends in Wealth Management: 2021Top Trends in Wealth Management: 2021
Top Trends in Wealth Management: 2021Capgemini
 
Top Trends in Payments: 2021
Top Trends in Payments: 2021Top Trends in Payments: 2021
Top Trends in Payments: 2021Capgemini
 
Health Insurance Top Trends 2021
Health Insurance Top Trends 2021Health Insurance Top Trends 2021
Health Insurance Top Trends 2021Capgemini
 
Top Trends in Retail Banking: 2021
Top Trends in Retail Banking: 2021Top Trends in Retail Banking: 2021
Top Trends in Retail Banking: 2021Capgemini
 
Capgemini’s Connected Autonomous Planning
Capgemini’s Connected Autonomous PlanningCapgemini’s Connected Autonomous Planning
Capgemini’s Connected Autonomous PlanningCapgemini
 
Top Trends in Retail Banking: 2020
Top Trends in Retail Banking: 2020Top Trends in Retail Banking: 2020
Top Trends in Retail Banking: 2020Capgemini
 
Top Trends in Life Insurance: 2020
Top Trends in Life Insurance: 2020Top Trends in Life Insurance: 2020
Top Trends in Life Insurance: 2020Capgemini
 
Top Trends in Health Insurance: 2020
Top Trends in Health Insurance: 2020Top Trends in Health Insurance: 2020
Top Trends in Health Insurance: 2020Capgemini
 
Top Trends in Payments: 2020
Top Trends in Payments: 2020Top Trends in Payments: 2020
Top Trends in Payments: 2020Capgemini
 

More from Capgemini (20)

Top Healthcare Trends 2022
Top Healthcare Trends 2022Top Healthcare Trends 2022
Top Healthcare Trends 2022
 
Top P&C Insurance Trends 2022
Top P&C Insurance Trends 2022Top P&C Insurance Trends 2022
Top P&C Insurance Trends 2022
 
Commercial Banking Trends book 2022
Commercial Banking Trends book 2022Commercial Banking Trends book 2022
Commercial Banking Trends book 2022
 
Top Trends in Payments 2022
Top Trends in Payments 2022Top Trends in Payments 2022
Top Trends in Payments 2022
 
Top Trends in Wealth Management 2022
Top Trends in Wealth Management 2022Top Trends in Wealth Management 2022
Top Trends in Wealth Management 2022
 
Retail Banking Trends book 2022
Retail Banking Trends book 2022Retail Banking Trends book 2022
Retail Banking Trends book 2022
 
Top Life Insurance Trends 2022
Top Life Insurance Trends 2022Top Life Insurance Trends 2022
Top Life Insurance Trends 2022
 
キャップジェミニ、あなたの『RISE WITH SAP』のパートナーです
キャップジェミニ、あなたの『RISE WITH SAP』のパートナーですキャップジェミニ、あなたの『RISE WITH SAP』のパートナーです
キャップジェミニ、あなたの『RISE WITH SAP』のパートナーです
 
Property & Casualty Insurance Top Trends 2021
Property & Casualty Insurance Top Trends 2021Property & Casualty Insurance Top Trends 2021
Property & Casualty Insurance Top Trends 2021
 
Life Insurance Top Trends 2021
Life Insurance Top Trends 2021Life Insurance Top Trends 2021
Life Insurance Top Trends 2021
 
Top Trends in Commercial Banking: 2021
Top Trends in Commercial Banking: 2021Top Trends in Commercial Banking: 2021
Top Trends in Commercial Banking: 2021
 
Top Trends in Wealth Management: 2021
Top Trends in Wealth Management: 2021Top Trends in Wealth Management: 2021
Top Trends in Wealth Management: 2021
 
Top Trends in Payments: 2021
Top Trends in Payments: 2021Top Trends in Payments: 2021
Top Trends in Payments: 2021
 
Health Insurance Top Trends 2021
Health Insurance Top Trends 2021Health Insurance Top Trends 2021
Health Insurance Top Trends 2021
 
Top Trends in Retail Banking: 2021
Top Trends in Retail Banking: 2021Top Trends in Retail Banking: 2021
Top Trends in Retail Banking: 2021
 
Capgemini’s Connected Autonomous Planning
Capgemini’s Connected Autonomous PlanningCapgemini’s Connected Autonomous Planning
Capgemini’s Connected Autonomous Planning
 
Top Trends in Retail Banking: 2020
Top Trends in Retail Banking: 2020Top Trends in Retail Banking: 2020
Top Trends in Retail Banking: 2020
 
Top Trends in Life Insurance: 2020
Top Trends in Life Insurance: 2020Top Trends in Life Insurance: 2020
Top Trends in Life Insurance: 2020
 
Top Trends in Health Insurance: 2020
Top Trends in Health Insurance: 2020Top Trends in Health Insurance: 2020
Top Trends in Health Insurance: 2020
 
Top Trends in Payments: 2020
Top Trends in Payments: 2020Top Trends in Payments: 2020
Top Trends in Payments: 2020
 

Recently uploaded

HBR SERIES METAL HOUSED RESISTORS POWER ELECTRICAL ABSORBS HIGH CURRENT DURIN...
HBR SERIES METAL HOUSED RESISTORS POWER ELECTRICAL ABSORBS HIGH CURRENT DURIN...HBR SERIES METAL HOUSED RESISTORS POWER ELECTRICAL ABSORBS HIGH CURRENT DURIN...
HBR SERIES METAL HOUSED RESISTORS POWER ELECTRICAL ABSORBS HIGH CURRENT DURIN...htrindia
 
How we think about an advisor tech stack
How we think about an advisor tech stackHow we think about an advisor tech stack
How we think about an advisor tech stackSummit
 
Harnessing the Power of GenAI for Exceptional Product Outcomes by Booking.com...
Harnessing the Power of GenAI for Exceptional Product Outcomes by Booking.com...Harnessing the Power of GenAI for Exceptional Product Outcomes by Booking.com...
Harnessing the Power of GenAI for Exceptional Product Outcomes by Booking.com...Product School
 
My Journey towards Artificial Intelligence
My Journey towards Artificial IntelligenceMy Journey towards Artificial Intelligence
My Journey towards Artificial IntelligenceVijayananda Mohire
 
The Future of Product, by Founder & CEO, Product School
The Future of Product, by Founder & CEO, Product SchoolThe Future of Product, by Founder & CEO, Product School
The Future of Product, by Founder & CEO, Product SchoolProduct School
 
Enhancing Productivity and Insight A Tour of JDK Tools Progress Beyond Java 17
Enhancing Productivity and Insight  A Tour of JDK Tools Progress Beyond Java 17Enhancing Productivity and Insight  A Tour of JDK Tools Progress Beyond Java 17
Enhancing Productivity and Insight A Tour of JDK Tools Progress Beyond Java 17Ana-Maria Mihalceanu
 
Introducing the New FME Community Webinar - Feb 21, 2024 (2).pdf
Introducing the New FME Community Webinar - Feb 21, 2024 (2).pdfIntroducing the New FME Community Webinar - Feb 21, 2024 (2).pdf
Introducing the New FME Community Webinar - Feb 21, 2024 (2).pdfSafe Software
 
Pragmatic UI testing with Compose Semantics.pdf
Pragmatic UI testing with Compose Semantics.pdfPragmatic UI testing with Compose Semantics.pdf
Pragmatic UI testing with Compose Semantics.pdfinfogdgmi
 
Leonis Insights: The State of AI (7 trends for 2023 and 7 predictions for 2024)
Leonis Insights: The State of AI (7 trends for 2023 and 7 predictions for 2024)Leonis Insights: The State of AI (7 trends for 2023 and 7 predictions for 2024)
Leonis Insights: The State of AI (7 trends for 2023 and 7 predictions for 2024)Jay Zhao
 
Artificial Intelligence, Design, and More-than-Human Justice
Artificial Intelligence, Design, and More-than-Human JusticeArtificial Intelligence, Design, and More-than-Human Justice
Artificial Intelligence, Design, and More-than-Human JusticeJosh Gellers
 
How AI and ChatGPT are changing cybersecurity forever.pptx
How AI and ChatGPT are changing cybersecurity forever.pptxHow AI and ChatGPT are changing cybersecurity forever.pptx
How AI and ChatGPT are changing cybersecurity forever.pptxInfosec
 
Dev Dives: Leverage APIs and Gen AI to power automations for RPA and software...
Dev Dives: Leverage APIs and Gen AI to power automations for RPA and software...Dev Dives: Leverage APIs and Gen AI to power automations for RPA and software...
Dev Dives: Leverage APIs and Gen AI to power automations for RPA and software...UiPathCommunity
 
IT Nation Evolve event 2024 - Quarter 1
IT Nation Evolve event 2024  - Quarter 1IT Nation Evolve event 2024  - Quarter 1
IT Nation Evolve event 2024 - Quarter 1Inbay UK
 
"How we created an SRE team in Temabit as a part of FOZZY Group in conditions...
"How we created an SRE team in Temabit as a part of FOZZY Group in conditions..."How we created an SRE team in Temabit as a part of FOZZY Group in conditions...
"How we created an SRE team in Temabit as a part of FOZZY Group in conditions...Fwdays
 
Enterprise Architecture As Strategy - Book Review
Enterprise Architecture As Strategy - Book ReviewEnterprise Architecture As Strategy - Book Review
Enterprise Architecture As Strategy - Book ReviewAshraf Fouad
 
"Testing of Helm Charts or There and Back Again", Yura Rochniak
"Testing of Helm Charts or There and Back Again", Yura Rochniak"Testing of Helm Charts or There and Back Again", Yura Rochniak
"Testing of Helm Charts or There and Back Again", Yura RochniakFwdays
 
Mind your App Footprint 🐾⚡️🌱 (@FlutterHeroes 2024)
Mind your App Footprint 🐾⚡️🌱 (@FlutterHeroes 2024)Mind your App Footprint 🐾⚡️🌱 (@FlutterHeroes 2024)
Mind your App Footprint 🐾⚡️🌱 (@FlutterHeroes 2024)François
 
"Running Open-Source LLM models on Kubernetes", Volodymyr Tsap
"Running Open-Source LLM models on Kubernetes",  Volodymyr Tsap"Running Open-Source LLM models on Kubernetes",  Volodymyr Tsap
"Running Open-Source LLM models on Kubernetes", Volodymyr TsapFwdays
 
Synergy in Leadership and Product Excellence: A Blueprint for Growth by CPO, ...
Synergy in Leadership and Product Excellence: A Blueprint for Growth by CPO, ...Synergy in Leadership and Product Excellence: A Blueprint for Growth by CPO, ...
Synergy in Leadership and Product Excellence: A Blueprint for Growth by CPO, ...Product School
 
"Platform Engineering with Development Containers", Igor Fesenko
"Platform Engineering with Development Containers", Igor Fesenko"Platform Engineering with Development Containers", Igor Fesenko
"Platform Engineering with Development Containers", Igor FesenkoFwdays
 

Recently uploaded (20)

HBR SERIES METAL HOUSED RESISTORS POWER ELECTRICAL ABSORBS HIGH CURRENT DURIN...
HBR SERIES METAL HOUSED RESISTORS POWER ELECTRICAL ABSORBS HIGH CURRENT DURIN...HBR SERIES METAL HOUSED RESISTORS POWER ELECTRICAL ABSORBS HIGH CURRENT DURIN...
HBR SERIES METAL HOUSED RESISTORS POWER ELECTRICAL ABSORBS HIGH CURRENT DURIN...
 
How we think about an advisor tech stack
How we think about an advisor tech stackHow we think about an advisor tech stack
How we think about an advisor tech stack
 
Harnessing the Power of GenAI for Exceptional Product Outcomes by Booking.com...
Harnessing the Power of GenAI for Exceptional Product Outcomes by Booking.com...Harnessing the Power of GenAI for Exceptional Product Outcomes by Booking.com...
Harnessing the Power of GenAI for Exceptional Product Outcomes by Booking.com...
 
My Journey towards Artificial Intelligence
My Journey towards Artificial IntelligenceMy Journey towards Artificial Intelligence
My Journey towards Artificial Intelligence
 
The Future of Product, by Founder & CEO, Product School
The Future of Product, by Founder & CEO, Product SchoolThe Future of Product, by Founder & CEO, Product School
The Future of Product, by Founder & CEO, Product School
 
Enhancing Productivity and Insight A Tour of JDK Tools Progress Beyond Java 17
Enhancing Productivity and Insight  A Tour of JDK Tools Progress Beyond Java 17Enhancing Productivity and Insight  A Tour of JDK Tools Progress Beyond Java 17
Enhancing Productivity and Insight A Tour of JDK Tools Progress Beyond Java 17
 
Introducing the New FME Community Webinar - Feb 21, 2024 (2).pdf
Introducing the New FME Community Webinar - Feb 21, 2024 (2).pdfIntroducing the New FME Community Webinar - Feb 21, 2024 (2).pdf
Introducing the New FME Community Webinar - Feb 21, 2024 (2).pdf
 
Pragmatic UI testing with Compose Semantics.pdf
Pragmatic UI testing with Compose Semantics.pdfPragmatic UI testing with Compose Semantics.pdf
Pragmatic UI testing with Compose Semantics.pdf
 
Leonis Insights: The State of AI (7 trends for 2023 and 7 predictions for 2024)
Leonis Insights: The State of AI (7 trends for 2023 and 7 predictions for 2024)Leonis Insights: The State of AI (7 trends for 2023 and 7 predictions for 2024)
Leonis Insights: The State of AI (7 trends for 2023 and 7 predictions for 2024)
 
Artificial Intelligence, Design, and More-than-Human Justice
Artificial Intelligence, Design, and More-than-Human JusticeArtificial Intelligence, Design, and More-than-Human Justice
Artificial Intelligence, Design, and More-than-Human Justice
 
How AI and ChatGPT are changing cybersecurity forever.pptx
How AI and ChatGPT are changing cybersecurity forever.pptxHow AI and ChatGPT are changing cybersecurity forever.pptx
How AI and ChatGPT are changing cybersecurity forever.pptx
 
Dev Dives: Leverage APIs and Gen AI to power automations for RPA and software...
Dev Dives: Leverage APIs and Gen AI to power automations for RPA and software...Dev Dives: Leverage APIs and Gen AI to power automations for RPA and software...
Dev Dives: Leverage APIs and Gen AI to power automations for RPA and software...
 
IT Nation Evolve event 2024 - Quarter 1
IT Nation Evolve event 2024  - Quarter 1IT Nation Evolve event 2024  - Quarter 1
IT Nation Evolve event 2024 - Quarter 1
 
"How we created an SRE team in Temabit as a part of FOZZY Group in conditions...
"How we created an SRE team in Temabit as a part of FOZZY Group in conditions..."How we created an SRE team in Temabit as a part of FOZZY Group in conditions...
"How we created an SRE team in Temabit as a part of FOZZY Group in conditions...
 
Enterprise Architecture As Strategy - Book Review
Enterprise Architecture As Strategy - Book ReviewEnterprise Architecture As Strategy - Book Review
Enterprise Architecture As Strategy - Book Review
 
"Testing of Helm Charts or There and Back Again", Yura Rochniak
"Testing of Helm Charts or There and Back Again", Yura Rochniak"Testing of Helm Charts or There and Back Again", Yura Rochniak
"Testing of Helm Charts or There and Back Again", Yura Rochniak
 
Mind your App Footprint 🐾⚡️🌱 (@FlutterHeroes 2024)
Mind your App Footprint 🐾⚡️🌱 (@FlutterHeroes 2024)Mind your App Footprint 🐾⚡️🌱 (@FlutterHeroes 2024)
Mind your App Footprint 🐾⚡️🌱 (@FlutterHeroes 2024)
 
"Running Open-Source LLM models on Kubernetes", Volodymyr Tsap
"Running Open-Source LLM models on Kubernetes",  Volodymyr Tsap"Running Open-Source LLM models on Kubernetes",  Volodymyr Tsap
"Running Open-Source LLM models on Kubernetes", Volodymyr Tsap
 
Synergy in Leadership and Product Excellence: A Blueprint for Growth by CPO, ...
Synergy in Leadership and Product Excellence: A Blueprint for Growth by CPO, ...Synergy in Leadership and Product Excellence: A Blueprint for Growth by CPO, ...
Synergy in Leadership and Product Excellence: A Blueprint for Growth by CPO, ...
 
"Platform Engineering with Development Containers", Igor Fesenko
"Platform Engineering with Development Containers", Igor Fesenko"Platform Engineering with Development Containers", Igor Fesenko
"Platform Engineering with Development Containers", Igor Fesenko
 

Is it sensible to use Data Vault at all? Conclusions from a project.

  • 1. Is it sensible to use Data Vault at all? Conclusions from a project. Mainz, 15th March 2016 11. Oracle DWH Community Treffen Alexander Mendle (Insights & Data, Capgemini)
  • 2. Our customers’ business model. Project setup. Green field DW with 3 source systems – ERP, CMS, and a transaction system. Data Vault was preset by our customers group who also provided a Data Vault architect. Capgemini supported the project in implementation and testing.
  • 3. With over 11,000 professionals across 40+ countries …
  • 4. … and being part of a multi-faceted group … 180,000 employees(1) in more than 40 countries A promise that expresses our brand philosophy Revenues(2) €10.573 billion Operating margin €486 million Operating profit €447 million Net cash and cash equivalents €1,464 million 6 strategic alliances EMC2, HP, IBM, Microsoft, Oracle, SAP 7 values shared since the company’s creation in 1967 honesty/boldness/ trust/freedom/ team spirit/modesty/fun A wide range of cutting-edge expertise for all our clients Five strategic sectors Expertise in Automotive ,Banking Consumer Products & Retail Energy and Utilities Insurance A unique way 1 Headcount including IGATE 2 For the FY15-16
  • 5. Capgemini Insights & Data Services Model Copyright © Capgemini 2013. All Rights Reserved 5DATAVAULT IST DER EINSATZ VON DATA VAULT SINNVOLL-V10.PPTX Industry verticalization Automotive Consumer Products & Retail Public Sector Financial Services Telco Energy & Utilities Life Sciences Media & Entertainment Core capabilities and offers Data & Info Management Master Data Management Big Data (Hadoop/NoSQL) Optimized data warehouse EPM (Enterprise Performance Management) BI & Data Visualization Predictive + data science Real-time analytics Delivery models BI Service Center Cloud Application management Agile as-a-service & BPO IP Solutions Rapid prototyping/ POC Business engagement Strategic customer partnership Digital transformation Specific alliance initiatives Performance management & strategy Risk sharing Governance, architecture and strategy Data and information governance Architecture Privacy & security Information strategy Business Data Lake
  • 6. Copyright © Capgemini 2013. All Rights Reserved 6DATAVAULT IST DER EINSATZ VON DATA VAULT SINNVOLL-V10.PPTX Agenda  What is Data Vault? – just a quick glimpse  Impact on Architecture  Impact on Implementation  Impact on Project  Summary
  • 7. Data Vault is applicable in a multi-layer DWH architecture Copyright © Capgemini 2013. All Rights Reserved 7DATAVAULT IST DER EINSATZ VON DATA VAULT SINNVOLL-V10.PPTX Stage Data Mart(s) Core (mostly 3NF) Source: Linstedt: “Super Charge Your Data Warehouse”, o. V., o. O., ISBN: 978-0-9866757-1-3, 2010 Data Vault is mostly tied to its unique data modelling approach. However, as of its newest version it’s a comprehensive set of data modelling, project methodology and system architecture. Stage Data Mart(s) Data Vault Hub: List of buiness keys. Link: N:M relations between Hubs. Satellites: details for Hubs & Links (historized)
  • 8. The Data Vault proposition: agile, quick and cheap Copyright © Capgemini 2013. All Rights Reserved 8DATAVAULT IST DER EINSATZ VON DATA VAULT SINNVOLL-V10.PPTX That’s the proposed enhancements... Source: Linstedt, Olschimke: “Experiences from a Data Vault 2.0 pilot”, 13th European TDWI Conference, Munich, 2013 Reduction in Total Cost of Ownership More agility • Supports cross-functional areas of business • Near zero change impacts to existing system • Reduction in data acquisition costs • Reduction in maintenance costs over the life of the EDW • Reduction in implementation complexitiy • Compliance with full audits (“all the data, all of the time”) • Rapid turn-around for new requirements • Parallel teams – all agile • Scalable teams – with limited ramp-up necessary • Automated ETL generation based on patterns.
  • 9. Copyright © Capgemini 2013. All Rights Reserved 9DATAVAULT IST DER EINSATZ VON DATA VAULT SINNVOLL-V10.PPTX Agenda  What is Data Vault?  Impact on Architecture  Impact on Implementation  Impact on Project  Summary
  • 10. Architecture easily deals with changes Copyright © Capgemini 2013. All Rights Reserved 10DATAVAULT IST DER EINSATZ VON DATA VAULT SINNVOLL-V10.PPTX Existing structures are not changed – no matter if its just a new attribute or a whole new source system, changes in business or in semantics. New things are just added without side effects. Source: Linstedt: “Super Charge Your Data Warehouse”, o. V., o. O., ISBN: 978-0-9866757-1-3, 2010
  • 11. Architecture easily deals with changes Copyright © Capgemini 2013. All Rights Reserved 11DATAVAULT IST DER EINSATZ VON DATA VAULT SINNVOLL-V10.PPTX Existing structures are not changed – no matter if its just a new attribute or a whole new source system, changes in business or in semantics. New things are just added without side effects. Source: Linstedt: “Super Charge Your Data Warehouse”, o. V., o. O., ISBN: 978-0-9866757-1-3, 2010
  • 12. Copyright © Capgemini 2013. All Rights Reserved 12DATAVAULT IST DER EINSATZ VON DATA VAULT SINNVOLL-V10.PPTX Agenda  What is Data Vault?  Impact on Architecture  Impact on Implementation  Impact on Project  Summary
  • 13. Unique construction of tables enables industrialization Copyright © Capgemini 2013. All Rights Reserved 13DATAVAULT IST DER EINSATZ VON DATA VAULT SINNVOLL-V10.PPTX Hub tables Unique “list” of business keys And the scheduling: first the hubs, then hub satellites and links, then link satellites. No more dependencies. With Data Vault 2 this became even more flexible. Link tables Unique list of business key combinations Hub & Link satellite tables Attributes belonging to hub / link entity
  • 14. NumberofmanualETLs Numberoftables Effects of Data Vault on implementation Copyright © Capgemini 2013. All Rights Reserved 14DATAVAULT IST DER EINSATZ VON DATA VAULT SINNVOLL-V10.PPTX Quick implementation of Stage and Core using Generators – must-be as there is a vast number of objects Data Vault requires a huge number of objects, but allows highly industrialized implementation Stage | CORE | Marts Stage | CORE | Marts
  • 15. Layers Stage Layer Data Vault Layer Data Marts Considerations from an implementation viewpoint Does your staff support that? Copyright © Capgemini 2013. All Rights Reserved 15DATAVAULT IST DER EINSATZ VON DATA VAULT SINNVOLL-V10.PPTX What will happen when implementing.  This is business as usual.  You will need a strong architect who masters Data Vault, and also advocates Data Vault against developers not familiar with Data Vault. Be prepared for arguments.  Developers will use scripts, generators, configurations and will not build manual ETLs  Most time will be spent programming generators, a build toolchain, test automation  You might do here: 1) integrate data into context, 2) homogenize data and 3) realize the data mart requirements. Probably all at a time.  Watch out that you do not build things over and over again. So be sure to have a good understanding of the semantics of your business model in the team. Use Business Vault or other helpers.  Developers will implement lots of joins in their queries. A generator-based DW requires a more software-development oriented team – that’s probably a slightly different story than a “BI consultant” team. Do you have the team that bridges the gap?
  • 16. Copyright © Capgemini 2013. All Rights Reserved 16DATAVAULT IST DER EINSATZ VON DATA VAULT SINNVOLL-V10.PPTX Agenda  What is Data Vault?  Impact on Architecture  Impact on Implementation  Impact on Project  Summary
  • 17. Process for building Data Vault is straight forward, the CORE can be built quickly... Copyright © Capgemini 2013. All Rights Reserved 17DATAVAULT IST DER EINSATZ VON DATA VAULT SINNVOLL-V10.PPTX  Analysis of business as usual....  ...but not much efforts for design needed – Data Vault rules for modelling apply.  ETLs can be generated from only four different templates (Hubs, Links, Hub Satellites and Link Satellites) Data Vault can help you pick up pace with the CORE (raw Data Vault) Analysis Design ETL generation Loading
  • 18. The thing is not to easily store lots of things, it’s about how to retrieve information from it. Copyright © Capgemini 2013. All Rights Reserved 18DATAVAULT IST DER EINSATZ VON DATA VAULT SINNVOLL-V10.PPTX Data Vault is a paradigm change in many ways. Quick and large store. „All the data – all of the time“ Needs experience for retrieval High effort for systematic Semantics built-in
  • 19. ...but may thwart you building the marts Copyright © Capgemini 2013. All Rights Reserved 19DATAVAULT IST DER EINSATZ VON DATA VAULT SINNVOLL-V10.PPTX Data Vault may help you pick up pace – up to the CORE layer (i. e. “DataVault Layer”)  Analysis of business as usual but  ....no consideration of a “CORE” modelling because all Data Vault rules apply  “CORE” is done, but still  Lacking data homogenisation?  “business entities” (n:m)?  probably the proper analysis of all that?  On top of that, you need to (technically) design your datamart  Implementing all these complex rules in ETL  Quick setup of CORE model  Quick implementation for STAGE and CORE -- generated and automated build, test, rollout faster slower
  • 20. That was our plan looking at it in november Copyright © Capgemini 2013. All Rights Reserved 20DATAVAULT IST DER EINSATZ VON DATA VAULT SINNVOLL-V10.PPTX AugTimeline Sep Oct Nov Dec Jan Feb Mar Phase 2Phase Phase 1 Phase 2 DeliverablesPhase 1 Deliverables Mappings and workflows for  STAGE  RAW DATA VAULT Mappings and workflows for  MASTER and MASTER CHECK  BUSINESS VAULT  MART Deliver ables Staging and Raw DataVault went quite fast. Business rules AND requirements are implemented within Data Mart, whereas CORE can be generated! Make use of helpers such as Business Vault.
  • 21. Not the DW – the toolchain software is the deliverable Copyright © Capgemini 2013. All Rights Reserved 21DATAVAULT IST DER EINSATZ VON DATA VAULT SINNVOLL-V10.PPTX Do you have a need to write / request a special type of offer? Consider buying / offering a generator tool chain instead of tables and ETL programs. Customer Service Provider Consider writing your next RfP not as RfP for a DW – but as RfP for a generator software. Obtain control over the build tools in your project – the result is reproducible. Consider a bid offering the generator software. Your offer might look astonishingly compelling. Give your customer a good argumentation why to go for a generated solution – and refrain from it if you think there is no fit.
  • 22. Copyright © Capgemini 2013. All Rights Reserved 22DATAVAULT IST DER EINSATZ VON DATA VAULT SINNVOLL-V10.PPTX Agenda  What is Data Vault?  Impact on Architecture  Impact on Implementation  Impact on Project  Summary
  • 23. Layers Do I have a strong need to enable agile methodologies? Do I have the right people to support that? Is there a strong need for any special Data Vault characteristic? Summary Putting these aspects together with a decision focus Copyright © Capgemini 2013. All Rights Reserved 23DATAVAULT IST DER EINSATZ VON DATA VAULT SINNVOLL-V10.PPTX What will happen when implementing.  Data Vault enables you to integrate new source systems quickly and realize new requirements with minimal dependencies.  Data Vault requires somewhat different skills than a classic BI project  Data Vault is different in analysis and operation than a classic BI environment.  Are you able to bring a better understanding of your business into the (BI) team?  Are you really in need to be highly agile on CORE level?  Do you really need to have high traceability and / or auditability?  Which other possibilities do you have to realize your requirements? How is my budget and time situation?  Do you have a small, probably volatile and for future phases not overseeable budget for your BI initiative?  Are you in need to quickly obtain a consolidated and flexible data layer (CORE-DW vs Data Vault) Operative: Data Vault allows for automation, puts analysis in two points of architecture, allows agility Tactic: Data Vault can help you get as much budget through the door as there is. Strategic: Service Providers can build new business models with Data Vault – for CORE layer
  • 24. Contact information Alexander Mendle Consultant Insights & Data Alexander.Mendle@capgemini.com Capgemini Deutschland GmbH Olof-Palme-Str. 14 81829 München Insert contact picture Copyright © Capgemini 2013. All Rights Reserved 24DATAVAULT IST DER EINSATZ VON DATA VAULT SINNVOLL-V10.PPTX
  • 25. www.capgemini.com About Capgemini With more than 120,000 people in 40 countries, Capgemini is one of the world's foremost providers of consulting, technology and outsourcing services. The Group reported 2011 global revenues of EUR 9.7 billion. Together with its clients, Capgemini creates and delivers business and technology solutions that fit their needs and drive the results they want. A deeply multicultural organization, Capgemini has developed its own way of working, the Collaborative Business ExperienceTM, and draws on Rightshore ®, its worldwide delivery model. Rightshore® is a trademark belonging to Capgemini The information contained in this presentation is proprietary. Copyright © 2013 Capgemini. All rights reserved.
  • 26. Just a few Data Vault Tools Copyright © Capgemini 2013. All Rights Reserved 26DATAVAULT IST DER EINSATZ VON DATA VAULT SINNVOLL-V10.PPTX • Example: quipu – http://www.datawarehousemanagement.org/ • An engine to play around: https://sourceforge.net/projects/pdidatavaultfw/ (Linux, MySQL, Kettle, Excel-configurated)
  • 27. Pictures Copyright © Capgemini 2013. All Rights Reserved 27DATAVAULT IST DER EINSATZ VON DATA VAULT SINNVOLL-V10.PPTX • https://commons.wikimedia.org/wiki/File:Gao-report-on-interchange.gif, 13.3.16, Public domain • https://commons.wikimedia.org/wiki/File:KUKA_robot_for_flat_glas_handling.j pg, 9.3.16, Public domain • https://pixabay.com/de/b%C3%BCro-ordner-regal-fenster-firma-638247/, 9.3.16, Public domain • https://www.flickr.com/photos/nationalsecurityzone/8552562622/in/photostrea m/, 9.3.16, https://creativecommons.org/licenses/by/2.0/, By: MedillNSZ (https://www.flickr.com/photos/nationalsecurityzone/) • https://commons.wikimedia.org/wiki/File:2010.07.21.152950_Abf%C3%BCllan lage_Gerolstein.jpg, 9.3.16, By Hermann Luyken (Own work) [Public domain], via Wikimedia Commons • https://commons.wikimedia.org/wiki/File:Euplectes_progne_male_South_Afric a_cropped.jpg, 9.3.16, Public domain Refer to these Websites for more information.