The document discusses the importance of customer insights and analytics in modern retail, emphasizing the use of a logical data fabric for data integration and management. Case studies from companies like Eroski, Walmart Mexico, and City Furniture illustrate how data virtualization can create a 360° view of customers, enhance operational efficiency, and support real-time data access. The conclusion advocates that embracing modern data architectures is essential for retailers to better understand and respond to customer needs.

1. WEBINAR
The Value of Customer
Insights & Analytics in a
Modern Retail Environment
The use of a logical data fabric

2. WEBINAR
The Value of Customer
Insights & Analytics in a
Modern Retail Environment
Robin Tandon
Director of Product Marketing, EMEA & LATAM
Denodo
The use of a logical data fabric

3. Speaker
Robin Tandon
Product Marketing Director, Denodo

4. Agenda
1. The Challenges of Retail
2. The Logical Data Fabric in Retail
3. Case Studies
1. Eroski
2. City Furniture
3. Walmart (Mexico)

5. The Challenges of Retail

6. 6
The challenges of retail
It all begins and ends with a happy customer
Customer
Experience
Operational
Efficiency
Financial
Performance

7. 7
Typical retail landscape
ERP
Supply Chain
Logistics
Warehousing Warehouse Head Office
Contact Center
In store
Ecommerce
Point of Sale
CRM
ERP
CRM
Call Recording
UCaaS Platform
CRM
Digital Marketing
ERP
Big Data
Supply Chain
BI & Analytics
Data Science
Google Analytics
Marketing Cookies
ERP
Ecommerce Platform

8. That’s Easy to Figure Out?

9. 9
Can you consolidate all your data to a single location?
Data warehouse Data lake Cloud

10. 10
Just put all the data in one place!
Cloud Data
1990s
Data Warehouse
Age
2010s
Big Data
Age
2020s
Modern Data
Age
2000s
Cloud / Social
Age
Database
ERP
ODS
ERP
ERP
ERP
ERP
Database
Database
Database Database
Data
Warehouse
Social Data
Big Data
Visualization
Mobile
Web
Internet of
Things
Connected
Devices
Autonomous Cars
1980s
Database
Age

11. 11
Data is distributed

12. Forces of Data Anti Gravity

13. 13
Data anti-gravity
Geography
Ownership
Technology

14. How Do We Fix This?

15. 15
Gartner – The Evolution of Analytical Environments
This is a Second Major Cycle of Analytical Consolidation
Operational Application
Operational Application
Operational Application
IoT Data
Other NewData
Operational
Application
Operational
Application
Cube
Operational
Application
Cube
? Operational Application
Operational Application
Operational Application
IoT Data
Other NewData
1980s
Pre EDW
1990s
EDW
2010s
2000s
Post EDW
Time
LDW
Operational
Application
Operational
Application
Operational
Application
Data
Warehouse
Data
Warehouse
Data
Lake
?
LDW
Data Warehouse
Data Lake
Marts
ODS
Staging/Ingest
Unified analysis
› Consolidated data
› "Collect the data"
› Single server, multiple nodes
› More analysis than any
one server can provide
©2018 Gartner, Inc.
Unified analysis
› Logically consolidated view of all data
› "Connect and collect"
› Multiple servers, of multiple nodes
› More analysis than any one system can provide
Fragmented/
nonexistent analysis
› Multiple sources
› Multiple structured sources
Fragmented analysis
› "Collect the data" (Into
› different repositories)
› New data types,
› processing, requirements
› Uncoordinated views
Just put all the data in one place!

16. Data Fabric – A Logical Approach to Data
Integration and Data Management

17. 17
Modern data fabric
Data Fabric Net
Compounds Customers Products Claims
RDBMS/OLTP Traditional Analytics/BI Data Lakes Cloud Data Stores Apps and Document
Repositories
Flat Files
Third Party
Legacy
Mart
Data Warehouse
Mart
ETL ETL
XML • JSON • PDF
DOC • WEB
A data fabric is an architecture pattern that informs and automates the design, integration and
deployment of data objects regardless of deployment platforms and architectural approaches
▪ It utilizes continuous analytics and AI/ML over all metadata assets to provide actionable insights and recommendations
on data management and integration design and deployment patterns.
▪ This results in faster, informed and, in some cases, completely automated data access and sharing
▪ Strongly supported by both Gartner and Forrester

18. What is a Logical Data Fabric?

19. 19
Logical data fabric
Logical Data
Fabric
Consumers
Data
Science
Machine
Learning
Artificial
Intelligence
Mobile
Applications
Predictive
Analytics
Business
Intelligence
Relational NoSQL Unstructured Docs Cloud Sensors IoT
Sources
Unified Data Security
Abstraction Layer
Universal Access
Self-
Servic
e
Data
Catalo
g

20. 20
Denodo Platform architecture
DATA CATALOG
Discover - Explore - Document
DATA AS A SERVICE
RESTful / OData
GraphQL / GeoJSON
BI Tools Data Science Tools
SQL
CONSUMERS
DATA VIRTUALIZATION
CONNECT
to disparate data
in any location, format
or latency
COMBINE
related data into views
with universal semantic
model
CONSUME
using BI & data science
tools, data catalog,
and APIs
Self-Service
Hybrid/
Multi-Cloud
Data
Governance
Query
Optimization
AI//ML
Recommendations
Security
LOGICAL
DATA
FABRIC
SOURCES
Traditional
DB & DW
150+
data
adapters
Cloud
Stores
Hadoop
& NoSQL OLAP Files Apps Streaming SaaS

21. 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
• Data Abstraction: decoupling
applications/data usage from data
sources
• Data Integration without
replication or relocation of
physical data
• Easy Access to Any Data, high
performant and real-time/ right-
time
• Data Catalog for self-service
data services and easy discovery
• Unified metadata, security &
governance across all data
assets
• Data Delivery in any format with
intelligent query optimization
that leverages new and existing
physical data platforms

22. 22
Three core components of a logical data fabric
AI/ML
Features
Logical Data
Warehouse
DATA VIRTUALIZATION
Enterprise Systems NoSQL
Hadoop
Data Warehouse
ETL
BI / Reporting
Analytics

23. Key Retail Use Case Studies

24. 24
Customer use case: Eroski
Business Need
▪ Eroski wanted to improve their view of
the customer, but their customer data
was held across over 30 different data
sources. These included customer
service, online activity, the Eroski loyalty
program as well as other sources, such
as consumer partnership data. Eroski
wanted to create a true 360 view of the
customer.
▪ Eroski’s marketing intelligence division
needed to create segment based
customer marketing initiatives based on
the demographic profile of their
customers
▪ The Denodo Platform for Data
Virtualization was invaluable in achieving
the business objectives of creating a
unified view. Denodo creates a virtualized
master data mart. Denodo connects to all
the disparate data sources seamlessly
creating a single unified view of the
customer. Denodo is also completely
flexible allowing Eroski to add data
sources such as social media or new
partnership data in real time allowing for
instant knowledge of end consumer.
Denodo helps Eroski have a full 360
view of the client.
Eroski was able to:
▪ Build client cards for individual customers
and make them available to stakeholders
▪ Enable customer analytics, based on all
available information through studies,
segmentation, and models
▪ Create comprehensive reports of
customer behavior
▪ Improve security and reduce risks related
to customers’ personal information
Solution Benefits
Operates approximately 2,000 retail outlets in particular supermarket
and hypermarkets across Spain as well as travel offices and gas
stations.

25. 25
Customer use case: Eroski
360° View of Customer
Enabled business users with
360° view of customer to
better understand
customer emotions, brand
preferences and purchase
patterns
With Denodo acting as a
unifying layer on top of
multiple data sources, a
consolidated view of the
customer was created.
Enabling Eroski to better
understand and react to
customer needs

26. 26
Why? Let’s use a case study
• Need to reduce package delivery time to remain competitive in the online
shopping arena.
▪ Original delivery time was 3 days
▪ Wanted to offer next-day delivery
• From online shopping cart to delivery, data goes through multiple applications
▪ Accumulated delay for status updates and communication between
applications adds up
• Real-time analytics could provide a fresher look at data to speed up the
process
• Huge ROI potential
• Challenges:
▪ Complex ecosystem, multiple data source types, large data volumes

27. 27
Customer use case: Walmart - Mexico
Business Need
▪ Ecommerce Poor Sync with Inventory
resulting in 5 mins delay between order
being placed and store receiving the
order
▪ Data Lake: Slow Access and Poor
processing on Queries
▪ Data migrations & Restore – Issues with
data access during migration and time to
restore
▪ Data Governance & Security – Complex
point to Point Data Governance
▪ Real Time Access to Ecommerce Data
Sources – Leads to Real time sync
avoiding process pitfalls, inconsistencies
& loses.
▪ Denodo Real time Access with our logical
layer & Data Catalogue helps remove
layers of data replication costs to
expensive data marts and data lakes
▪ Data Virtualization layer used as a logical
layer between relational data bases and
cloud migration to Azure
▪ Denodo Single Point of Entry Walmart to
simplify maintenance, Data Governance
& Security to manage final users
privileges & Access
• Ecommerce Challenge: Reduction in
order cancellation due to optimized
inventory and improvements in in store
order processing
• Data Lake Challenge: Access very slow
Outcome: Denodo’s query optimization
technology vastly improved query
processing.
• Cloud Migrations: Data virtualization
provides a logical layer to safely migrate
in real time data to the Azure cloud
• Data Governance & Security
Challenge: The Denodo platform
provides a single intelligent interface to
manage security and governance-
improving regulatory compliance
Solution Benefits
Part of the global Walmart group, operates 2,755 stores across
Mexico, including 294 Walmart Supercentre stores and 166 Sam’s
Club outlets. Walmart Mexico (Walmex).

28. 28
Data virtualization benefits for Walmart
Sources Data Virtualization
STAGIN
G
REPOSITO
RY
Data
Caching
Master
Catalo
g
Sensors
ED
W
Early
Discovery
Global
Local
Social
Data Platform
OD
S
No
SQ
L
Data
Produc
ts
DATA
MART
S
Cost
Based
Optimizer
Local Data
Marts &
Consumption
External
Located
on Cloud
Located
on
Premise
Custom
Catalog
Files
Data Discovery /
Self Service
Advanced
Analytics
DATA GOVERNANCE
Dashboar
ds
Streamin
g
Batc
h
SQ
L
Project Highlights:
• Data Governance across the data
sources
• Simplification of data estate
• Supply chain

29. 29
Customer use case: City Furniture
Business Need
▪ Their ETL processes were slow
and cumbersome impeding many
important initiatives
▪ The com needed a centralized data
source to provide real time
accurate data to diverse teams
▪ City Furniture integrated the
Denodo Platform into its data
architecture as the highest-level
data layer for all analytics and
operational systems. City Furniture
connected all data sources, from
basic flat-file Excel spreadsheets,
to cloud databases, to a legacy
IBM iSeries server platform, to the
Denodo Platform's virtual layer.
• Accelerate data access, which,
during the pandemic, helped in
timing the market, picking up
market share, and maximizing
profits
• Enabled City Furniture to deliver 40
new reports that are full of
actionable production insight, and
they are driving better business
decisions every day.
• This project resulted in a deep
cultural shift. It moved data from
having an “orbital” position in the
organization to being part of the
very nucleus of it.
Solution Benefits
Founded in 1971 by Kevin Kong has grown to include 2 key brands,
City Furniture and Ashley Furniture Homestore, operating from 29
outlets across the state of Florida, with planned expansions through
out the south east of the United States

30. 30
Enterprise Applications
(e.g.: MyDay, ASAP, Web, High Jump)
Vendor
SFTP,
APIs
Sales
Supply Chain
Operations
Marketing
Digital
Merchandising
DW
Real Time
KPIs
1 E. Architect
2 IBM
Consultant
4 Data
Engineer
0 Data
Scientist
5 Analysts
2019
Q4
Linux ETL
and
Monitoring
Server
Data
Modeling
Confidentiality Disclaimer:
The content of this presentation is proprietary and confidential information of City Furniture. It is not intended to be distributed to any third party without consent of City Furniture.

31. 31
2020-
21
Base View Virtual Layer
Enterprise Applications
(e.g.: MyDay, ASAP, Opportunities)
High
Jump
Spreadsheet SMTP
Business
Intelligence/DS/ML
Data Modeling
GS
Linux ETL
and
Monitori
ng Server
Machine
Learning
Docker
Sales
Supply
Chain
Operations
Marketing
Digital
Merchandising
DW
Scheduling Server
Data Catalogue
Base View Data Modeling
BigQuery

32. 32
Conclusions
✔ Retail have specific data integration and data management
needs
✔ A logical approach helps businesses become more agile and
derive value faster
✔ Data virtualization empowers retailers to know their
customers with a 360° view
✔ Its not possible to centralize all data into a single location –
so modern data architectures such as a logical data fabric
need to be embraced.

33. Q&A

34. 34
Next Steps
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Logical Data Fabric
A Technical Whitepaper
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35. Thanks!
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