1. Data warehouse apply
in retail & finance
Lecturer: Dr. Doan Trung Tung
Students:
- Pham Anh Doi
- Nguyen Quang Huy
- Nguyen Hai Nam
- Luong Anh Tuan
- Pham Duc Thang
- Nguyen Thi Tuyet Trinh
- Emmanuel Nana Ofori
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2. Agenda
1. Why apply data warehouse in Retail and Finance
2. Typical Uses of Data Warehousing in Retail
3. Opportunity and Challenge when apply Data Warehousing in
Retail
4. Typical Uses of Data Warehousing in Finance
5. Opportunity and Challenge when apply Data Warehousing in
Finance
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3. Data warehouse vs Database
Data Warehouse (OLAP) Operational Database(OLTP)
It involves historical processing of information. It involves day-to-day processing.
OLAP systems are used by knowledge workers such as
executives, managers, and analysts.
OLTP systems are used by clerks, DBAs, or database
professionals.
It is used to analyze the business. It is used to run the business.
It focuses on Information out. It focuses on Data in.
It is based on Star Schema, Snowflake Schema, and Fact
Constellation Schema.
It is based on Entity Relationship Model.
It focuses on Information out. It is application oriented.
It contains historical data. It contains current data.
It provides summarized and consolidated data. It provides primitive and highly detailed data.
It provides summarized and multidimensional view of
data.
It provides detailed and flat relational view of data.
The number of users is in hundreds. The number of users is in thousands.
The number of records accessed is in millions. The number of records accessed is in tens.
The database size is from 100GB to 100 TB. The database size is from 100 MB to 100 GB.
These are highly flexible. It provides high performance.
4. Understanding a Data Warehouse
• A data warehouse is a
• Subject oriented
• Integrated
• Time-variant
• Non-volatile collection of data.
This data helps analysts to take informed decisions in an
organization.
6. How did data warehouse originate?
Comprehensive
decision making
support
Subject orientation
Integrate data
Flexibility in time
Keeps data safe
and secure
Store large
volumes of data
Accurate and grounded
7. Data Warehouse Applications
• A data warehouse helps business executives to
organize, analyze, and use their data for decision
making.
• A data warehouse serves as a sole part of a plan-
execute-assess "closed-loop" feedback system for the
enterprise management. Data warehouses are widely
used in the following fields:
• Financial services
• Banking services
• Consumer goods
• Retail sectors
• Controlled manufacturing
9. Why do we use S3, and not HDFS as the
source of truth?
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• S3 is designed for 99.999999999% durability and 99.99% availability of objects over
a given year
• S3 provides bucket versioning, which we use to protect against inadvertent data
loss
• S3 is elastic, and provides practically “unlimited” size
10. 2. Typical Uses of Data Warehousing in Retail
• Market Basket Analysis
• In-Store Placement
Use decision support to understand which items are being purchased, where
they belong, and modify configurations in order to maximize the # of items
in the market basket.
Retailers are able to negotiate more effectively with their suppliers
Display space, product placement . . .
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11. 2. Typical Uses of Data Warehousing in Retail
• Product Movement and Supply chain
Analyzing the movement of specific products and the quantity of products
sold helps retailers predict when they will need to order more stock
Product sales history allows merchandisers to define which products to
order, the max # of units and the frequency of reorders
Automatic replenishment with JIT delivery
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12. 2. Typical Uses of Data Warehousing in Retail
• Product Pricing
Price elasticity models manipulate detailed data to determine not only the
best price, but often different prices for the same product according to
different variables
Permits differential pricing
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13.
14.
15.
16. So how does Netflix use analytics?
83.18 million worldwide streaming customers
gather a tremendous amount of data
make better decisions and make users happier with
their service.
Netflix
17. So how does Netflix use analytics?
• When you pause, rewind, or fast forward
• What day you watch content (Netflix has found people watch TV shows
during the week and movies during the weekend.)
• The date you watch
• Where you watch (zip code)
• What device you use to watch
• When you pause and leave content (and if you ever come back)
• Searches (about 3 million per day)
Some of the “events” Netflix tracks:
18. So how does Netflix use analytics?
• Data Warehouse -> BI platform for
reporting is currently the mainstream
model in most company. This is
typically the state of the art in most
large enterprises.
• BI platforms provide a range of
capabilities for building analytical
applications.
19. 3. Opportunity and Challenge when apply Data Warehousing in Retail
• Opportunity
Successful data warehouse efforts have resulted in increased sales, improved
productivity, decreased inventories and, lately, enhanced customer loyalty.
Strategic customer marketing is now at the forefront of most retail data warehouse
strategies, retailers are intensifying their efforts to identify, satisfy, retain and
maximize the value of their best customers.
• Challenge
Data preparation continues to be difficult.
Populating the retail data warehouse starts in the store at the point-of-sale registers
not the most conducive environment for detailed data collection.
The consumers have very little patience for giving personal information such as an
address or telephone number
The sheer volume of transactional information is also a challenge for any retail data
warehouse initiative.
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20. First American Corporation
• 1990, lost $60 million
• 2000, profitable, innovative leader
thanks to VISION with Data
Warehouse
Nguồn: University of Virginia
21. First American Corporation
Phase 1 Phase 2 Phase 3 Phase 4 Phase 5
1-2Q 1996 2-3Q 1996 3Q 1996 – 1Q 1997 2Q 1997 – 1Q 1998 2-4Q 1998
Business
Goals
Identify the top revenue
producers
Identify the least profitable
customers
Include actual transaction
and product data in
profitability formulas
Understand all aspects of
client and product
profitability
Incorporate profitability
understandings in business
processes
Technical
Goals
Enhance the existing
customer information
system with retail revenue
Enhance the existing
customer information system
with direct contribution view
for consumers
Enhance existing customer
information system with net
income after capital charges
(NIACC) for consumers
Deploy the warehouse –
proof of concept (consumer)
Commercial profitability
integration
Complete production
testing of the warehouse
Nguồn: University of Virginia
22. The VISION Data Warehouse Architecture
Nguồn: University of Virginia
23. Raymond James Financial
Customer Profile
• Raymond James Financial is a Florida-based diversified holding company providing financial services to individuals, corporations, and municipalities through its
subsidiary companies.
Business Situation
• The company’s data backup and recovery solution wasn’t keeping up with its rapid growth; a move to 64-bit computing required a compatible data management
solution.
Solution
• Raymond James migrated to the CommVault Systems Galaxy data backup and recovery solution running on Microsoft® Windows Server™ 2003 Enterprise and
Datacenter Editions.
Benefits
• Data backups 25 percent faster
• Data restores 75 percent faster
• Data reliability up 12 percent
• IT productivity increases
• 64-bit data warehouse enabled
“We can be tremendously more productive, because our
backups and restores are faster and virtually effortless.”
-Terry Poovey, Manager of Storage Administration, Raymond James Financial
24. 5. Opportunity and Challenge when apply Data
Warehousing in Finance
• Opportunity
Less of a training curve because banks have been monitoring trends
and fluctuations in data long before the DW
Regular users of decision support
• Challenge
Deregulation, mergers, changing demographics and nontraditional
competitors
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