This document discusses data mart approaches to architecture. It defines a data mart as a subset of a data warehouse that supports the requirements of a particular department. It notes that data marts are often built and controlled by a single department. The document outlines the key differences between data warehouses and data marts such as scope, subjects covered, data sources, size and implementation time. It also discusses the types of data marts and why organizations implement them to improve response times, decision making and match user views. Dimensional modeling concepts are introduced along with examples from healthcare and banking organizations.

1. DATA MART APPROCHES
TO
ARCHITECTURE

2.  What is Data Mart ?
 Data mart and Data Warehouse.
 Why We need Data Mart.
 Types of Data mart.
 Concept of OLAP.
 Dimensional model.
 EXAMPLE – HCMC and BMO

3.  A subset of a data warehouse that
supports the requirements of a
particular department or business
function.
 Data mart are often built and
controled by a single department
within an organization

4. POINTS DATA WAREHOUSE DATA MART
 SCOPE CORPORATE LINE OF BUSINESS
 SUBJECTS MULTIPLE SINGLE
 DATA SOURCES MANY FEW
 SIZE 100 GB - TB+ < 100 GB
 IMPLEMENTATION
TIME
2-3 YEARS MONTHS

6.  To improve end user response time.
 We can easly get information for single
department such as Marketing, Finance,HR.
 Requires less cost in implementation in
comparison with data warehouse.
 Helpful for significant decision making.
 To provide data in a form that matches the
collective view of a group of user.

7.  Dependent Data Mart
 Independent Data Mart

8. Marketing
Sales
Finance
Human Resources
Data
Warehouse
Data Marts
External Data
Flat Files
Operational
Systems Marketing
Sales
Finance

9. Sales or Marketing
External Data
Flat FilesOperational
Systems

11. The data is found at the intersection of
dimensions.
Store
GL_Line
Time
FINANCE
Store
Product
Time
SALES
Customer

12. Identify fact tables
◦ Translate business measures into fact tables
◦ Analyze source system information for
additional measures
◦ Identify base and derived measures
◦ Document additivity of measures
Identify dimension tables
Link fact tables to the dimension tables
Create views for users

13.  Hennepin County Medical Center is a public
hospital
 360 Hospital beds
 1999; recorded 318,200 clinic visits
 131,000 unduplicated patients
 HCMC Ranked one of nation’s best
hospitals 3rd consecutive year on the U.S.
News & World Report

14.  Ownership:
◦ Data belongs to the organization
◦ Data belongs to the patient
 Quality:
◦ Data quality is everyone’s business
◦ Data flow :Input, Processing, Output

15.  Accessibility of data any time, any how,
anywhere
 Create a “Doubt” free environment
 Develop subject specific data distribution
 Utilize e-business approach to deliver data
(Intranet)

16.  In Feb. 2007 HCMC implemented 2007 SAP
across all areas of the hospital.
NEED – 1.How many new patient are coming and
their age group.
2. Frequently identify patients returning
to hospital

17.  In 2012 Oracle implemented data mart for
various department.
 Targeted Department – Clinical , labour and
delivery, supply chain , surgery, GI lab.

18.  Fact table
 Fact
 Measure
 Dimension
 Dimension table
Fact Table
Patient Resourc
e
Time Total Appointments
Dimensions
John DoeAge
John Doe
John Doe
John Doe
John Doe
Dr. V
Dr. V
Dr. G
Dr. V
Dr. G
January
February
March
May
April
11
6
4
3
6

19. Fact table
Race
Age
Resource
Gender
Patient

20. Fact table
Financial
Class
Patients and their
Financial Class
Appointments of Patients
and their Financial Class

21.  Where they were consuming time 400 hrs to
create the report, using data mart this was
reduce to 10 hrs.

22. •User
participation
•Security
•Storage (MOLAP,
ROLAP, HOLAP)
•Archiving
•Training
•Maintenance

23.  This bank founded in 1817, montreal ,
canada
 1995 The credit card division surveyed the
competitive landscape and quickly realize
that the card business was changing
dramatically.

24.  IT could not wait for the implementation of
Data warehouse .becouse it was time
consuming process.
 Bank was anticipating a considerable increase
in compition from US-based card issuers.
SOLUTION – Bank decided to implement Data
Mart for credit Card division.

25.  In 1995 proposal to build data mart for credit
card division.
 Project were granted in early 1996, after
accepting project bank worked with data mart
vendor.
 Launched data mart in Aug 1996.

27.  IRR(internal rate of return) greater than 100%.
 The transaction volume on these new accounts
is 59% higher than the average account.