Watch full webinar here: https://bit.ly/3wZLspv CITY Furniture, a Florida based retail giant, realized that bringing data from its orbital position to the core nucleus of the business decision-making was critical to meeting its business goals. That required an enterprise-wide digital transformation where data science and advanced analytics became the foundation for the company's new digital business model. Building a Logical Data Fabric allowed CITY Furniture to democratize their data and empower all their distributed data consumers. Learn how the fast-growing retail company could virtualize all its data sources and create a semantic layer to connect and deliver critical data-driven insights to all data consumers. This integrated data view empowered data users from marketing, sales, operations, supply chain, and merchandising functions to make critical insights-driven decisions that helped CITY Furniture increase their market share and grow the business beyond the borders of Florida.

1. CITY Furniture: Building an
Enterprise-Wide Logical Data Fabric at
the Core of their Digital Transformation
Chad Simpson
CIO, CITY Furniture

2. It all began in the summer of 1971 when founder, Kevin Koenig, received his MBA at Florida Atlantic University. With just $1,500 in his pocket, he set
out to accomplish one mission: open his first waterbed store: Waterbed City. Today, we represent both the CITY Furniture and Ashley Furniture
HomeStore brands and we've grown to have 16 CITY Furniture locations and 13 Ashley HomeStore locations throughout the state of Florida. It’s our
drive to win that has allowed us to become a top furniture retailer in South Florida with continuous growth. With planned expansions throughout
Southeast, Southwest, and Central Florida, CITY Furniture is a business focused on fast growth and exceptional service.
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.
Keith Koenig
CEO
Andrew Koenig
President
Kevin Koenig
Founder

3. Major Inflection Points
0 Data
Engineer
0 Data
Scientist
1 Analyst
13 RPG
developers
201
8
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.
12 Executive
Level
Users
201
8
Months to
fulfill any
Information
request
201
8
1971—2018
PRG “Batch & Queue Reporting”
0-47 years
2018
New Data Warehouse Architecture
“framework of the future”
2018
Data Team. Grew from 1-3 folks in 2018 to 25+ in 2022 (Data
Engineering, Analytics, Science, Reporting)
2018-2019
Realtime data replication “insights in the
morning, action in the afternoon”
2020-2022
Data Virtualization Layer “many to
one + real time reporting”
1971
Nasdaq debuts
2018
US Triggers a Trade War
2019
Protestors Take to the Street
2020
COVID-19
2021
COVID-19 Vaccines Arrive as the Virus Mutates
2022
Russia invades Ukraine
Waterbed City started in 1971
1 Store, NRW $25K 30 Stores, NRW +$400M 34 Stores, NRW +$500M 36 Stores, NRW +$600M 38Stores, NRW +$800M

4. Enterprise Applications
(e.g.: MyDay, ASAP, Web, High Jump)
Vendor
SFTP
Sales
Supply Chain
Operations
Marketing
Digital
Merchandising
RPG, db2
2018
Architecture
0 Data
Engineer
0 Data
Scientist
1 Analyst
13 RPG
developers
201
8
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.
12 Executive
Level
Users
201
8
Months to
fulfill any
Information
request
201
8
(reports)

5. 2018
Data
Architecture
- Pain Points
0 Data
Engineer
0 Data
Scientist
1 Analyst
13 RPG
Developers
201
8
Enterprise Applications
(e.g.: MyDay, ASAP, Web, High Jump)
Vendor
SFTP
Sales
Supply Chain
Operations
Marketing
Digital
Merchandising
RPG, db2
Data designed at a time when space was a
premium, presenting today challenges like:
Challenges:
Transactional legacy system with limited
primary keys, short names, numeric dates in
various non-standard representations, no
relational or normalized table design etc.
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.
12-20
Executive
Level Users
201
8
(reports)

6. 2019 Q1
Data
Warehouse
Architecture
1 E. Architect
1 IBM
Consultant
1 Data
Engineer
0 Data
Scientist
2 Analyst
2019
Q1
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.
12 – 20
Users
2019
Q1

7. Enterprise Applications
(e.g.: MyDay, ASAP, Web, High Jump)
Vendor
SFTP
Sales
Supply Chain
Operations
Marketing
Digital
Merchandising
2019 Q2
Architecture
DW
Real Time
KPIs
1 E. Architect
2 IBM
Consultant
2 Data
Engineer
0 Data
Scientist
2 Analysts
2019
Q2
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.
2019 Q2
100-800
Users
(reports)

8. Enterprise Applications
(e.g.: MyDay, ASAP, Web, High Jump)
Vendor
SFTP,
APIs
Sales
Supply Chain
Operations
Marketing
Digital
Merchandising
2019 Q3
Architecture
DW
1 E. Architect
2 IBM
Consultant
3 Data
Engineer
0 Data
Scientist
4 Analysts
2019
Q3
ETL builds
running on
Linux in
AWS
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.
2019 Q3
100-800
Users
Real Time
KPIs

9. Enterprise Applications
(e.g.: MyDay, ASAP, Web, High Jump)
Vendor
SFTP,
APIs
Sales
Supply Chain
Operations
Marketing
Digital
Merchandising
2019 Q4
Architecture
DW
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.
Real Time
KPIs

10. 2020 - Current
Architecture
1 E.
Architect
2 Data
Engineer
4 DS/DE
7 Analysts
2020-
21
Base View Virtual Layer
Enterprise Applications
(e.g.: MyDay, ASAP, Opportunities)
DE
High
Jump
Spreadsheet SMTP
Business Intelligence/DS/ML
Data Modeling
GS
Linux ETL
and
Monitoring
Server
Machine
Learning
Docker
Sales
Supply Chain
Operations
Marketing
Digital
Merchandising
DW
Scheduling Server
Data Catalogue
Base View Data Modeling
BigQuery
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.
Users
> 1000

11. CITY Enterprise Benefits / Roadmap
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.
REST API Data Catalogue Scheduler
VQL GraphQL

12. CITY Furniture Team
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.
Chad Simpson
CIO
Steve Wilder
CFO
Ricardo Perdigao
VP Architect
Ian Sauer
VP Data
Ryan Fattini
Director DE
Guillermo Zerpa
Manager DE
Jim Baker
IBM Sr. Consultant
Maxie Callaghan
Director Analytics
Jessi Kimmerly
Sales Analyst Mgr.
Dan Muthama
Customer Care Analyst Mgr.
Yanchen Chen
Data Science Engineer
John Adrian
Data Science Engineer
Tongtian Fan
Data Science Engineer
Bob Kalacinski
DENODO Sr. Consultant
Da Alex Yang
Supply Chain Analyst Mgr.
Chris Anton
Operations Analyst Mgr.
Mike Mizne
Digital Analyst Mgr.
Alejandro Tejada
BI Analyst
Kevin Da Silva
DE/Analyst
Martina Burone
BI Analyst
Igor Cruz
Sr. Data Engineer

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