This document discusses data virtualization and its benefits for business users. It summarizes that data virtualization can create a connected data landscape that is easily shared, empower business users with self-service BI tools, develop trusted high quality data, and support flexibility. It notes data virtualization provides a logical data layer that improves decision making, broadens data usage, and offers performant access to integrated data without moving or replicating source systems.

1. Data Virtualisation for Business Consumption
November 2020

2. Enabling digital
transformation by
connecting expression,
experience and
enablement
600+
Consultants
100+
Active
Clients
ASX
Listed
Company
4
Australian
Locations

3. EnablementExperienceExpression
RXP Group on a page
MAKING HAPPIER HUMANS
Fusing brand, insight, design and technology to help organisations transform and innovate
STRATEGY & ADVISORY
EXPERIENCE & DESIGN
DATA & INSIGHTS
DIGITAL PLATFORMS
▪ Growth Strategy
▪ Brand Strategy
▪ Digital Transformation
▪ Human Centered Design
▪ User Experience
▪ Customer Experience
▪ Digital Development
▪ Service Design
▪ Program Management
▪ Project Management
▪ Business Analysis
▪ Organisational Change Management
▪ Quality Assurance/ Testing
▪ PMO Support
▪ Agile Delivery
▪ CRM
▪ ESM
▪ Web & Mobile Development
▪ Development
▪ Cloud & DevOps
▪ Microservices
▪ Test Automation
BRAND & CONTENT PROJECT SERVICES
▪ Brand & CX Strategy
▪ Advertising & Digital
Marketing
▪ Content Marketing
▪ Social Media
▪ Data Driven Marketing
▪ Brand Innovation
▪ Content Production
▪ Data Strategy
▪ Integration and API’s
▪ Data Governance
▪ Business Intelligence
▪ Visualisation
▪ Insights & Analytics
▪ Data Science and AI
MANAGED SERVICES
▪ System Support
▪ Incident Management
▪ Defect Management
▪ Release Management
▪ Platform Integration
▪ Problem Management

4. We improve business intelligence,
so that people can make better decisions
DATA & INSIGHTS
100+
Consultants
4
Locations
2
Data Centre
of Excellence’s
(CoE’s) created
6
Top technology
partnerships
RXP Insight
We combine agile methodologies and technology deployments that
enable data consumers to uncover patterns and insights within data
sets and easily automate repetitive processes to allow focus on data
discovery.
We specialise on end-to-end Information Management ecosystems
and we provide consistent methodologies and best practice expertise
across the entire data lifecycle:
▪ Data Strategy and Governance
▪ Data Modelling and Storage
▪ Integration & APIs
▪ Analytics & Visualisation
▪ AI, ML & NLP

5. RXP Data Management Framework
End-to-end Information Management framework for the enterprise data lifecycle.

6. Data Management Strategy on a Page
Mission: Expand and leverage our data capabilities to help customers achieve their personal best financial outcome, and strengthen our position as a
financial provider of choice.
Current State of Data
• Data Management maturity score =
3.6; Quality = 4.0
• Data personalises Customer &
Supplier experiences
• Data is trusted as it is easy to
access, always verified and
explained
• Data is consistently governed,
timely and accessible for business
users at speed and with transparent
rules.
• Real-time evidence of value for
each Customer
• Machine learning drives insights
and decisions
• Data refines automated assurance
processes
• Data drives innovation
• Our data ecosystem empowers and
protects external partner
collaboration
Top Data Management Initiatives:
Data Governance program
• Phase 1: Establish structures and processes; Tool evaluation
• Phase 2: Tool selection & implementation
Data Quality program implementation
Data Management platform implementation
• Augmentation of ingested data
• Data segmentation & self service capabilities for business users
Master Data Management
• 360 degree views of Customer and Product
Underlying Principles:
1.Data is an asset to be valued, governed, secured, maintained, certified and leveraged.
2.Every data asset will be owned by a business unit accountable for its governance.
3.Decisions on shared data will be centralised.
4.Our enterprise data architecture will be a cloud first, component and standards-based platform with
architectural control centrally maintained and enforced.
5.Solution sourcing will apply the principles of ‘reuse before buy’ and ‘buy before build’.
6.A transparent and risk-based approach will underpin the regulatory compliance of our data processes and
management of data risks.
7.Business users will be primarily responsible for self-service access to and routine analysis of data.
8.Data stored by our systems will by default be connected to analytic processes, not replicated in a data
warehouse.
9.A balanced approach will be adopted to managing data transformation and operational support.
10.Business processes will be digitised, with performance data captured & analysed.
• Data Management maturity
score = 1.8 (of 5)
• The majority of available data
assets are being collected
and utilised.
• Data assets are not being
consistently defined or
governed.
• Data Quality not owned or
measured. Maturity score =
1.1
• Data assets are difficult for
business users to access
directly.
• Data ingestion and
transformation into a useable
business data model is slow.
• Non-traditional streaming
and unstructured data
sources are under-utilised.
State of Data in 2022
Customer example #1

7. Data Management Strategy on a Page
Mission: Expand and leverage our data capabilities to help customers achieve their personal best financial outcome, and strengthen our position as a
financial provider of choice.
Current State of Data
• Data Management maturity score =
3.6; Quality = 4.0
• Data personalises Customer &
Supplier experiences
• Data is trusted as it is easy to
access, always verified and
explained
• Data is consistently governed,
timely and accessible for business
users at speed and with transparent
rules.
• Real-time evidence of value for
each Customer
• Machine learning drives insights
and decisions
• Data refines automated assurance
processes
• Data drives innovation
• Our data ecosystem empowers and
protects external partner
collaboration
Top Data Management Initiatives:
Data Governance program
• Phase 1: Establish structures and processes; Tool evaluation
• Phase 2: Tool selection & implementation
Data Quality program implementation
Data Management platform implementation
• Augmentation of ingested data
• Data segmentation & self service capabilities for business users
Master Data Management
• 360 degree views of Customer and Product
Underlying Principles:
1.Data is an asset to be valued, governed, secured, maintained, certified and leveraged.
2.Every data asset will be owned by a business unit accountable for its governance.
3.Decisions on shared data will be centralised.
4.Our enterprise data architecture will be a cloud first, component and standards-based platform with
architectural control centrally maintained and enforced.
5.Solution sourcing will apply the principles of ‘reuse before buy’ and ‘buy before build’.
6.A transparent and risk-based approach will underpin the regulatory compliance of our data processes and
management of data risks.
7.Business users will be primarily responsible for self-service access to and routine analysis of data.
8.Data stored by our systems will by default be connected to analytic processes, not replicated in a data
warehouse.
9.A balanced approach will be adopted to managing data transformation and operational support.
10.Business processes will be digitised, with performance data captured & analysed.
• Data Management maturity
score = 1.8 (of 5)
• The majority of available data
assets are being collected
and utilised.
• Data assets are not being
consistently defined or
governed.
• Data Quality not owned or
measured. Maturity score =
1.1
• Data assets are difficult for
business users to access
directly.
• Data ingestion and
transformation into a useable
business data model is slow.
• Non-traditional streaming
and unstructured data
sources are under-utilised.
State of Data in 2022
Data Virtualisation is
relevant in these areas

8. Strategy, Goals & Objectives
Key Objectives
• Create a connected data landscape that is easily shared by
users in all business units.
• Empower business users to identify appropriate data and
have access to self-services Business Intelligence tools for
interacting with that data.
• Develop a state of data which is trusted and of
demonstrably high quality.
• Support decisions to change technology infrastructure
arrangements or modernise line-of-business applications by
creating a flexible data management architecture that
facilitates change without impeding business performance.
• Provide flexibility, resilience and rapid response to change
in external data interactions.
• Creation of an organisational ethos that, by valuing data as
an enabler, is continuously testing, learning and improving
all aspects of its operations.
Trust: data is well defined, with its provenance (lineage)
described, and has a senior business owner responsible for its
appropriate governance.
Data Literacy: data definitions are unambiguous and
universally accessible.
Quality: data is complete, validated against a trusted source
and subject to continuous monitoring and improvement.
Security: data is classified and secured at all times, with non-
intrusive controls and monitoring in place for data access,
sharing and breach reporting.
Data Democratisation: data can be accessed, researched &
analysed in a self-service environment.
Time to Value: accelerate the time to business value of new
data & analytic initiatives.
Time Value of Data: using data while it retains its maximum
impact or commercial value. Data history is also retained and
accessible when it has business relevance.
Data Enrichment: existing data sets can easily be augmented.
Data Goals & Characteristicsachieved through enabling
Customer Specific:
Removed in this presentation
Business Strategy
Customer example #2

9. Strategy, Goals & Objectives
Data Virtualisation is
relevant in these areas
Key Objectives
• Create a connected data landscape that is easily shared by
users in all business units.
• Empower business users to identify appropriate data and
have access to self-services Business Intelligence tools for
interacting with that data.
• Develop a state of data which is trusted and of
demonstrably high quality.
• Support decisions to change technology infrastructure
arrangements or modernise line-of-business applications by
creating a flexible data management architecture that
facilitates change without impeding business performance.
• Provide flexibility, resilience and rapid response to change
in external data interactions.
• Creation of an organisational ethos that, by valuing data as
an enabler, is continuously testing, learning and improving
all aspects of its operations.
Trust: data is well defined, with its provenance (lineage)
described, and has a senior business owner responsible for its
appropriate governance.
Data Literacy: data definitions are unambiguous and
universally accessible.
Quality: data is complete, validated against a trusted source
and subject to continuous monitoring and improvement.
Security: data is classified and secured at all times, with non-
intrusive controls and monitoring in place for data access,
sharing and breach reporting.
Data Democratisation: data can be accessed, researched &
analysed in a self-service environment.
Time to Value: accelerate the time to business value of new
data & analytic initiatives.
Time Value of Data: using data while it retains its maximum
impact or commercial value. Data history is also retained and
accessible when it has business relevance.
Data Enrichment: existing data sets can easily be augmented.
Data Goals & Characteristicsachieved through enabling
Customer Specific:
Removed in this presentation
Business Strategy
Customer example #2

10. What do Business Users Need

11. 11
IT DepartmentBusiness
“You’re too slow, too
expensive, and never
deliver what I want.”
“You can’t make up your
mind, keep adding
features, and never see
the big picture.”
Casual User:
“Just forget it.”
Power User:
“Just give me a data dump.”
BU Leader:
“We’ll do it ourselves.”
“I’d rather be doing
something else than
taking your order.”
“You’ll come crawling
back to us soon.”
Why is there a Problem?

12. Business Users and Self-Service Initiatives
Supporting self-service initiatives need to focus on three areas:
▪ Visualization:
▪ Easy-to-use, business-oriented reporting tools and dashboards
▪ Understand and explore:
▪ Extended business metadata in catalog for end users
▪ Access:
▪ A data delivery strategy – a single logical consolidated view of the data

13. Data Access Layer

14. Denodo’s Logical Architecture
ETL
Data Warehouse
Kafka
Physical Data Lake
ML/AI
SQL
interface
Logical Data Layer
Streaming
Analytics
Distributed Storage
Files
DataAbstractionLayer
Protocols,schemas,datatypes,
datamodels,etc.
DataCatalog
BusinessSemantics,Explorationand
Search,etc.
ETL
Data Warehouse
Kafka
Physical Data Lake
ML/AI
SQL
interface
Streaming
Analytics
Distributed Storage
Files
IT Storage and Processing
BI & Reporting
Mobile
Applications
Predictive Analytics
AI/ML
Real time dashboards
Consumer Tools

15. 15
A logical data layer – a “data fabric” – that provides high-performant, real-time, and secure access to
integrated business views of disparate data across the enterprise
The Denodo Platform
1. Data Abstraction: decoupling
applications/data usage from data
sources
2. Data Integration without replication
or relocation of physical data
3. Easy Access to Any Data, high
performant and real-time / right-
time
4. Data Catalog for self-service data
services and easy discovery
5. Unified metadata, security &
governance across all data assets
6. Data Delivery in any format with
intelligent query optimization that
leverages new and existing
physical data platforms

16. 16
Benefits of a Virtual Data Layer
▪ A Virtual Layer improves decision making and shortens development cycles
• Surfaces all company data from multiple repositories without the need to replicate all
data into a data warehouse or data lake.
• Eliminates data silos allows for on-demand combination of data from multiple sources.
▪ A Virtual Layer broadens usage of data
• Improves governance and metadata management to avoid “data swamps”.
• Decouples data source technology. Access normalized via SQL or web services.
• Allows controlled access to the data with low grain security controls.
▪ A Virtual Layer offers performant access
• Leverages the processing power of the existing sources controlled by Denodo’s optimizer.
• Processing of data for sources with no processing capabilities (e.g. files)
• Caching and ingestion engine to persist data when needed.
TTV
USAGE
PERFORMANCE

17. Demonstration

18. Demo Personas
Data Scientist
Business User
BI Analyst
Denodo is tool agnostic.
Denodo provides SQL based access e.g. JDBC, ODBC and ADO.NET.
Allows Integration with Reporting tools: Tableau, MicroStrategy, PowerBI, BO, Cognos, Looker, OBIEE, etc.
Denodo’s Data Catalog is a marketplace for your data assets.
Browse through tags and categories, understand lineage and data definitions.
Search the catalog and validate the data is trustworthy.
Denodo’s Notebook for data science allows execution of queries, visualization of charts and Python and R code.
Based on Apache Zeppelin, a popular Open-source notebook.
Fully integrated with Denodo’s security.

19. 19
Demo Scenario
▪ Historical sales data offloaded to Hadoop
cluster for cheaper storage
▪ Marketing campaigns managed in an external
cloud app
▪ Country is part of the customer details table,
stored in the DW
Sources
Combine,
Transform
&
Integrate
Consume
Base View
Source
Abstraction
join
group by state
join
Sales Campaign Customer
SaaS solution
How effective are our marketing Campaigns?

20. Data Governance for Self Service
Benefits
Single Entry Point
for Enforcing
Security and
Governance Policies
Data on-premises
and off, combined
through the same
governed virtual
layer
Single Source of
Truth
Who is Doing /
Accessing What,
When and How
Fewer copies of
personal data.
Lineage of copies is
available.

21. Summary
Business Friendly Interface
▪ Self-service with proper guardrails
▪ Data models and catalog are two of the same
Speed to Insight
▪ Decouple IT from business, giving them freedom to choose the right
technology for the right problem
Regulations, enterprise-wide governance & data security
▪ Controlled access all data assets in secure, business friendly format
▪ Full audit trails
The combination of a data catalog with a virtualization layer in a single platform can
efficiently address current business challenges:

22. Next Steps
Workshop offer:
For today’s participants we are offering a two-hour
assessment workshop to help you pinpoint where data
virtualisation will have the greatest impact in your
organisation.
Please contact either Adrian or Chris using the details below.
Contact information:
Adrian Bridge
Principal Consultant
RXP Group
0417 875 919
adrian.bridge@rxpservices.com
Chris Day
Director, APAC Sales Engineering
Denodo
+61 433 370 083
cday@denodo.com

23. VIRTUAL
November 24-25, 2020 | 9:00am SGT | 12:00pm AEDT
The Agile Data Management and Analytics
Conference
Advancing Cloud, Analytics & Data Science with Logical Data Fabric
REGISTER NOW
denodo.link/32Q70XC

24. Thank you