1. The document discusses primary and secondary data, methods of data collection, and data classification. 2. Primary data is original data collected directly by researchers, while secondary data was previously collected by others. Primary data collection methods include surveys, interviews, and experiments, while secondary data comes from existing sources like publications and records. 3. Data classification organizes data into categories to aid usage and protection, with the goals of making data easily retrievable, meeting compliance requirements, and increasing awareness of data sensitivity.

1. `
18MAT120
Business Statistics
Anju Asokan
Department of Mathematics
Amrita Vishwa Vidyapeetham

2. 1
Unit 1
Primary & Secondary Data
 Primary & Secondary Data
 Comparison
 Data Collection Methods Place your
Webcam Video here
Size 100%

3. 2
Primary Data
Primary data is the data that is collected for the first time through
personal experiences or evidence, particularly for research
It is also described as raw data or first-hand information
The data is mostly collected through observations, physical testing,
mailed questionnaires, surveys, personal interviews, telephonic
interviews, case studies, and focus groups, etc.

4. 3
Primary Data
The mode of assembling the information is costly, as the analysis is
done by an agency or an external organization, and needs human
resources and investment
The investigator supervises and controls the data collection process
directly

5. 4
Secondary Data
Secondary data is a second-hand data that is already collected and
recorded by some researchers for their purpose, and not for the
current research problem
It is accessible in the form of data collected from different sources
such as government publications, censuses, internal records of the
organization, books, journal articles, websites and reports, etc.

6. 5
Secondary Data
This method of gathering data is affordable, readily available, and
saves cost and time
However, the one disadvantage is that the information assembled is
for some other purpose and may not meet the present research
purpose or may not be accurate.

7. 6
Comparison - Primary Data & Secondary Data
 Primary data are those that are collected for the first time
Secondary data refer to those data that have already been
collected by some other person
 Primary data are original because these are collected by the
investigator for the first time
Secondary data are not original because someone else has
collected these for his own purpose

8. 7
Comparison - Primary Data & Secondary Data
 Primary data are in the form of raw materials
Secondary data are in the finished form
 Primary data are more reliable and suitable for the enquiry
because these are collected for a particular purpose
Secondary data are less reliable and less suitable as someone
else has collected the data which may not perfectly match our
purpose

9. 8
Comparison - Primary Data & Secondary Data
 Collecting primary data is quite expensive both in the terms of time
and money
Secondary data requires less time and money; hence it is
economical
 No particular precaution or editing is required while using the
primary data as these were collected with a definite purpose
Both precaution and editing are essential as secondary data were
collected by someone else for his own purpose

10. 9
Methods of Data Collection
Primary Data Sources:
Creative works (paintings, movie reels, music etc.)
Diaries
Experiments performed by the researcher
Letters
Surveys and censuses
Interviews
A primary source is collected directly from the original source. It is not
clouded with someone else’s views or judgments.

11. 10
Methods of Data Collection
Secondary Data Sources:
Encyclopedias
Essays
Newspaper opinion pieces
Reviews
Textbooks

12. 11
Data Collection
Data collection is a process of gathering information from all the
relevant sources to find a solution to the research problem
It helps to evaluate the outcome of the problem
The data collection methods allow a person to conclude an answer to
the relevant question
Most of the organizations use data collection methods to make
assumptions about future probabilities and trends

13. 12
Primary Data Collection
Primary data or raw data is a type of information that is obtained
directly from the first-hand source through experiments, surveys or
observations
The primary data collection method is further classified into two types:
 Quantitative Data Collection Methods
 Qualitative Data Collection Methods

14. 13
Primary Data Collection
 Quantitative Data Collection Method
It is based on mathematical calculations using various formats like
close-ended questions, correlation and regression methods, mean,
median or mode measures
This method is cheaper than qualitative data collection methods and it
can be applied in a short duration of time

15. 14
Primary Data Collection
 Qualitative Data Collection Method
It does not involve any mathematical calculations
This method is closely associated with elements that are not
quantifiable
This qualitative data collection method includes interviews,
questionnaires, observations, case studies, etc.

16. 15
Primary Data Collection
 Qualitative Data Collection Methods
1. Observation Method
Observation method is used when the study relates to behavioral
science. This method is planned systematically. It is subject to many
controls and checks.
The different types of observations are:
•Structured and unstructured observation
•Controlled and uncontrolled observation
•Participant, non-participant and disguised observation

17. 16
Primary Data Collection
 Qualitative Data Collection Methods
2. Interview Method
The method of collecting data in terms of oral or verbal responses. It is
achieved in two ways, such as
•Personal Interview – In this method, a person known as an interviewer
is required to ask questions face to face to the other person.
•Telephonic Interview – In this method, an interviewer obtains
information by contacting people on the telephone to ask the
questions or views orally.

18. 17
Primary Data Collection
 Qualitative Data Collection Methods
3. Questionnaire Method
In this method, the set of questions are mailed to the respondent. They
should read, reply and subsequently return the questionnaire. The
questions are printed in the definite order on the form.

19. 18
Primary Data Collection
 Qualitative Data Collection Methods
3. Questionnaire Method
A good survey should have the following features:
•Short and simple
•Should follow a logical sequence
•Provide adequate space for answers
•Avoid technical terms
•Should have good physical appearance such as colour, quality of the
paper to attract the attention of the respondent

20. 19
Primary Data Collection
 Qualitative Data Collection Methods
4. Schedules
This method is similar to the questionnaire method with a slight
difference. The enumerations are specially appointed for the purpose
of filling the schedules. It explains the aims and objects of the
investigation and may remove misunderstandings, if any have come
up.

21. 20
Secondary Data Collection
Secondary data is data collected by someone other than the actual
user. It means that the information is already available, and someone
analyses it.
The secondary data includes magazines, newspapers, books, journals,
etc. It may be either published data or unpublished data.

22. 21
Secondary Data Collection
Published data are available in various resources including
• Government publications
• Public records
• Historical and statistical documents
• Business documents
• Technical and trade journals
Unpublished data includes
• Diaries
• Letters
• Unpublished biographies, etc.

23. 22
Data Classification
Data classification is a data organization process into various
categories that helps with both protection and general usage of such
data
The very purpose of a classification process is to make your data
easily locatable and retrievable without needing to interrogate it
again.

24. 23
Data Classification
Data classification is all about using a variety of labels to define a piece
of information based on its data’s type, integrity, access permissions,
and content. It’s not uncommon to use different security measures
based on the results of the data classification with one of the
parameters being the data’s importance and/or confidentiality.

25. 24
Data Classification Types
 Classifying data based on its context, the main points of interest are
indirect indicators of the information’s sensitivity, including location,
creator, application, etc
 User-defined classification is entirely reliant on manual user
selection for each document, it relies heavily on the end-user’s
discretion and knowledge to appropriately flag documents with
different types of sensitivity
 Classification type that automatically inspects files’ contents to
determine their importance, also called content-based classification

26. 25
Data Classification Process
 Finding out the correct criteria and/or categories that would be
used to perform the entire data classification process;
 Implement various security-related measures based on the results of
the classification process;
 Ensure the maintaining of proper data classification protocols by
outlining the responsibilities of a company’s employees

27. 26
Data Classification Steps
1. Risk assessment
Clear understanding of all of the requirements from the confidential
and privacy standpoint is a requirement to begin
2. Classification policy development
A comprehensive classification policy without overcomplicating
everything is another big step towards a decent data classification
system
3. Data categorization
Understanding your data types and how important they might be
beforehand is also heavily recommended before starting

28. 27
Data Classification Steps
4. Data location discovery, identification and classification
The main part of the process when it comes to classifying data is the
actual data discovery, along with identification and subsequent
classification
5.Security measures and maintenance
Applying appropriate security measures and updating them when
necessary is the last significant part of the data classification system.

29. 28
Data Classification Vendors
• Dataguise (US
• Titus (Canada)
• Google (US)
• Innovative Routines
International (IRI)
• SoftWorks AI (US)
• AWS (US)
• Clearswift (UK)
• PKWARE (US)
• Microsoft (US)
• OpenText (Canada)
• Boldon James (England)
• Forcepoint (US)
• Varonis (US), Informatica (US)
• Spirion (US)
• Janusnet (Australia)
• Digital Guardian (US)
• Seclore (US)
• Netwrix Corporation (US)
• GTB Technologies (US)
• Sienna Group (US)

30. 29
Data Classification
Classification means arranging the mass of data into different classes
or groups on the basis of their similarities and resemblances. All
similar items of data are put in one class and all dissimilar items of
data are put in different classes.

31. 30
Purpose of Classification
 It helps in presenting the mass of data in a concise and simple form.
 It divides the mass of data on the basis of similarities and
resemblances so as to enable comparison.
 It is a process of presenting raw data in a systematic manner
enabling us to draw meaningful conclusions.
 It provides a basis for tabulation and analysis of data.
 It provides us a meaningful pattern in the data and enables us to
identify the possible characteristics in the data.

32. 31
Benefits of Data Classification
 Compliance
Classifying data, adding labels, and enforcing policies helps your
organization meet legal compliance and regulatory requirements.
 Usage Rights
By understanding the sensitivity of the data, you can begin to
understand who should or shouldn’t have access to it both inside and
outside of your organization.

33. 32
Benefits of Data Classification
 Awareness
Data classification helps to ensure employees are more aware of the
type of information they are dealing with and its value, as well as their
obligations in protecting it to prevent data loss or compromise
intellectual property
 End User Empowerment
Data classification brings security to the front of your organization by
empowering its users. Many data leaks could be avoided if a data
classification solution is in place. Adding visual labels to headers and
footers helps to raise end user awareness and assist them in becoming
more security focused and avoid sharing sensitive content

34. 33
Objectives of Data Classification
 To consolidate the volume of data in such a way that similarities and
differences can be quickly understood. Figures can consequently be
ordered in sections with common traits.
 To aid comparison.
 To point out the important characteristics of the data at a flash.
 To give importance to the prominent data collected while
separating the optional elements.
 To allow a statistical method of the materials gathered.