WGU D467 – DATA ETHICS &
BEST PRACTICES | COMPLETE
STUDY GUIDE + EXAM PREP
NOTES UPDATE 2026
Exploring Data
Section 1: Introduction
Lesson 1: Data types and structures
Every piece of information in the world is data.
How data is collected
Interviews
Observations
Forms
Questionnaires
Surveys
Cookies
Cookies: Small files stored on computers that contain information
about users
1
,Data collection considerations
1. Select the right data type
2. Determine the time frame for data collection
3. How the data will be collected
4. How much data to collect
Population: All possible data values in a certain dataset
Sample: A part of the population that is representative of the
population
5. Choose data sources
First-party data: Data collected by an individual or group using
their own resources (this is the preferred method because you
know exactly where the data came from.
Second-party data: Data collected by a group directly from itʼs
audience and then sold
Third-party data: Data collected from outside sources who did
not collect it directly
6. Decide what data to use
Data Formats
Internal data (Primary data): Collected by a researcher from
first-hand sources
External data (Secondary data): Gathered by other people or
from other research
Quantitative data: Can be measured and counted using
numbers (quantity, amount, range)
Qualitative data: Cannot be counted, measured or easily
expresses in numbers (names, categories, descriptions)
Discrete data: Data that is counted and has a limited number of
values
Continuous data: Data that is measured and can have any numeric
value
2
, Nominal data: A type of qualitative data that is categorized without
a set order (this data doesnʼt have a sequence)
Ordinal data: A type of qualitative data with a set order or scale (has
a specific sequence like movie reviews with star ratings)
Internal data: Data that lives within a companyʼs own systems
(usually more reliable and easy to collect)
External data: Data that lives and is generated outside of an
organization (itʼs structures and typically used when your analysis
requires all possible sources)
Structured data: Data organized in a certain format such as rows
and columns (Usually stored in spreadsheets and relational
databases). Like structured
thinking, this gives the data a framework
Unstructured data: Data that is not organized in an easily
identifiable manner (most data that is generated is unstructured)
Unstructured data examples:
Audio
files
Video
files
Email
Photos
Social Media
Structured data: Data elements: Pieces of
information, such as
Works nicely within a data
peopleʼs rights names,
model
account
Data model: A model numbers, and addresses
that is used for
organizing data
elements and how they
relate to one another
3
, Unstructured data:
Varied data types
Most often qualitative data
May have an internal
structure difficult to
search
Provides more
freedom for analysis
4
BEST PRACTICES | COMPLETE
STUDY GUIDE + EXAM PREP
NOTES UPDATE 2026
Exploring Data
Section 1: Introduction
Lesson 1: Data types and structures
Every piece of information in the world is data.
How data is collected
Interviews
Observations
Forms
Questionnaires
Surveys
Cookies
Cookies: Small files stored on computers that contain information
about users
1
,Data collection considerations
1. Select the right data type
2. Determine the time frame for data collection
3. How the data will be collected
4. How much data to collect
Population: All possible data values in a certain dataset
Sample: A part of the population that is representative of the
population
5. Choose data sources
First-party data: Data collected by an individual or group using
their own resources (this is the preferred method because you
know exactly where the data came from.
Second-party data: Data collected by a group directly from itʼs
audience and then sold
Third-party data: Data collected from outside sources who did
not collect it directly
6. Decide what data to use
Data Formats
Internal data (Primary data): Collected by a researcher from
first-hand sources
External data (Secondary data): Gathered by other people or
from other research
Quantitative data: Can be measured and counted using
numbers (quantity, amount, range)
Qualitative data: Cannot be counted, measured or easily
expresses in numbers (names, categories, descriptions)
Discrete data: Data that is counted and has a limited number of
values
Continuous data: Data that is measured and can have any numeric
value
2
, Nominal data: A type of qualitative data that is categorized without
a set order (this data doesnʼt have a sequence)
Ordinal data: A type of qualitative data with a set order or scale (has
a specific sequence like movie reviews with star ratings)
Internal data: Data that lives within a companyʼs own systems
(usually more reliable and easy to collect)
External data: Data that lives and is generated outside of an
organization (itʼs structures and typically used when your analysis
requires all possible sources)
Structured data: Data organized in a certain format such as rows
and columns (Usually stored in spreadsheets and relational
databases). Like structured
thinking, this gives the data a framework
Unstructured data: Data that is not organized in an easily
identifiable manner (most data that is generated is unstructured)
Unstructured data examples:
Audio
files
Video
files
Photos
Social Media
Structured data: Data elements: Pieces of
information, such as
Works nicely within a data
peopleʼs rights names,
model
account
Data model: A model numbers, and addresses
that is used for
organizing data
elements and how they
relate to one another
3
, Unstructured data:
Varied data types
Most often qualitative data
May have an internal
structure difficult to
search
Provides more
freedom for analysis
4
