Data Warehouse Design Project

MIS 6309 BUSINESS DATA WAREHOUSING
INSTRUCTOR: KEVIN R. CROOK
DATA WAREHOUSE DESIGN PROJECT
BookYourTicket.com
PRESENTED BY
PRADEEP YAMALA
pxy160860

BookYourShow.com Page 1 of 21
SUMMARY
BookYourShow.com is an online ticket booking website under media and entertainment sector
which offers showtimes, movie tickets, reviews, trailers, concert tickets and events near you.
Also, features promotional offers and coupons. This website targets internet users utilizing
services to perform online transactions like buying tickets. It is also positioned as India’s largest
entertainment ticketing website.
It all started in the year 1999 when 3 long-time friends go holidaying together in South Africa
the seed of a Big Tree is planted. A company is planned, from roots to fruits. Soon after the
Eureka moment, C.E.O. Amar quits his job at Sony, Co-Founder Akbar takes over Technology,
and Co-Founder Anthony takes over Finance.
With the cinema industry on a high and multiplexes and large cinema chains starting to
entertain Indian audiences around the country; Big tree takes over the rights to retail and
service New Zealand based ticketing software, Vista in India. During the dot-com bust that
happened in 2002, the company offered technology solutions from the Customer Relationship
Management point of view to fight the storm. This business flourished under the leadership of
our 3 musketeers.
Network 18 invested in March 2007. In August, the same year an internal contest was held to
coin a name for the new company. A developer intern came up with the name
BookYourShow.com and the rest, as they say, is history.
Big tree Entertainment Pvt. Ltd launches India's first ticketing aggregator - BookYourShow - in
August 2007, now one of the biggest ticketing portals in the country.

COMPETITIVE ANALYSIS OF THE COMPANY
Within a decade of its inception, BookYourShow poses a 40% compound annual growth rate
(CAGR) in revenues and over 90% market share in the online entertainment ticketing space.
BookYourShow becomes the official ticketing partner for Mumbai Indians, Kings XI Punjab, and
Delhi Daredevils. Today we have Pune Warriors and Rajasthan Royals too on board this high
drama entertainment circus called the IPL. Also, becomes the exclusive ticketing partner for
Formula 1, the Indian Grand Prix.
Records are meant to be broken - As it stands today, the highest number of tickets sold in a
single month was October 2014 - more than 5 Million - 5,696,685. BookYourShow is awarded
'The Hottest Company of the Year-2011-12' and 'The Company to watch out for' at the
prestigious CNBC Young Turks Award. BookYourShow App has around 7.2 Million downloads
that include Windows, Android, iOS, and Blackberry.
Accel Partners invests USD 18 Million Dollars i.e. Rs. 100 crores in BookYourShow. BigTree
Entertainment acquires Chennai based online ticketing company Ticket Green also acquires
Bengaluru-based Social Media Analytics firm Eventifier. BookYourShow awarded ‘Best
Omnichannel Customer Experience Brand’ at the OneDirect Quest Customer Experience
(QuestCX) Awards.
ScaleArc today announced that Bigtree Entertainment Pvt. Ltd. has selected ScaleArc for SQL
Server to ensure the availability and performance for its online entertainment ticketing
business – BookYourShow. BookYourShow is the largest entertainment ticketing portal in India
with more than 400 million average page views a month (website and mobile application). To

handle the onslaught of traffic and prevent downtime during the release of blockbuster movies,
BookYourShow deployed ScaleArc to ensure seamless availability.
With India’s undying love for films, it is not surprising that film ticketing comprises almost half
of BookYourShow’s business. Ticketing for sporting and other events contribute the next
biggest share to the revenue pie while the rest comes from advertising. Content, along with the
ad platform, is aimed at making advertising a significant revenue source. BookYourShow
expects it to contribute 10 per cent to the overall revenue.
BookYourShow just rolled out the newly designed and improved version of its Android app
which is highly interactive, smart and intuitive and improves the user experience. From about
14 steps, the booking experience has been reduced to 7 steps. Not just that, In addition to the
default English language option, users will also be able to discover entertainment options on
BookYourShow in Tamil, Telugu, Hindi and Kannada.
SWOT Analysis
Strengths
• Vast network of event organizers and major cinema chains
• Simple and convenient to use
• Continuous innovation like providing ticket booking
application for Blackberry mobile
• Constantly updated with forthcoming events and movies

• Around 90% market share in the online entertainment
ticketing space
Weaknesses • Mostly limited to urban areas as people in India are still
apprehensive for online payments
Opportunities
• Expand capabilities to cover more events and movies across
various cities
• Acquiring more partnerships with various business entities
• Co-organizing events with various event organizers to
increase physical brand presence
Threats
• Possibility of mismanagement due to lack of coordination
with event organizers
• Improved functionalities by competitive online ticketing
portals
• Newly emerging competitive online ticketing portals
The major competitors are Ticketfinder.com, paytm.com, and Tickets.com

DATA WAREHOUSE ARCHITECTURE
A data warehouse is a repository (collection of resources that can be accessed to retrieve
information) of an organization’s electronically stored data, designed to facilitate reporting and
analysis.
The final step in building a data warehouse is deciding a data warehousing architecture which
includes Inmon, Kimball and Standalone Data Mart. between using a top-down versus bottom-
up design methodology. And Kimball is the best fit for BookYourTicket.com. The final step in
building a data warehouse is deciding between using a top-down versus bottom-up design
methodology.
Kimball is a proponent of an approach to data warehouse design described as bottom-up in
which dimensional data marts are first created to provide reporting and analytical capabilities
for specific business areas such as “Sales” or “Production”. These data marts are eventually
integrated together to create a data warehouse using a bus architecture, which consists of
conformed dimensions between all the data marts. So, the data warehouse ends up being
segmented into several logically self-contained and consistent data marts, rather than a big and
complex centralized model. Business value can be returned as quickly as the first data marts
can be created, and the method lends itself well to an exploratory and iterative approach to
building data warehouses so that no master plan is required upfront. The Kimball’s method
focuses on optimization and quick win which are two key aspects needed for the
BookYourShow.com to rapidly expand their customer base.

Inmon is one of the leading proponents of the top-down approach to data warehouse design, in
which the data warehouse is designed using a normalized enterprise data model where data
warehouse is defined as a centralized repository for the entire enterprise. Dimensional data
marts containing data needed for specific business processes or specific departments are
created from the enterprise data warehouse only after the complete data warehouse has been
created.
Kimball is best preferred because business users can see some results quickly, with the risk you
may create duplicate data or may have to redo part of a design because there was no master
plan. With Inmon by the time we start generating results, the business source data has
changed or there is changed priorities and you may have to redo some work anyway.

BUSINESS PROBLEMS SOLVED USING BUSINESS DATA WAREHOUSE
Over the past few years, data warehousing capabilities have tremendously evolved to meeting
enterprise standards, addressing different cases such as velocity, variety, and volume.
Querying – A data warehouse needs to be capable of dealing with repetitive queries. Repetitive
queries support dashboards and reporting requirements when addressing a large number of
visitors.
Scale – This applies to multiple data structures and formats. You need a data warehouse that
can deal with large amounts of data in order to address the management of query workloads
and query optimization.
Real-time loading – In today’s world bulk and batch loading remain the most common method.
More advanced data warehouse technologies are moving to continuous loading methods,
which means that data is being loading from operational sources in real-time. This enables you
to ingest stream data and perform updates for reading optimization.
The data warehouse supports online analytical processing (OLAP), which enables high-level end
users to gain insight into business operations through interactive and iterative access to the
stored data. This enables business executives to improve corporate strategies and operational
decision making by querying the data warehouse to examine business processes, performance,
and trends.
Movies ratings based on the reviews and recommendations to users
Movie reviews provided by the customers are essential to track and decide whether the movie
needs to be suggested for others users or not. Movies with good reviews need to be filtered on

the other hand bad movie reviews should also to taken into consideration and calculate
average movie ratings.
Analyzing customer profiles and based on the past reviews, various movies are recommended
to the customers. Analyzing customer interests and suggesting various other events and special
screening events. This provides a better experience to the customers and increases the
frequency of visits by the customer.
Analyzing website traffic and transactions
At any point, to time the website traffic needs to be monitored for analyzing performance,
interruptions, and delays. Also, measure number of transactions per day. Doing this the load
can be measured and if needed, necessary steps should be taken to increase the load capacity
by adding additional servers. With this website crash, transactions failures, delay while loading
pages and various other problems can be minimized.
Measures number of customers who visited the website, number of transactions per day,
number of bookings during peak time like new movie release, the launch of the new show.
Thereby improving customer experience by eliminating transaction failures, delay during
payment gateway transactions, and while loading pages.
Analyzing Feedback and reviews
For any website feedbacks and reviews play a prominent role in improving the business,
provides better solutions, improves the process and providing better customer satisfaction.
Because these reviews are real time problems faced by the customers and they must be solved

to maximize profits and to improve the integrated workflow. There may be positive as well as
negative feedbacks, all these should be stored and analyzed to make the process better and
make it more convenient to the users. Analyzing customer feedback on various issues during
booking time and providing quick solutions to the problems faced. Calculate the average rating
of the movie by analyzing all the customer ratings.
Data Mining
Segmenting various customers and recommending various suggestions
Customers are segmented into various clusters based on their preferences, booking history and
search results. So, based on this various analysis is done to target few groups of customers to
maximize profits. Also, target marketing campaigns to reach more customers ultimately
improving customer base. Fraud detection of customer credit cards by validating card details.
Strategic and official partner
Strategic partnering with various companies by negotiating with them to improve business and
financial support. Thereby becoming official ticketing partner for various events. This can be
achieved by analyzing the company, their track record, events held, successful events
conducted.
Enhancing customer experience
Improving customer online experience is considered one of the primary things for any website.

Since it’s a ticket booking website user should constantly experience upcoming events, new
movie releases, recommended movies depending on various factors. This can be analyzed by
various factors like a number of visits by a user, customer interests, previous bookings, reviews
or feedback written.
Minimizing booking time
Generally, any user prefers to book a ticket in less than five minutes. Avoiding unnecessary
steps while booking a ticket can reduce overall booking time, thereby improving customer time,
avoiding delayed transactions and customer satisfaction. This can be achieved by analyzing
various fields a customer should fill and optimizing mandatory fields. On measuring average
booking time per ticket during peak time and normal peak hours few measures can be taken.
Choosing a payment gateway
All transactions happen through a gateway. So, choosing an optimal payment gateway is
essential in business and financial perspectives for any company. Due to failed transactions,
user money gets blocked ending up with the unsuccessful transaction.
Calculating average time taken to process a transaction, a number of failed transactions in a
month and few other data can be analyzed while selecting a perfect gateway. Also, the gateway
chosen should perform at its best during peak load when there is more traffic.
Official partners for events
Choosing best events also maximize the commissions, profits and brand value. The company
conducting the event should be trustworthy and there should be last minute issues regarding

tickets or price changes. Some events might get canceled after the tickets are booked, event
date might get postponed these types of issues are unavoidable. The Proper agreement should
be made before being an official partner, terms and clauses should be defined before signing an
agreement. Generally, analyzing about the company, their previous events, user reviews on the
previous events and choosing the best event.
Average bookings for a particular period
Maintaining good average bookings per day or at given time period always maximizes the
profits and withstand competition from competitors. This can be achieved by
analyzing average bookings on special occasions and holidays, planning way ahead by providing
discounted ticket price, special offers and offers on multiple ticket bookings etc.
Cancellation and refund assessment
For any company or website where there are money transactions, cancellations and refund do
not come under their profits. In fact, it minimizes the profits and losses customer goodwill and
loyalty. So, various reasons for cancellations are analyzed like failed transactions, event
canceled at the last minute, movie release date postponed. These problems can be minimized
by solving the errors or issues relating to the respective department. Analyzing an average
number of cancellations and the average amount refunded during a particular time. Measuring
these can result in maximizing profits and retain customer satisfaction and good will.

BUSINESS ANALYTICS QUESTIONS
1. The total commission received per ticket for an event.
2. Total processing charges for all the tickets
3. Top rated movies during a particular year
4. Number of failed transactions during payment gateway
5. Average age of users who frequently watch horror movies

DIMENSIONAL MODEL

FACT TABLES DESCRIPTION
TABLE NAME HIGH-LEVEL DESCRIPTION GRAIN ADDITIVE
FACTS
NON-ADDITIVE
FACTS
movie_show_facts Child table of various
dimensions
▪ movie
▪ theater
▪ special_screening
▪ show
▪ movie_show_junk
We can quantify facts such
as special_charges,
screen_capacity,
seats_occupied
Coarse-
grained
Special_charges Screen_capacity
Seats_occupied
customer_booking_facts Child table of various
dimensions
▪ admin
▪ discount
▪ ticket
▪ customer
▪ booking
▪ show
▪ theater
▪ movie
▪ events
We can quantify facts such
as total_price,
discount_price,
Fine-
grained
All facts none

ticket_price,
processing_charge,
tickets_per_booking
reviews_facts Child table of various
dimensions
▪ feedback_reviews
▪ movie
▪ customer
we can measure
movie_ratings
Coarse-
grained
movie_rating none
Customer_refund_facts Child table of various
dimensions
▪ customer
▪ booking
▪ refund
we can measure
refund_amount and
refund_processing_charges
Fine-
grained
All facts none

DIMENSION TABLES AND DESCRIPTION
TABLE
NAME
DESCRIPTION OF
DIMENSION
DIMEN
SION
TYPE
TYPE OF
CHANGES
RICH ATTRIBUTES NON-SELF-
EVIDENT
Movie Dimensional table
movie consists of
movie_key as the
primary key and has
attributes movie_id,
movie_name,
release_date, year,
month, day, actors.
Affinity Timestamped None Not
applicable
Theater Dimensional table
theater consists of
theater_key as the
primary key and have
attributes theater_id,
theater_name, location,
website, contact_info
Affinity Type 1 None Contact_inf
o is contact
information
of company
Admin Dimensional table
admin consists of
admin_key as the
attributes admin_id,
company_name,
password,
phone_number,
email_id, Address,
Address1, city, state
Affinity Timestamped Address consists of
address1, city, and
state
Not
applicable
Discount Dimensional table
discount consists of
discount_key as the
attributes
coupon_code,
start_date, end_date
applicable
Custome
r
Dimensional table
customer consists of
customer_key as the
attributes customer_id,
password,
Affinity Timestamped Customer_fullNam
e consists of
customer_first_na
me,
customer_middle_
name,
Not
applicable

customer_firstName,
customer_middleName,
customer_lastName,
last_movie_booked,
emai_id, age,
profession,
phone_number
customer_last_nam
e
Feedback
_reviews
Dimensional table
feedback_reviews
consists of
feedback_reviews_key
as the primary key and
have attributes
feedback, comments,
queries, date
applicable
Movie_s
how_jun
k
Junk table
Movie_show_junk
consists of
movie_show_junk_key
have attributes
gross_share, distributor,
food_menu,
isMovieGood,
screen_size
Junk Type 2 None Not
applicable
Ticket Dimensional table ticket
consists of ticket_key as
the primary key and
have attributes
ticket_id,
ticket_number,
ticket_type,
seat_number,
show_date
Affinity Type 1 None Not
applicable
Show Dimensional table show
consists of show_key as
the primary key and
have attributes
show_id, language,
start_time, end_time
Junk Type 1 None Not
applicable
Special_s
creening
Dimensional table
special_screening
consists of
Affinity Type2 None Not
applicable

special_screening_key
have attributes
event_id, event_name,
release_date
Booking Dimensional table
booking consists of
booking_key as the
attributes
booking_date,
booking_id, day, month,
year, booking_time,
booking_type,
transaction_status_cod
e,
transaction_status_desc
ription, status_code,
status_description
Affinity Type2 Booking_date
consistsof day,
month, year and
transaction_status_
code,
transaction_status_
description,
status_code,
status_description
Not
applicable
Refund Dimensional table
refund consists of
refund_key as the
attibutes refund_id,
refund_date,
refund_status_code,
refund_status_descripti
on,
processing_start_date
Affinity Type2 Processing_start_d
ate
Refund_status_cod
e
Refund_status_des
cription-
Not
applicable
Credit_ca
rd
Dimensional table
credit_card consists of
credit_card_key as the
attributes card_number,
expiry_date, cvv,
name_on_card
Affinity Type 1 None Not
applicable
Payment
_info
Dimensioanl table
payment_info consists
of payment_info_key
attributes payment_id,
payment_date,
payment_time_paymen
t_type,
applicable

payment_method,
payment_gateway
Events Dimensional table
events consist of
events_key as the
attributes event_id,
event_date,
event_time, location,
constact_info, email_id,
status, website
Applicable
HIGHLY BROWSABLE DIMENSION
A dimension table from which we can get a lot of analytical value from a dimension browse.
Usually, timestamped dimension makes them highly browsable dimension.
Booking table is considered as a highly browsable dimension because we can get more
information by querying this table alone.
We can solve the following business problems from the Business Table alone
• list of customers booking tickets over the phone
• number of failed transactions in a day
• average number of failed transactions in a month
• number of pending booking at that point of time
• Average bookings per day, month and year
• List of customer booking tickets online

JUNK DIMENSION
Occasionally, there are miscellaneous attributes, that don’t fit into tight star schemas. Rather
than discarding flag fields and yes/no attributes, place them in a junk dimension. In addition,
you can handle comment and open-ended text attributes by creating a text-based junk
dimension.
MOVIE_SHOW_JUNK holds details like gross_share, food_menu, distributor, isMovieGood,
screen_size and movie_show_junk_key which is a surrogate key. It has an identifying
relationship with MOVIW_SHIW_FACTS table. As these attributes did not fit in any of the
dimensions they are moved in a junk dimension.
BRIDGE & RELATIONSHIP CAUSED TO ADD THE BRIDGE
Since a customer can have many credit cards linked to their account, each customer has at least
one credit card or many credit cards (consider customer has multiple credit cards), we need to
construct a bridge. Hence, a CUSTOMER_CARD_BRIDGE is added to CUSTOMER dimension table
in the start schema and to CREDIT_CARD dimension table. This bridge does not have any
attributes other that keys from to dimensions.

BUSINESS ANALYTICS QUESTIONS AND QUERIES
1. The total commission received per ticket for an event.
Query CUSTOMER_BOOKING_FACTS, EVENTS dimension and ADMIN dimension to get the
total commission received per ticket for an event
2. Total processing charges for all the tickets
Query CUSTOMER_BOOKING_FACTS and TICKET dimension to get the total processing
charges for all the tickets
3. Top rated movies during a particular year
Query REVIEW_FACTS, MOVIE dimension and FEEDBACK_REVIEWS dimension to get the
Top rated movies during a particular year
4. Number of failed transactions during payment gateway
Query CUSTOMER_BOOKING_FACTS, PAYMENT_INFO dimension, BOOKING dimension to
get the number of failed transactions during payment gateway
5. Average age of users who frequently watch horror movies
Query REVIEWS_FACTS, MOVIE dimension, CUSTOMER dimension to get average age of
users who frequently watch horror movies

Data Warehouse Design Project

More Related Content

What's hot (20)

Viewers also liked (19)

Similar to Data Warehouse Design Project (20)

Recently uploaded (20)

Data Warehouse Design Project