Title Page.
CST3330: Technology and Innovation Management.
CASE STUDY: THE ETHOPIAN AIRLINE
Student Number: M00852257
Student Name: ADEREHINWO JOSEPH ADEWALE
Table of Contents
CASE STUDY: THE ETHOPIAN AIRLINE ................................................................................ 3
INTRODUCTION............................................................................................................................. 3
DATA REQUIREMENT.................................................................................................................. 4
Internal data at Ethiopian Airline ................................................................................................... 5
External data at Ethiopian Airline .................................................................................................. 6
DATA WAREHOUSE ...................................................................................................................... 7
A discussion of data identified which would be suitable for storage in a Datawarehouse with
an example and brief discussion of one suitable data schema Snowflake .................................... 7
The ETL process ............................................................................................................................... 9
Online Analytical Processing operations (OLAP) .......................................................................... 9
BIG DATA ....................................................................................................................................... 13
A discussion of data that is not suitable for storge in the data Warehouse and the reasons
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Using examples from your scenario, discuss a framework that could be used to collect, store,
and analyses this data. .................................................................................................................... 13
CONCLUSIONS ............................................................................................................................. 14
References ............................................................................................................................................ 15
CASE STUDY: THE ETHOPIAN AIRLINE
INTRODUCTION.
Commercial aviation company Ethiopian Airlines was established on December 21, 1945. The
airline has records of its operations going back 76 years. With a fleet record of 157 foreign
departures per week and 53 international destinations, it began operating a year after it was
created. Due to its effectiveness and standards, the airline had previously held the title of being
the best in Africa. Customers can choose from a variety of operational booking alternatives.
There is first class, economy class, and business class, for instance. The airline has been
profitable for practically every year since it began operations and has continued to do so. The
airline reportedly holds a lion's share since it is "the pan of African network." Additionally, it
has tracked daily travel to 21 domestic destinations from local points, with both the newest and
youngest fleets offering greater services to the general public. Mr. Tewolde Gebremariam is
the company's chief executive officer, and he has kept meticulous records to ensure that the
company maintains its competitive edge. It has advanced certified specialists from the United
States (USA) Federal Aviation Administration for airplane maintenance (FAA). Technical
repairs are being carried out on items including the engine, cameras, aircraft lights,
speedometers, and order.
In today's human society, data has emerged as a crucial consideration (Baars, 2018). According
to research, the use of data has transformed many corporate operations, including the aviation
industry (Kasirzadeh, 2019). Examples of the data gathered include real-time data, which is
examined and shared with the pilots for subsequent flights because the airline industry is one
of the sectors where professionalism is most necessary because it deals with people's lives
(Rakshit, 2017). Another example is when data from an incident is studied to determine what
caused it, such as when data from a plane crash is used to determine whether the engine was to
blame or whether hardware or software malfunctioned. In order to avoid such a problem in the
near future, further adjustment is made after the data has been analyzed. According to research,
data analysis is one of the key elements that has kept the aviation business growing over the
years with a positive fleet record (Dou, 2020).
The Ethiopian Airlines is described as a commercial airline that was established on December
21st, 1945, and it has 76 years of operational history in addition to the other significant essential
records mentioned before. This did not occur in a single day. It requires a lot of effort and
perseverance. How, therefore, did the airline outperform its rivals and maintain its competitive
advantage? For the sustainability of its company, the airline gathers data and conducts decision
analysis. The internal and external data played a role for Ethiopian airline surpass the Kenyan,
Rwandair airlines and other more. The internal and external data are known as (structured and
unstructured data). The airline analyzes data and provides packages, such as affordable
international and domestic ticket prices and good customer care (out and on plane services).
Ethiopian Airlines must have given new technology a hearty "THANKS" today. They have
offered numerous methods for quickly analyzing large data to enhance operations and maintain
competitive advantage (Adner, 2021). For instance, big data analytics and tools are now
available for conducting analyses. Tableau, Weka, and Hadoop are a few (Acharjya, 2019).
This is a dream come true for the airline because it is striving toward cutting maintenance costs,
giving its clients good services, and competing with other carriers. The Federal Aviation
Administration (FAA) of the United States has professionals working with Ethiopian Airlines,
and part of their duties includes installing cutting-edge technology on the aircraft. The
Ethiopian airline has adopted and is using brand-new planes with cutting-edge technology.
Data may be easily accessed and gathered in planes due to this. For instance, sensors from realtime operations are looking for anomalies, and the data is being quickly processed to deal with
issues that are predicted to arise and unanticipated scenarios. Data on ticket sales is also
evaluated to determine how many people visited there at a specific moment.
However, because the aviation sector entails moving a sizable number of people from one
location to another, due attention must be taken when doing research. It concerns human lives
and could put them in danger due to a small fault in the software or hardware (Elhmoud, 2021).
Other information is gathered from Accuwether and loyalty programs, demographic
information, information from assistance agencies, information from mobile applications and
websites, information from the engine because it has real-time sensors, information that can be
used to determine the cause of failure or the best course of action, particularly in the event of
bad weather, and information on how to provide a safer flight in order to improve customer
experience and maintain a competitive edge.
However, this project will go through how data is used by the airline industry for cost analysis
and commercial decision making.
DATA REQUIREMENT
Data from both inside and outside the airline are gathered. data such as past travel plans,
purchased flights, and location-based demographic information. Real-time flight data from the
engine and sensors is also gathered. Once more, technical data as well as data from third parties,
such as data from aid organizations, social media platforms, and so on, are collected. To
achieve its objectives and maintain its competitive edge, however, is the reason for data
collection. The internal and external data are shown in the table below. Including both
structured and unstructured data:
Internal data at Ethiopian Airline
Structured

Total sales transactions
Un-structured

Data from web browser, and cookies
from the airline website

Data from the Ethiopian reservation

Data from survey and polls

Data from emails

Data from social media platforms and
systems

Data from the loyalty program and
inventory system

The, inflight customers purchase
data and baggage information
mobiles applications
The information above was gathered from internal sources of Ethiopian Airlines. The
representation of the structured and unstructured data is obvious. Unstructured data includes,
for instance, cookies from the web browser from the business' websites, data from surveys and
polls, information gathered from passengers' mobile phone applications for airlines, and
information from social networking platforms like "the most frequently asked questions
(FAQ)". These data cannot fit into a predetermined format. It signifies that because the data
are unstructured, even though they were gathered from internal corporate sources, they cannot
be housed in the data repository. also known as qualitative data.
Furthermore, demographic information such a customer's name, age, gender, and location is
included in the data as the complete sales transaction. The time of a customer's purchase ticket
and, if the customer used a credit card, the card information, which includes the card pin, are
also included in the data from the total sales. Because they adhere to a strict format, the data
gathered from the inventory systems, loyalty programs, in-flight purchases, and baggage
information are all structured data that can be preserved in the data repository. The number of
passengers and the times of departure and arrival are gathered. The information helps the
business in its study of effective business decisions for service improvement.
Ethiopian Airlines has, nevertheless, seen a significant transformation over the years as a result
of data that helps it maintain its competitive edge. However, for the protection and privacy of
client data, the corporation must abide by certain data regulations.
External data at Ethiopian Airline
Structured

Un-structured
Sensor’s data in the airplane

Third party data from social medial
platforms


The alliance agency data
The customer purchase history and
web browser cookies data

Data from the airline tracking

Data from Accuweather

Flight recorders and sensors

Data
systems and from travel agents (0TA)
such
as
the
online
customers
purchase history

The demographic data from alliance
partners such as customers name,
age and gender
from
including
sellers
other
and
brokers
social
media
platforms

Data from TV news channels and
related
sources
for
example,
promotions and advertisements
The company obtains the aforementioned external data from third parties. Both structured and
unstructured data are present. Unstructured data cannot conform to a proper format, i.e., cannot
be saved in the data repository, and includes information gathered from Tv news channels,
promotions, advertisements, social media platforms like LinkedIn, Twitter, Instagram, data
from flight recorders and sensors, customer purchase histories, and cookies from web browsers.
Accuwether data is used to gauge the state of the climate. This will enable the airline to
determine the current weather and if it is suitable for takeoff.
Additionally, the majority of the information gathered by alliance agencies and airplane sensors
is demographic information. Passenger names, ages, and genders may be included in this data.
Additionally, information from tracking systems is gathered, such as the time of passenger
departure and arrival. Additionally, data from partner agencies are gathered, and the Ethiopian
airline uses all of this information to enhance decision-making.
DATA WAREHOUSE
A discussion of data identified which would be suitable for storage in a Datawarehouse
with an example and brief discussion of one suitable data schema Snowflake
The data that will be kept in the airline's data warehouse is structured data. The data
warehouse's design, which only allows for structured data, is the cause. data that follow a strict
format. Data examples include user names, age, sex, location, phone number, and address,
sometimes known as demographic data. The user's payment information, such as credit card
numbers, expiration dates, pin codes, and CVVs Once more, the information gathered from the
aircraft engine, such as the various routes to be taken, is formatted and may be saved in the
Datawarehouse for analytical purposes. The name of the client who purchases a ticket, together
with the time, place, and destination, are all included in the total sales data. This data is
structured and adheres to a certain standard. The data warehouse can also store information
gathered from customer loyalty cards and flight reservation systems. The corporation analyzes
the passenger data in order to boost sales, reduce operating expenses, and provide clients deals
and reasonably priced services. Algorithms are being used to analyze the data. Although this
service is provided to the broader public, the data analysis helps the airline to determine which
group of people to target the most when it comes to advertising and giving out discounts.
Although no one is excluded, businesses always have a target demographic in mind.
However, by offering several offers, discounts, and service promotions, data analytics has
enabled the airline to see a significant change over time. For instance, offering discounts to
clients during special occasions and holidays.
The below depicts a Snowflake scheme
The aforementioned snowflake architecture depicts a primary fact table that is connected to
numerous aspects and is appropriate for the data warehouse of Ethiopian Airlines to enhance
decision-making in its cost analysis. There are a total of five dimensions shown on the
snowflake cost analysis fact table, which are each discussed below.
Information about demographic clients, such as name, address, and phone number, is stored in
the customer dimension. To better identify the types of customers who primarily purchase
airline tickets, the dimension is evaluated. the sexual orientation that provides discounts and
promotions and travels the most. Moreover, the kind of luggage they bring. Understanding the
type of flight that departs from a specific airport, along with the departure date, time, and arrival
gate, is made easier by the flight dimension. The size of the aircraft is utilized to determine
which seats, whether in first class, business, or economy, are more frequently reserved by
passengers and to develop any necessary and cost-effective creative techniques to draw people
in any of the classes. When necessary, manufacturer data is also obtained from the aircraft
dimensions and examined. In order to determine when in the year the airline is most likely to
receive more passengers, the time dimension is examined, as is the airport dimension, which
reveals the name, location, and terminal that most passengers depart from.
However, the aforementioned snowflake will help the airline efficiently use the five dimensions
in its cost analysis.
The ETL process
Extract
The extract is the initial step in the ETL process. The extract involves moving data from the
source site into the data warehouse for long-term storage and analysis. In this instance,
information is being taken from Ethiopian Airlines' operating databases. Structured data, such
as total sales, demographics, payment information, and real-time data, including information
from engine sensors, are among the data that are extracted. Data from the company's websites,
partner agencies, and mobile applications are all extracted. Data duplication and anomalies
were verified for standards during the data extraction process, and a similarity data check was
done to generate categories of the same data. Last but not least, the data was examined for
integrity before it was finally retrieved because unreliable data cannot be entered into the data
warehouse even though it can be transformed.
Transform
Before being loaded into the data warehouse for long-term storage or analytical uses like cost
analysis, the data retrieved from the airline systems is further processed. First off, the retrieved
data may not match the Datawarehouse settings for the currency and time zone, for instance.
Then, this data is changed. Data value mapping is the second step in the transformation. The
airline ensures that case-specific data is transformed. For illustration, suppose that the genders
are represented by the letters "M" for male and "F" for female. Data cleansing is the last step.
In this stage, the airline ensures that the data follows a certain format in order to fit into the
data warehouse without any issues. Before the loading step, there are numerous modifications
made in the transformation stage.
Load
The extracted and transformed data are now placed into the data warehouse for long-term
archival or for analytical uses like cost analysis. But before refreshing its data lake, the
corporation uses the complete refresh loading procedure. Today, the business uses incremental
loading, which enables modifications to be made before fully loading the data. It is more
adaptable since a data administrator has the option to make changes.
Online Analytical Processing operations (OLAP)
The electronic processing OLAP is a tool that helps businesses design and organize their
various data systems. (Patil, 2029) The technology helps businesses run intricate analytical
queries on a certain dimension. A dimension is queried based on the administrator's or firm's
preferences, for instance, if a company wanted to evaluate a customer's dimension or sales to
understand how it is doing, for example, whether there are processes that need to be amended.
Given that Ethiopian Airlines has a sizable corporate operation and operates internationally,
MOLAP (multidimensional online analytical processing) will be more appropriate for the
carrier. MOLAP can efficiently obtain data from the airline's warehouse without interfering
with the database server's operation, It can handle complex queries effectively and produce
accurate results in a variety of dimensions. This will improve the company's intelligently led
decision-making. The MOLAP cube is shown in the figure below.
Multidimensional OLAP Cube
In roll-down, the cube is broken up into tiny pieces to reveal more of its intricacies. Roll-up,
on the other hand, enlarges the dimensions in order to get more particular, predictive
information while seeing less details. The roll-up figure below shows how years are
compressed into months and how the roll-up figure shows how years are shrunk into moths to
display accurate information on the dimension.
According to the query that was run, the cube is divided during slicing in order to display a
particular dimension. The query for slicing the first class dimension is conducted in the figure
below, producing its categories, and the query for slicing the first class and January is executed
in the second figure, producing only the first class and the January dimensions.
Depending on the query that is run, the cube is divided into multiple dimensions for dicing.
The image below shows how the cube is divided into two dimensions to display two categories
drawn from all three of the cube's dimensions.
When the query is run in a pivot operation, only one face of the dimension is shown. The cube
can return six different results in response to the query, as seen in the figure below.
BIG DATA
An explanation of the types of data that are unsuitable for storage in the data warehouse
Large volumes of data are gathered together as big data. Both structured and unstructured
representations of the data are available. Big data must be standardized before analysis can be
performed on it (Inmon, 2018). Big data is information that the Ethiopian Airlines uses that
cannot be stored in a data warehouse. data from the aircraft sensors, as an illustration. Because
both domestic and foreign flights run every day, the sensors collect a lot of huge data. Flights
are produced by many businesses with unique hardware and software configurations. Despite
the fact that some data may be structured and used for statistics, each flight has a specific
technique of recording its data. But the amount of data produced by flights is so large that it
cannot be stored in the data warehouse. Due to the speed of the flights, data must first be
generated in tones before it can be written into a snowflake. It's referred to as an operable speed.
There is a constant change of data due to the speed of the planes and other data that cannot be
stored into the warehouse are data from social media platforms. The data such as “emojis,
smileys and symbols”. The weather data, data from survey and sweepstakes, data from
brokers and blogs including advertisement data. Snowflake does not provide a room for the
storage. Yet, the data from the flight sensors can be processed very fast with a snowflake
framework and saved into the data warehouse and used for the decision making and cost
analysis.
The unstructured data are also pre-defined and do not follow a strict format. The data cannot
be saved to the data warehouse due to this. An illustration of social media data is provided
above. Emoji, smileys, and other symbols are undefined data that is not in its normalized form.
The data warehouse is not a good place to store audio and video files that are written in
abbreviations by social media users. Some of the audio data when played is eco-noise or waves,
and the language used may not be defined. They also contain online information.
Using examples from your scenario, discuss a framework that could be used to collect,
store, and analyses this data.
As was already noted, Ethiopian Airlines need a framework that can quickly analyze data from
airplane sensors and store it in a data warehouse for analysis. The framework of a snowflake
will do the trick. This is a distinct framework for data processing and storage that is still
comparable to Hadoop. Because it is a combination of softwares that processes and stores
unstructured data, Hadoop is unique. Hadoop won't be suggested in this situation because it is
an exposed framework that includes numerous software tools. Because it is open source, some
people even refer to it as a third-party data framework.
A highly secure cloud service is provided by the snowflake architecture. And unlike Hadoop,
which duplicates data into fixed-sized files on the distributed file system, there is no data
duplicate before it can be saved. By default, the snowflake framework does not permit two
nodes to share the same data. Companies rely heavily on using Snowflake since it offers
minimal cost, data privacy, and even faster retrieval of data when a query is conducted on the
cloud.
However, if a business chooses to use Hadoop to analyze unstructured data, precautionary and
security steps must be followed to protect data privacy.
CONCLUSIONS
Ethiopian Airlines is a business that needs a lot of data to make business decisions, analyze
costs, improve operations, and outperform its rivals by offering exceptional services to its
clients. The airline, which ranks among the best in Africa and has an impressive daily, local,
and international flying record, is very interested in increasing its revenue. The airline does
extensive data analysis, with both internal and external data contributing. The airline gathers
information from websites, inventory systems, and mobile applications. ETL is used to create
the data from the operational system. For better promotions and offers, data on travelers'
bookings, payments, and travel history is gathered and analyzed. Since the aviation sector is
one in which professionalism is highly required for safer flights, data is also collected such as
real-time and from engines to maintain their planes. The aviation industry works to reduce
costs as well. In order to do this, the airline tracks the travel patterns of each passenger and
uses that information to increase customer traffic both within and outside of holiday seasons.
Less passengers will just put more strain on the plane's engines and operating sensors. Further
data analysis is being done to identify ideal weather conditions for a safer flight, and in the
event that there is a lot of customer baggage, the airline promptly pays another commercial
flight to transport the loads. A platform called Hadoop is used to gather and examine
unstructured data. Additionally, if the business decides to use the framework, preventive and
security measures must be followed for the safety of the data due to the open source nature of
the program and the ease with which hackers might hack it to obtain user data. These reports
also included information about the company's internal and external data as well as a suggested
methodology for gathering unstructured data, along with a discussion of its benefits and
drawbacks. Using the Hadoop framework previously described, huge data may be studied
together with the various data types and motivations behind it.
However, analysis has been crucial in helping the airline continue to run throughout the years
as it uses bid data to make business intelligence decisions that reduce costs and please
consumers.
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