Difference between Qualitative and Quantitative Data, Horizontal and Vertical Analysis, Data Quality and Performance Evaluation of British Airways Group

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5 October 2022
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1.
a) Difference between qualitative data and quantitative data
The term Qualitative data
This refers to a type of data that approximates, characterizes and includes techniques designs
and estimates that do not give discrete arithmetical data
Types of qualitative data include
i. Binomial data –this refers to qualitative data which put items in one of the two
absolutely conjoint categories, example right or wrong
ii. Nominal data – this refer to qualitative data that put items in one of the two named
categories that do not have inferred or original value
iii. Ordinal data – refers to qualitative data that assign items to categories that do have
some type of inferred or original rank or value
Examples include tastes, textures, smells, attractiveness, and color
Whereas,
Quantitative data
This refers to a data type that is quantifiable and which entails designs, methods, and
estimates that give discrete arithmetical data (Lewin, 2015, pp.215-225.)
Types of quantitative data include
i. Continuous data- this refers to data that can be distributed, apportioned and given to
smaller levels. To mention one can measure the width of a field into more accurate
measures such as into centimetres, meters, and kilometres
ii. Discrete data -these are counts that cannot be accurately quantifiable or made more
precise with indivisible entities. For instance, you cannot have 1.4 adults
Examples are humidity, temperature, prices, area, and volume
b) Difference between horizontal analysis and vertical analysis
Horizontal analysis
Trend analysis also referred to as horizontal analysis which is a financial statement analysis
method that depicts fluctuations in the numbers relating to financial statement units or objects
over a given span of time (Lakada, Lapian, and Tumiwa, 2017. 2012-2016, pg53).
An example of horizontal analysis for instance when comparing sale in the year 2017 and
2018 shows how to prepare horizontal analysis between the two years
The horizontal analysis is extremely effective in understanding the performance of an
organization in a period. The financial statements of an organization shows the financial state
of the organization as well as its performance during a period. Horizontal analysis contains
data about an organization in stepwise format. It helps in analysing the financial state and
performance of the organization during a period without going into any comparative analysis.
Vertical analysis

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This refers to a financial statement analysis technique that expresses each amount on a
financial statement as a percentage of another given amount (Rahman, 2019.)
For example, vertical analysis can be used in analysis of the balance sheet to result in every
amount in the balance sheet expressed as a percentage of the total assets.
The vertical analysis on the other hand is extremely useful for comparative analysis of
financial statements of an organization. Vertical analysis is useful in comparing the
performance of an organization in the current period with its performance in preceding year
or two or more preceding years. Vertical balance sheet containing information about assets
and liabilities of an organization again will be easier to compare due to the comparative data
provided in such statement.
Statistical data
This refers to a branch of mathematics that deals with numerical data collection, organization,
analysis and interpretation (Agrawal, and Gopal, 2018, pp. 93-99).
Statistics as a scientific discipline is fundamental in facilitating quantifiable and accurate
information is extracted from big data
Whereas
Big data
This refers to the process of obtaining and interpreting complex data types in terms of
variation and volume, in other situations the speed at which they should be assembled
(Sagiroglu, and Sinanc, 2019, pp. 42-47).
2. Meaning of data quality.
Data quality this refers to an intricate way of measuring data properties from different
perspectives as a function of its ability to be conveniently processed and interpreted for other
applications such as data warehouse, database or data analytics system (Jarke, Jeusfeld,
Quix, and Vassiliadis, 2019. pp.229-253).
Importance of data quality in the financial statements
Financial statements refer to written reports produced by an organization’s management that
convey the business operations, the financial position and the financial performance of an
organization for a given accounting period
i. It enhances improved and informed decision making
Improved data quality leads to better and informed decision making across the company. A
thorough analysis of the financial statements for example balance sheet enables the financial
manager to know the value of the existing assets if they afford more and in severe
depreciation if assets can be disposed of
ii. Increased transparency of the financial statements

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Improved data quality will help to ensure that even the least details in the statement of
financial position can make a great impact on the organization. This can be made possible for
instance by providing figure like profit earned before tax, profit retained after tax and
depreciation of assets which are able to inform the financial manager a lot
iii. Mitigation of errors
High and improved data quality is able to mitigate errors by ensuring accurate and detailed
financial statements which are important to avoid and cut on costly mistakes. If an error has
occurred reconciliation procedures are able to find them
iv. Trust building and confidence in the financial statements
Most importantly an improved data quality is able to result in an accurate financial statement
that induces trust in the business. Investors and creditors need to know that the business is
doing well before putting in their funds this can be made possible if the balance is showing
profit rather than losses
v. Better payment cycles
Ultimately, high data quality can lead to improved payment cycles as it optimizes on the
accounts payable and accounts receivable cycles, which help to ensure accurate financial
statements on the outgoing payments such as dividends to shareholders
vi. Evaluation of tax liability
High data quality will enable the company to reduce its tax burden through the loopholes that
exist in tax laws because when the company makes high profit the corporate tax rates is
equally high, therefore tax evaluation is necessary
vii. Better strategic planning and forecasting
High data quality in the financial statements is able to create opportunities for educated and
informed strategic management planning and forecasting through the cash flow statements
and trading accounts
viii. Competitive advantage
If a business is using high data quality than the competitors, then they gain competitive
advantage since data is the most valuable resource in today’s businesses
Assessment of two
1.
a. Below is the ascertainment of the aircraft manufacturer with the greatest
number of the fleet from the year 2017 data report
Boeing, with a fleet number of eight with airbus having six and Embraer having a fleet
number of 2. Therefore, Boeing having the largest fleet
b. Determining the aircraft manufacturer with the biggest or largest fleet depiction
from the given data for the year ended 2017
Using addition to determine the total of each aircraft manufacturer to find British air
fleet with the largest presentation
Ultimately, adding the number of the fleet under each of the three aircraft
Airbus total= 1+44+67+18+12= 142