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BASEL BERN BRUGG DÜSSELDORF FRANKFURT A.M. FREIBURG I.BR. GENF
HAMBURG KOPENHAGEN LAUSANNE MÜNCHEN STUTTGART WIEN ZÜRICH
D...
The “Yellowphant” is blowing it all away!
DWH Modernization09.09.2016
Hadooooop!!
Traditional
BI & DWH
2
The future belongs to data
4th Industrial Revolution
Digital Transformation
Traditional IT
09.09.2016 DWH Modernization3
DWH Modernization09.09.2016
Traditional
Business
Intelligence
DW
Moderni-
zation
Big Data &
Data Science
IoT
EVERYTHING AS...
DWH Modernization09.09.2016
Traditional
Business
Intelligence
DW
Moderni-
zation
Big Data &
Data Science
IoT
EVERYTHING AS...
Technical Driver
Leading Driver for DW Modernization
DWH Modernization09.09.2016
Source: Data Warehouse Modernization, Bes...
Expectations on modernized DW-Solutions
DWH Modernization09.09.2016
Business
Source: Data Warehouse Modernization, Best Pr...
Typical Starting Position for Modernization
DWH Modernization8 09.09.2016
DM
Marketin
g
Kern-DWH
Staging Area
DM
Vertrieb
...
Scope Modernization of BI/DWH Modernization
DWH Modernization9 09.09.2016
Modern Analytical Data Management Solutions (DWE)
DWH Modernization10 09.09.2016
Data Access
Metadata, Governance, Data Qu...
Landscape for Analytical Data Management Solutions
DWH Modernization11 09.09.2016
Data
Acquisition
Data
Sources
Governance...
„Classical DWH“ based on analytical Landscape
DWH Modernization12 09.09.2016
Data
Acquisition
Data
Sources
Governance
Orga...
Data
Acquisition
Data
Sources
Governance
Organisation
Information
Provisioning Consumer
Data
Management
Streaming Data bas...
BI/DWH Strategy for the next 3 years
DWH Modernization14 09.09.2016
decrease
increase
Quelle: Data Warehouse Modernization...
BI/DWH Strategy for the next 3 years
DWH Modernization15 09.09.2016
Quelle: Data Warehouse Modernization, Best Practices R...
BI/DWH Strategy for the next 3 years
DWH Modernization16 09.09.2016
57% replace
Quelle: Data Warehouse Modernization, Best...
Greenfield
Status Quo
R 
E 
B 
Feature Extension
R 
E 
B 
Partial renewal
Modernization
R 
E 
B 
Functional
Moder...
Architectures
Features Technology
Scalability
Automation
DataOrganization
Expenses
Cloud, Services,
Elastic-DWH …
Data Vau...
Route to modern Data Warehouse Environments
DWH Modernization19 09.09.2016
DWE Vision
Target Solution
Roadmap to Vision
Ex...
World of 2 velocities
DWH Modernization20 09.09.2016
Traditional BI/DWH Big Data & Data Science
Conclusion
DWH Modernization21 09.09.2016
Digital Business drives DWH Modernization
Enhance the scope to Data Warehouse En...
This and other Questions…
DWH Modernization22 09.09.2016
Fragen und Antworten …
Gregor Zeiler
Senior Solution Manager
gregor.zeiler@trivadis.com
09.09.2016 DWH Modernization23
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Trivadis TechEvent 2016 DWH Modernization – in the Age of Big Data by Gregor Zeiler

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Dispositive Architekturen sind in vielen Unternehmen über die Zeit organisch gewachsen, wartungsintensiv und nur mit hohem Aufwand zu erweitern. Aktuelle Entwicklungen wie z.B. Bimodale IT / BI, Big Data und Digitalisierung stellen weitere Anforderungen an analytische Datenmanagement Lösungen und beschleunigen zusätzlich den Änderungsbedarf. Der Vortrag beleuchtet, welche Aspekte bei der Modernisierung fachlich, technisch und organisatorisch zu berücksichtigen sind, welche Zielkonflikte zu managen sind und welche Potentiale sich für weitere Nutzung ergeben.

Veröffentlicht in: Daten & Analysen
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Trivadis TechEvent 2016 DWH Modernization – in the Age of Big Data by Gregor Zeiler

  1. 1. BASEL BERN BRUGG DÜSSELDORF FRANKFURT A.M. FREIBURG I.BR. GENF HAMBURG KOPENHAGEN LAUSANNE MÜNCHEN STUTTGART WIEN ZÜRICH DWH Modernization – in the Age of Big Data Gregor Zeiler Senior Solution Manager BI/Big Data @GregorZeiler
  2. 2. The “Yellowphant” is blowing it all away! DWH Modernization09.09.2016 Hadooooop!! Traditional BI & DWH 2
  3. 3. The future belongs to data 4th Industrial Revolution Digital Transformation Traditional IT 09.09.2016 DWH Modernization3
  4. 4. DWH Modernization09.09.2016 Traditional Business Intelligence DW Moderni- zation Big Data & Data Science IoT EVERYTHING AS A SERVICE CLOUD COMPUTING 4
  5. 5. DWH Modernization09.09.2016 Traditional Business Intelligence DW Moderni- zation Big Data & Data Science IoT EVERYTHING AS A SERVICE CLOUD COMPUTING Traditional Business Digital Business 5
  6. 6. Technical Driver Leading Driver for DW Modernization DWH Modernization09.09.2016 Source: Data Warehouse Modernization, Best Practices Report, Q2/2016 6 Business Driver Business alignment Modern practices for Analytics, … Data Lake, Data Vault, Hadoop, …
  7. 7. Expectations on modernized DW-Solutions DWH Modernization09.09.2016 Business Source: Data Warehouse Modernization, Best Practices Report, Q2/2016 Analytics, Exploration, Better decision making Operational efficiency Technology Agility, Maintenance, Automation 7
  8. 8. Typical Starting Position for Modernization DWH Modernization8 09.09.2016 DM Marketin g Kern-DWH Staging Area DM Vertrieb neu DWH Post Merger Datenpool Ad-hoc DM Bestand DM Abschlus s DM Vers.tec h. DM Finanzen DM Anpassu ng OLAP Finanzen OLAP Vertrieb DM Vertrieb Vertriebs- Reporting Reporting Finanzen OLAPVertrieb-Ad- hoc-Analysen OLAP Marketing Finanzen-Ad- hoc-Analysen Meldewesen Datenextrakte (PE., Bilanz, etc.) Over several years grown Data Warehouse Solution which does not meet both current and upcoming requirements (66% DW Projects have started before 10 years) Source: Data Warehouse Modernisierung – Auslöser, Stoßrichtungen und Potenziale (Erik Purwins, Gregor Zeiler)
  9. 9. Scope Modernization of BI/DWH Modernization DWH Modernization9 09.09.2016
  10. 10. Modern Analytical Data Management Solutions (DWE) DWH Modernization10 09.09.2016 Data Access Metadata, Governance, Data QualityGovernance, Data Quality Meta Data Service Decision Layer RELIABLE DATABASE FEXIBILITY AGILITY VARIABILITY ROW-DATA „Classic“ DWH Federation Virtualization Big Data Analytics The traditional data warehouse has been overwhelmed by analytic demands, giving rise to the logical data warehouse as a best-in-class architecture for a new style of data management solutions for analytics. © Gartner: 2014, Analyst(s): Mark A. Beyer, Roxane Edjlali
  11. 11. Landscape for Analytical Data Management Solutions DWH Modernization11 09.09.2016 Data Acquisition Data Sources Governance Organisation Information Provisioning Consumer Data Management Legal ComplianceQuality & Accountability SecurityMetadata Management Master Data Management IT Operations Business StakeholdersBI Competence Center Un-/Semi- structured Data Structured Data Master & Reference Data Machine Data Content Services(Push)Connectors(Pull) StreamBatch/Bulk IncrementalFull Raw Data at Rest Standardized Data at Rest Optimized Data at Rest Data Lab (Sandbox) Data Refinery/Factory Virtualization Raw Data in Motion Standardized Data in Motion Optimized Data in Motion Query Service / API Search Information Services Data Science Tools Dashboard Prebuild & AdHoc BI Assets Advanced Analysis Tools
  12. 12. „Classical DWH“ based on analytical Landscape DWH Modernization12 09.09.2016 Data Acquisition Data Sources Governance Organisation Information Provisioning Consumer Data Management Legal ComplianceQuality & Accountability SecurityMetadata Management Master Data Management IT Operations Business StakeholdersBI Competence Center Un-/Semi- structured Data Structured Data Master & Reference Data Machine Data Content Services(Push)Connectors(Pull) StreamBatch/Bulk IncrementalFull Raw Data at Rest Standardized Data at Rest Optimized Data at Rest Data Lab (Sandbox) Data Refinery/Factory Virtualization Raw Data in Motion Standardized Data in Motion Optimized Data in Motion Query Service / API Search Information Services Data Science Tools Dashboard Prebuild & AdHoc BI Assets Advanced Analysis Tools Core DWH Data Marts Staging Area ETL
  13. 13. Data Acquisition Data Sources Governance Organisation Information Provisioning Consumer Data Management Streaming Data based on analytical Landscape DWH Modernization13 09.09.2016 Legal ComplianceQuality & Accountability SecurityMetadata Management Master Data Management IT Operations Business StakeholdersBI Competence Center Un-/Semi- structured Data Structured Data Master & Reference Data Machine Data Content Services(Push)Connectors(Pull) StreamBatch/Bulk IncrementalFull Raw Data at Rest Standardized Data at Rest Optimized Data at Rest Data Lab (Sandbox) Data Refinery/Factory Merge Layer Raw Data in Motion Standardized Data in Motion Optimized Data in Motion Query Service / API Search Information Services Data Science Tools Dashboard Prebuild & AdHoc BI Assets Advanced Analysis Tools Event Hub Stream Analytics Hadoop Raw Data Processed Files NoSQL DB SQL Engine
  14. 14. BI/DWH Strategy for the next 3 years DWH Modernization14 09.09.2016 decrease increase Quelle: Data Warehouse Modernization, Best Practices Report, Q2/2016, Philip Russom .
  15. 15. BI/DWH Strategy for the next 3 years DWH Modernization15 09.09.2016 Quelle: Data Warehouse Modernization, Best Practices Report, Q2/2016, Philip Russom . 50% combine 6% replace
  16. 16. BI/DWH Strategy for the next 3 years DWH Modernization16 09.09.2016 57% replace Quelle: Data Warehouse Modernization, Best Practices Report, Q2/2016, Philip Russom .
  17. 17. Greenfield Status Quo R  E  B  Feature Extension R  E  B  Partial renewal Modernization R  E  B  Functional Modernization R  E  B  Possible Modernization Strategies DWH Modernization17 09.09.2016 44% Disruptive Modernization R  A  B  Performance Opt. R  E  B  Re- Platforming 21% 42% 47% Data Lab Data Lake, Hadoop Source of information: tdwi Best Practices Report Q2/2016 DWH Modernization ..% Percentage of selected Modernization Strategy. Multiple Choices possible. … Sample Modernization Approaches Legende: R…Risc E…Effort B…Benefit …high …medium …low RenewalReengineering Replacement ExtensionStatusQuoExtended Data Vault System Mod. R  E  B  53% 42% with Extension- Strategy 58% with Renewal- Strategy
  18. 18. Architectures Features Technology Scalability Automation DataOrganization Expenses Cloud, Services, Elastic-DWH … Data Vault (DW Core) EDWH  LDW Big Data … Advanced Analytics, Self-Service, … Hadoop, NoSQL InMemory… Development-, Change- Automation … New Data-sources/-types Quality/Security… Governance, Compliance Processes, Skills… Dev./Operating CAPEX/OPEX… BI/DWH Modernization areas DWH Modernization18 09.09.2016 DWE DW EDWH, Silos, …
  19. 19. Route to modern Data Warehouse Environments DWH Modernization19 09.09.2016 DWE Vision Target Solution Roadmap to Vision Existing Problems and Pain Points Upcoming Requirements Modern Analytical Architectures Existing DWH Solution
  20. 20. World of 2 velocities DWH Modernization20 09.09.2016 Traditional BI/DWH Big Data & Data Science
  21. 21. Conclusion DWH Modernization21 09.09.2016 Digital Business drives DWH Modernization Enhance the scope to Data Warehouse Environments Design your Architecture by Pains and Needs not primarily by Technology Choose a suitable Modernization Strategy - be spunky Be aware of the two velocities
  22. 22. This and other Questions… DWH Modernization22 09.09.2016
  23. 23. Fragen und Antworten … Gregor Zeiler Senior Solution Manager gregor.zeiler@trivadis.com 09.09.2016 DWH Modernization23

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