The document outlines Q-Perior's approach to implementing the Denodo platform for data virtualization, highlighting key considerations such as technology, business user needs, and organizational maturity. It details various challenges encountered during client projects—technical, business, and organizational—and emphasizes the advantages of data virtualization like real-time analytics and self-service capabilities. Q-Perior positions data virtualization as a 'data factory for business,' offering services to streamline data management and reporting processes.

1. Logical Data Fabric: The Future of Data
Management and Analytics

2. #DenodoDataFest
Key Considerations While Rolling Out
Denodo Platform
Associate Partner at Q-PERIOR
Ulrich Gantenbein

3. © Q_PERIOR | Page 3
© Q_PERIOR
Data Intelligence
Q-PERIOR
Your partner for
Data Intelligence
PROACTIVE, DYNAMIC AND PERSONAL

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Figures, Facts, Data
As a leading business and IT consulting company, Q_PERIOR operates successfully around the world.
OFFICES WORLDWIDE
16
MUNICH / HAMBURG / FRANKFURT /
STUTTGART / INGOLSTADT / NUREMBERG /
FREIBURG / ROSENHEIM /
VIENNA / ZURICH / BERN /
SARAJEVO / CLUJ-NAPOCA /
NEW YORK / TORONTO / LONDON
32
225
REVENUE 2020 [MIO. €] NATIONS OF EMPLOYEES
5%
REVENUE INCREASE 2020 CONSULTANTS
>1.250
CUSTOMER PROJECTS
>6.000
€

5. © Q_PERIOR | Page 5
Q-PERIOR Approach
For best results, Data Virtualization should not take technology only into consideration
TECHNOLOGY
BUSINESS USER GROUPS
ORGANIZATION
Maturity/Capability
Use Case Repository
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DATA
VIRTUALIZATION
Our approach for data virtualization advisory
takes the following 3 dimensions/areas into
consideration:
• Technology / Architecture
• Business User Groups (your clients)
• Organization (incl. the operations model)

6. © Q_PERIOR | Page 6
Accelerators
To deliver quick results, Q-PERIOR uses a multitude of accelerators in predefined topics
within these 3 dimensions.
Dimension Field of action
Organization
Data Governance
Framework
TOM Build-- Run- and
Maintenance model
Change Management
Framework
Role and Competence
model
Organization- & Processes
Technology
Logical Enterprise Data
Warehouse
Self-Service Framework
Data Virtualization
Reference- Target- &
Solution Architecture
Software Assessment
Framework
Evaluation & installation
of platform
(SaaS/PaaS/Open Source)
Partnerships and
Certifications
Business User
Groups
Self-Service Maturity
Assessment
Business Capability Maps
Use Case Repository Role & competence
models
Acceptance assessments Change Mgmt. & Training

7. © Q_PERIOR | Page 7
Examples from client projects with Denodo
Technical challenges – some examples
• Performance
o Optimized SQL statements for the whole data flow
o Data modeling / guidelines for Denodo and the whole data flow from source- to consumer system
o Workarounds for source systems not forcefully perfect to be directly connected to Denodo
• Integration of SAP systems
o There are a lot of different SAP sources systems (BW with or without in-memory DB, ERP systems..)
o Which layer of SAP can / should you access?
o Specific development in source system mostly required
Dimension Field of action
Technology
Logical Enterprise Data
Warehouse
Self-Service Framework
Data Virtualization
Reference- Target- &
Solution Architecture
Software Assessment
Framework
Evaluation & installation
of platform
(SaaS/PaaS/Open Source)
Partnerships and
Certifications

8. © Q_PERIOR | Page 8
Examples from client projects with Denodo
Business challenges – some examples
• Exchange with business stakeholders is often a real challenge but nevertheless extremely important
• Authorization / Data Ownership especially for ERP- sources and multi-sources
• Denodo consumption layer / interface for different user groups (different business user groups, data
scientists, etc.)
• Role- & competence models 🡪 Self-Service maturity & acceptance
• Acceptance & skills of business user groups 🡪 Change Management / Training
Dimension Field of action
Business User
Groups
Self-Service Maturity
Assessment
Business Capability Maps
Use Case Repository Role & competence
models
Acceptance assessments Change Mgmt. & Training

9. © Q_PERIOR | Page 9
Examples from client projects with Denodo
Organizational challenges – some examples
• Use Cases with Denodo are often realized in order to achieve a higher self-service level:
o to avoid the well know IT-Bottleneck
o to answer the requirements of a data driven organization
• Challenges arise in the IT- as well as in the Business- organization (struc. & processes):
o Where does the responsibility for the system and data end for IT and where does it start for business user groups?
o What is the IT organization capable and willing to deliver (level of self-service has always 2 sides)?
o How can organizational processes be adapted to the paradigm change with data virtualization (Denodo)?
Dimension Field of action
Organization
Data Governance
Framework
TOM Build-- Run- and
Maintenance model
Change Management
Framework
Role and Competence
model
Organization- & Processes

10. 10
Fast connection and merging
of various data sources works
well with Denodo .

11. 11
Denodo has proven
itself as a central
authorization layer

12. Uwe Raetz | 19 November 2019 | Risk Management IT12
We would like to provide
Information as a Service
and focus on creation of
business value.

13. Denodo is the key enabler
for our architecture and
became our Swiss
army-knife for data
integration.

14. © Q_PERIOR | Page 14
Q_PERIOR AG
Our top services for your success
Ulrich Gantenbein
Associate Partner
Lead Data Intelligence
Q-PERIOR Office: 031 310 07 00
Hohlstrasse 614 Mobile: 079 287 49 28
8048 Zürich ulrich.gantenbein@q-perior.com
Switzerland www.q-perior.com
.

15. © Copyright Denodo Technologies. All rights reserved
Unless otherwise specified, no part of this PDF file may be reproduced or utilized in
any for or by any means, electronic or mechanical, including photocopying and
microfilm, without prior the written authorization from Denodo Technologies.
Thank You!

16. © Q_PERIOR | Page 16
Backup
Backup slides
Backup

17. © Q_PERIOR | Page 17
Hands-On
Advisory
▪ Data driven
Transformation
▪ Data processes &
organization
▪ Self-service
▪ Data management
▪ Hybrid data
architecture
1
Data Intelligence Services
Services along the data flow – both conceptual as well as hands-on!
structured data unstructured data
• Data sources
• Data integration
• Data warehousing, data
lakes , data lakehouse &
data modelling
• Reporting & analytics
• Enterprise performance
management
• Data virtualization
1
1
1
1
Your
Benefits
Our
Services
• From data to information to
corporate values
• Lead through data intelligence
(also in times of crisis)
• Data-driven corporate mgmt.
• Driver-based simulations
• AI & automation
Revenue by Region
57.5 M CHF
OKR Attainment
Revenue increase segment A by 10%
2.48 M CHF (17%)
Shop Visitors
Ø visitors per day
42
Monthly Sales Evolution
1.065 M CHF

18. © Q_PERIOR | Page 18
Data Intelligence Services
Focus Area: Data Virtualization / Data Factory 4 Business – THE missing link
• Data Sources
• Data Integration
Your Benefits
▪ Time to Market (3-4 times faster than classical ETL)
▪ Self-Service „on-demand“
▪ Data Integration for the Business
▪ Realtime Reporting & Analytics
Data Factory for the Business
Closing THE missing link in traditional data
architecture
Our Services in
Data Virtualization:
▪ Use Case Moderation
▪ Target- and Solution Architecture
▪ Software Evaluation
▪ Functional and technical Design
▪ Implementation (Hands-on)
Hands-on Know-How in:
✔ Denodo
✔ BO Universe
✔ Microsoft Azure
✔ SAP Cloud Data Warehouse
Hands-On
Advisory
1
Structured Data Unstructured Data
1
1
1
• Reporting & Analytics
• Enterprise Performance
Management
• Data Virtualization 🡪
Data Factory 4 the Business
1
• Invest your time in self-service
reporting & analytics instead of
data collection & data integration
• Self-Service on business demand
• Avoidance of data redundancy
• Virtual & central data catalogue
• Killer of the IT-bottleneck
• Do-it-yourself (DIY) reporting &
analytics
Umsatz nach Region
57.5 M CHF
Zielerreichung OKR
Umsatzsteigerung Segment A um 10%
2.48 M CHF (17%)
DIY
▪ Data-maturity
assessment
▪ Evaluation of
business capabilities
▪ Use case moderation
▪ Conception of target-
& solution
architecture
▪ Software evaluation
▪ Mapping of business
capabilities with tech.
building blocks
▪ Guidelines for data
virtualization
▪ Optimization of data
& analytics
organization
1
• Data warehousing, data lakes,
data lakehouse, data-modeling

19. © Q_PERIOR | Page 19
Q-PERIOR’s services for Data Virtualization
Systematic approach from target- & solution architecture to implementing use cases
If no uses cases have
been defined yet, we
help you developing
and documenting
them in structured
workshops along tbd
criteria's (e.g.
business value, time
to market etc.) to
prioritize them for the
implementation
Use Case
Moderation
Preparation and
moderation of a
selection of adequate
virtualization software
packages, based on
the target- and
solution architecture
and the identified and
prioritized use cases
Software
Evaluation
Integrating / connecting
the evaluated virtualization
software into an existing
application landscape by
including / connecting
existing technical
components (data sources
& BI self-service tools ) as
well as security &
compliance components
Plattform-Integra
tion
Adjust Q-PERIOR
reference architecture
framework to the client
specific needs.
Mapping of existing
technical components of
the client’s infrastructure
with use case categories
to identify potential gaps
or obsolete applications
in client’s infrastructure
🡪 solution architecture
Target- & Solution
Architecture
Phased approach for the
technical implementation
of defined use cases
following the proven
principal
«start small and grow»
An iterative / agile
approach as well as a
classical approach are
possible
Use Case
Implementation
Hands-On Hands-On

20. © Q_PERIOR | Page 20
References (selection)
Added Value
Definition
Services
Data Virtualization can be seen as the data factory of the business. Instead of
replicating and loading different data sources of structured & unstructured data
traditionally via ETL technology into data silos for the business, this approach
keeps the data in the sources and provides its data virtually to the business.
In other words – data virtualization allows a virtual enterprise data warehouse
with many advantages compared to traditional data warehouses and data lakes
approaches
Among other added values this approach allows real self-service for the
business and that’s why we call it data factory for the business
▪ „Time to Market“ (3-4 times faster than
classical ETL
▪ Self-Service „on-demand“
▪ Data Integration for the business
▪ Realtime Reporting & Analytics
Data factory for the Business
▪ Target- & solution architecture
▪ Use Case Moderation
▪ Software Evaluation
▪ Functional & technical design
▪ Platform Integration (Hands-on)
▪ Implementation (Hands-on) with:
▪ Denodo
▪ BusinessObjects Universe
▪ Microsoft Azure
▪ SAP Cloud Data Warehouse
Finance – Data Management/ Data Virtualization
Concept framework and implementation know-how for Data Virtualization
Logical data architecture framework for data virtualization by Q-PERIOR

21. © Q_PERIOR | Page 21
Data Provider
(Source-Systems) Data Virtualization
Q_PERIOR Data Intelligence reference architecture framework (logical data architecture)
Main
Systems
Support
Systems
Structured
Data
External
Sources
Internal
External
Unstructured
Data
Data Integration
Batch-Extraction
Replication (24h)
Events
(< 1 Min)
Streaming
(continuously)
Virtual Access
Referencing
Raw Data/Data
Lake/ Data
Lakehouse
Analytics Exploration
(Analytics Platforms&
Modeling Tools,
“Sandbox”-Principal)
ERP 1
DWH 1
DWH n
ERP 2
Data Storage
Query Push
Down
Data Semantic &
Metadata
Data Publishing
Domains
(industry spec.)
Reporting / Advanced Analytics Layer
Data Preparation
Visualization &
Planning
Management
Operational
Regulatory
Ad-Hoc
……
Reporting
Planning
Controlling
Finance
Sales
Procurement
……..
Analytics
Business Prozess
Integration
Customer
Predictive
Supply Chain
Maintenance
……..
Operations
Management
Compliance,
Regulatoren
Enterprise
Development
Product
Management
EAM
(Enterprise
Asset Mgmt.)
Sales &
Marketing
Risk-Managem
ent
IT Applications / Technology
Business Applications
Informations- & Data Management
Organization & Processes
ERP n
DWH 2
Data
Combination
Data Cashing
Data Maintenance / Responsibilities | Reference- / Meta-Data-Mgmt. | Data Security | Data Protection / Compliance | Data Quality | Data Authorization | Data Life Cycle Mgmt. (Data Aging)
Organization (Structure & Processes| Org.- &. Delivery Models | Support & Maintenance | Knowledge Mgmt. | Project Portfolio | Platform Strategy (on Premise / Cloud) | Product Life Cycle Management
Data
Wrangling
Data
Stewardship
Data
Transformation
Data
Profiling
Data
Integration
Data
Exploration
Semantic &
Metadata
virtual data factory
Intelligent Data
Handling
Data Security

22. © Q_PERIOR | Page 22
Data Producer Data Consumer Business Layer
virtualization
Reporting / Advanced Analytics Layer
Oracle
eBS
Reports
Edge Compute
IoT
Amazon S3
• Governance
• Usage to Cost Compare
Snowpipe
Example no. 1 for a data architecture with data virtualization

23. © Q_PERIOR | Page 23
Example no. 2 for a data architecture with data virtualization

24. © Copyright Denodo Technologies. All rights reserved
Unless otherwise specified, no part of this PDF file may be reproduced or utilized in
any for or by any means, electronic or mechanical, including photocopying and
microfilm, without prior the written authorization from Denodo Technologies.
Thank You!