WGU D468 Discovering Data |Study Guide Notes & Extra Questions and
Answers |PA and OA|2025 Update with complete solutions
Discovering Data D468
Module 1
● Data Analysis is the collection, transformation, and organization of data in order
to draw conclusions, make predictions, and drive informed decision-making
● A Data Analyst is someone who collects, transforms, and organizes data in
order to help make informed decisions.
● Foundations: Ask>Prepare>Process>Analyze>Share>Act>Capstone
● Data analytics is the science of data
○ Data is basically a collection of facts or information, and through analysis.
● Businesses need a way to control all that data so they can use it to improve
processes, identify opportunities and trends, launch new products, serve
customers, and make thoughtful decisions.
○ For businesses to be on top of the competition, they need to be on top of
their data.
● People analytics is the practice of collecting and analyzing data on the people
who make up a company’s workforce in order to gain insights to improve how the
company operates.
● The six steps of the data analysis process are:
○ Ask, Prepare, Process, Analyze, Share, and Act
○ The purpose is to gain insights that drive informed decision-making
The Six Phases of Data Analysis
1. Ask: business challenge, objective, or question
2. Prepare: data generation, collection, storage, and data management
3. Process: data cleaning and data integrity
4. Analyze: data exploration, visualization, and analysis
5. Share: communicating and interpreting results
6. Act: putting insights to work to solve the problem
In the Ask Phase - you will work to understand the challenge to be solved or the
question to be answered - you’ll ask many questions to help you along the way
,In the Prepare Phase - you’ll find and collect the date you’ll need to answer your
questions. You’ll identify data sources, gather data, and verify that it is accurate and
useful for answering questions
The Process Phase - is when you will clean and organize your data. Task you perform
here include removing any inconsistencies; filling in missing values. Essentially, you’re
ensuring the data is ready before you begin analysis.
The Analyze Phase - is when you do the necessary data analysis to uncover answers
and solutions.
The Share Phase - when you present your findings to decision-makers through a
report, presentation, or data visualizations. As part of the share phase, you decide
which medium you want to use to share your findings and select the data to include.
Last is the Act Phase - in which you and others in the company put the data insights
into action. This could mean implementing a new business strategy, making changes to
a website, or any other action that solves the initial problem.
Key takeaways
The six phases of the data analysis process help answer business challenges, such as
understanding how to improve a retirement program. Additionally, iterating on and
reviewing your work throughout the data analysis process is critical for obtaining quality
results.
Decision Intelligence - is a combination of applied data science and the social and
managerial sciences.
Data Science - the discipline of making data useful, is an umbrella term that
encompasses three disciplines: machine learning, statistics, and analytics.
Data Analysis Process—how industry professionals move from data to decision
EMC's data analysis process
,EMC Corporation's data analytics process is cyclical with six steps:
1. Discovery
2. Pre-processing data
3. Model planning
4. Model building
5. Communicate results
6. Operationalize
EMC Corporation is now Dell EMC. This model, created by David Dietrich, reflects the
cyclical nature of typical business projects. The phases aren’t static milestones; each
step connects and leads to the next, and eventually repeats. Key questions help
analysts test whether they have accomplished enough to move forward and ensure that
teams have spent enough time on each of the phases and don’t start modeling before
the data is ready. It is a little different from the data analysis process on which this
program is based on, but it has some core ideas in common: the first phase is
interested in discovering and asking questions; data has to be prepared before it can be
analyzed and used; and then findings should be shared and acted on.
SAS's iterative process
An iterative data analysis process was created by a company called SAS, a leading
data analytics solutions provider. It can be used to produce repeatable, reliable, and
predictive results:
1. Ask
2. Prepare
3. Explore
4. Model
5. Implement
6. Act
7. Evaluate
The SAS model emphasizes the cyclical nature of their model by visualizing it as an
infinity symbol. Its process has seven steps, many of which mirror the other models, like
ask, prepare, model, and act. But this process is also a little different; it includes a step
after the act phase designed to help analysts evaluate their solutions and potentially
return to the ask phase again.
Project-based data analytics process
A project-based data analytics process has five simple steps:
1. Identifying the problem
, 2. Designing data requirements
3. Pre-processing data
4. Performing data analysis
5. Visualizing data
This data analytics project process was developed by Vignesh Prajapati. It doesn’t
include the sixth phase, or the act phase. However, it still covers a lot of the same steps
described. It begins with identifying the problem, preparing and processing data before
analysis, and ends with data visualization.
Big data analytics process
Authors Thomas Erl, Wajid Khattak, and Paul Buhler proposed a big data analytics
process in their book, Big Data Fundamentals: Concepts, Drivers & Techniques. Their
process suggests phases divided into nine steps:
1. Business case evaluation
2. Data identification
3. Data acquisition and filtering
4. Data extraction
5. Data validation and cleaning
6. Data aggregation and representation
7. Data analysis
8. Data visualization
9. Utilization of analysis results
This process appears to have three or four more steps than the previous models. But in
reality, they have just broken down what has been referred to as prepare and process
into smaller steps. It emphasizes the individual tasks required for gathering, preparing,
and cleaning data before the analysis phase.
Data Ecosystems - are made up of various elements that interact with one another in
order to produce, manage, store, organize, analyze, and share data.
Data science is defined as creating new ways of modeling and understanding the
unknown by using raw data.
Data scientists create new questions using data, while analysts find answers to
existing questions by creating insights from data sources
Data-driven decision-making is defined as using facts to guide business strategy.
Gut instinct is an intuitive understanding of something with little or no explanation.
Why gut instinct can be a problem:
Answers |PA and OA|2025 Update with complete solutions
Discovering Data D468
Module 1
● Data Analysis is the collection, transformation, and organization of data in order
to draw conclusions, make predictions, and drive informed decision-making
● A Data Analyst is someone who collects, transforms, and organizes data in
order to help make informed decisions.
● Foundations: Ask>Prepare>Process>Analyze>Share>Act>Capstone
● Data analytics is the science of data
○ Data is basically a collection of facts or information, and through analysis.
● Businesses need a way to control all that data so they can use it to improve
processes, identify opportunities and trends, launch new products, serve
customers, and make thoughtful decisions.
○ For businesses to be on top of the competition, they need to be on top of
their data.
● People analytics is the practice of collecting and analyzing data on the people
who make up a company’s workforce in order to gain insights to improve how the
company operates.
● The six steps of the data analysis process are:
○ Ask, Prepare, Process, Analyze, Share, and Act
○ The purpose is to gain insights that drive informed decision-making
The Six Phases of Data Analysis
1. Ask: business challenge, objective, or question
2. Prepare: data generation, collection, storage, and data management
3. Process: data cleaning and data integrity
4. Analyze: data exploration, visualization, and analysis
5. Share: communicating and interpreting results
6. Act: putting insights to work to solve the problem
In the Ask Phase - you will work to understand the challenge to be solved or the
question to be answered - you’ll ask many questions to help you along the way
,In the Prepare Phase - you’ll find and collect the date you’ll need to answer your
questions. You’ll identify data sources, gather data, and verify that it is accurate and
useful for answering questions
The Process Phase - is when you will clean and organize your data. Task you perform
here include removing any inconsistencies; filling in missing values. Essentially, you’re
ensuring the data is ready before you begin analysis.
The Analyze Phase - is when you do the necessary data analysis to uncover answers
and solutions.
The Share Phase - when you present your findings to decision-makers through a
report, presentation, or data visualizations. As part of the share phase, you decide
which medium you want to use to share your findings and select the data to include.
Last is the Act Phase - in which you and others in the company put the data insights
into action. This could mean implementing a new business strategy, making changes to
a website, or any other action that solves the initial problem.
Key takeaways
The six phases of the data analysis process help answer business challenges, such as
understanding how to improve a retirement program. Additionally, iterating on and
reviewing your work throughout the data analysis process is critical for obtaining quality
results.
Decision Intelligence - is a combination of applied data science and the social and
managerial sciences.
Data Science - the discipline of making data useful, is an umbrella term that
encompasses three disciplines: machine learning, statistics, and analytics.
Data Analysis Process—how industry professionals move from data to decision
EMC's data analysis process
,EMC Corporation's data analytics process is cyclical with six steps:
1. Discovery
2. Pre-processing data
3. Model planning
4. Model building
5. Communicate results
6. Operationalize
EMC Corporation is now Dell EMC. This model, created by David Dietrich, reflects the
cyclical nature of typical business projects. The phases aren’t static milestones; each
step connects and leads to the next, and eventually repeats. Key questions help
analysts test whether they have accomplished enough to move forward and ensure that
teams have spent enough time on each of the phases and don’t start modeling before
the data is ready. It is a little different from the data analysis process on which this
program is based on, but it has some core ideas in common: the first phase is
interested in discovering and asking questions; data has to be prepared before it can be
analyzed and used; and then findings should be shared and acted on.
SAS's iterative process
An iterative data analysis process was created by a company called SAS, a leading
data analytics solutions provider. It can be used to produce repeatable, reliable, and
predictive results:
1. Ask
2. Prepare
3. Explore
4. Model
5. Implement
6. Act
7. Evaluate
The SAS model emphasizes the cyclical nature of their model by visualizing it as an
infinity symbol. Its process has seven steps, many of which mirror the other models, like
ask, prepare, model, and act. But this process is also a little different; it includes a step
after the act phase designed to help analysts evaluate their solutions and potentially
return to the ask phase again.
Project-based data analytics process
A project-based data analytics process has five simple steps:
1. Identifying the problem
, 2. Designing data requirements
3. Pre-processing data
4. Performing data analysis
5. Visualizing data
This data analytics project process was developed by Vignesh Prajapati. It doesn’t
include the sixth phase, or the act phase. However, it still covers a lot of the same steps
described. It begins with identifying the problem, preparing and processing data before
analysis, and ends with data visualization.
Big data analytics process
Authors Thomas Erl, Wajid Khattak, and Paul Buhler proposed a big data analytics
process in their book, Big Data Fundamentals: Concepts, Drivers & Techniques. Their
process suggests phases divided into nine steps:
1. Business case evaluation
2. Data identification
3. Data acquisition and filtering
4. Data extraction
5. Data validation and cleaning
6. Data aggregation and representation
7. Data analysis
8. Data visualization
9. Utilization of analysis results
This process appears to have three or four more steps than the previous models. But in
reality, they have just broken down what has been referred to as prepare and process
into smaller steps. It emphasizes the individual tasks required for gathering, preparing,
and cleaning data before the analysis phase.
Data Ecosystems - are made up of various elements that interact with one another in
order to produce, manage, store, organize, analyze, and share data.
Data science is defined as creating new ways of modeling and understanding the
unknown by using raw data.
Data scientists create new questions using data, while analysts find answers to
existing questions by creating insights from data sources
Data-driven decision-making is defined as using facts to guide business strategy.
Gut instinct is an intuitive understanding of something with little or no explanation.
Why gut instinct can be a problem:
