Applying Data Analytics to Business Performance
docx
keyboard_arrow_up
School
Southern New Hampshire University *
*We aren’t endorsed by this school
Course
530
Subject
Information Systems
Date
Jan 9, 2024
Type
docx
Pages
7
Uploaded by ElderPencilParrot14
Quiz One: Passed with 80%
1.
Which is not a reason to use data analytics in business performance?
a.
Increase sales.
b.
Improve productivity.
c.
Eliminate risk and fraud.
d.
Improve the customer experience.
2.
The financials collected for month-end, quarter, or full year are an example of what type of data?
a.
Mobile data and IOT data.
b.
Time-series data
.
c.
Machine-to-machine data.
d.
Human-generated data.
3.
Which is not a question you would normally ask when evaluating organizational capabilities and gaps?
a.
What are our customers saying about the products they're buying?
b.
What data does the organization already use to make strategic decisions?
c.
What data or information needs to be collected to help the organization grow and
create value?
d.
Will the cost and effort of collecting the data outweigh the benefits this information will provide?
4.
When analyzing historical revenue, which question are you most likely to ask?
a.
What are the factors that influence purchasing behavior?
b.
What is the profit contribution of each product line?
c.
Who are our competitors?
d.
What are our customers saying about the products they're buying?
5.
The contactless payments made in a retail outlet are an example of what type of data?
a.
Human-generated data.
b.
Mobile data and IOT data.
c.
Nonfinancial data.
d.
Machine-to-machine data.
Quiz Two: Passed with 80%
1.
The management of Alset asked you to perform analysis on past financial results to generate forward-looking insights. Which type of data analytics is this an example of?
a.
Diagnostic analytics.
b.
Descriptive analytics.
c.
Predictive analytics.
d.
Prescriptive analytics.
2.
What type of data are the social media posts Alset used to better understand customers?
a.
Ordered quantitative data.
b.
Underordered quantitative data.
c.
Unordered qualitative data.
d.
Ordered qualitative data.
3.
What is the definition of advanced analytics?
a.
Using data to help identify root causes in a situation.
b.
Using data insights to inform scenarios that might take place in the future.
c.
Working with large amounts of complex data sets using various data analytics tools to derive insights.
d.
Delivering data insights to describe a situation or a scenario.
4.
Using Alset's annual sales by operating segment to describe a situation or scenario is an
example of which type of data analytics?
a.
Descriptive.
b.
Diagnostic.
c.
Predictive.
d.
Prescriptive.
5.
Which type of data analytics would you be using if you wanted to find out what should be
done?
a.
Descriptive.
b.
Diagnostic.
c.
Predictive.
d.
Prescriptive.
Quiz Three: Passed with 76%
1.
Which best describes a digital mindset?
a.
The process of identifying and resolving technical issues when using digital tools.
b.
A way of thinking that enables you to imagine better, technology-enabled ways of getting things done.
c.
The use of technology to replace human work to streamline onerous workflows, innovate, and improve on existing solutions.
d.
The ability to evaluate, reflect on, and interpret a problem or potential opportunity.
2.
Which is not a benefit of having an analytical and data-driven mindset?
a.
It helps individuals and organizations make informed decisions.
b.
It helps you ask the right questions and translate an unclear request into a well-
defined analytical problem.
c.
It enables you to improve on existing solutions by replacing human work with technology.
d.
It enables you to be aware of your own biases, so you are more likely to stay objective and clear minded.
3.
Why is it important to frame and scope a data analytics project properly?
a.
To ensure alignment among stakeholders of the data analytics project.
b.
To ensure that the same data analytics project has not been done previously.
c.
To ensure that not all the processes are automated through data analytics.
d.
To ensure that the probability of success of the data analytics project is over 15%.
4.
How would you define business intelligence?
a.
Evaluating, reflecting on, and interpreting a problem or potential opportunity.
b.
Ordering your thoughts in a logical and consistent way.
c.
Presenting data analyses in clear and actionable ways.
d.
Identifying and resolving technical issues when using digital tools.
5.
Which of the four key personal attributes needed to help you succeed in a digital world includes the ability to identify data sources and work with data to assess a problem?
a.
Critical thinking skills.
b.
Problem-solving skills.
c.
Analytical skills.
d.
Communication.
6.
What is data analytics?
a.
An interdisciplinary field incorporating computer science, software engineering, and statistics.
b.
Analyzing information to identify trends and answer important questions.
c.
A way of thinking that enables you to imagine better, technology-enabled ways of
getting things done.
d.
Presenting data analyses in clear and actionable ways.
7.
Which type of data analytics uses data to help identify root causes in a situation?
Your preview ends here
Eager to read complete document? Join bartleby learn and gain access to the full version
- Access to all documents
- Unlimited textbook solutions
- 24/7 expert homework help
a.
Descriptive.
b.
Diagnostic.
c.
Predictive.
d.
Prescriptive.
8.
Why is it important to define the objective of a data analytics project?
a.
To understand and align the purpose and goal of the data analytics project.
b.
To identify stakeholders who need to be consulted during the project.
c.
To define the scope of work and range of scenarios to be explored.
d.
To identify what data needs to be gathered.
9.
Which of these is an example of a socioeconomic factor that may influence your data analytics project?
a.
Proximity to competitors.
b.
Improvements in the transportation infrastructure.
c.
Changes in the regulatory environment.
d.
Customer-driven boycotts due to sentiment on a particular issue.
10. What is data?
a.
Data is structured.
b.
Data is factual information or facts.
c.
Data is a structure or classification.
d.
Data is numerical information.
11. What are the six Vs of data?
a.
Volume, vastness, velocity, veracity, value, and variability.
b.
Volume, variety, velocity, versatility, value, and variability.
c.
Volume, variety, velocity, veracity, value, and variability.
d.
Volume, variety, veracity, value, vulnerability, and variability.
12. Which of these is not a dimension of data?
a.
Data complexity.
b.
Data quality.
c.
Data integrity.
d.
Data storage.
13. The process of data cleansing is what type of data analytics challenge?
a.
Ensuring data privacy and security.
b.
Gaining senior management buy-in.
c.
Making data usable.
d.
Identifying and collecting meaningful data.
14. Feature engineering identifies and mines specific attributes and variables of a data set to
facilitate which types of data analytics?
a.
Predictive and advanced analytics.
b.
Prescriptive and descriptive analytics.
c.
Descriptive and predictive analytics.
d.
Prescriptive and advanced analytics.
15. What is the main difference between qualitative and quantitative data?
a.
Qualitative data is considered less valuable than quantitative data.
b.
Quantitative data is always structured; qualitative data is always unstructured.
c.
Qualitative data is nonnumerical; quantitative data is numerical.
d.
Quantitative data can be used in data analytics projects, unlike qualitative data.
16. What is the difference between structured and unstructured data?
a.
Structured data generally takes up less storage space than unstructured data.
b.
Unstructured data cannot normally be represented by rows and columns in
a table, unlike structured data.
c.
Unstructured data describes video data, whereas structured data describes image data.
d.
Structured data is used much more often in data analytics projects.
17. Data generated from a physical object that is connected to the web is described as what?
a.
Internet of things (IoT) data.
b.
Geographic data.
c.
Biometric data.
d.
Data exhaust.
18. Which graph or chart should you use when exploring the relationship between two sets of data, for example, when comparing diameter and height?
a.
Line chart.
b.
Bar chart.
c.
Scatterplot.
d.
Pie chart.
19. Which is not an example of an outcome a machine learning model could be used to predict?
a.
Whether a customer will default.
b.
Whether a text message is spam.
c.
Which customer is most likely to leave.
d.
Which product performed best in the previous 12-month period.
20. What is the definition of predictive analytics?
a.
Working with large amounts of complex data sets using various data analytics tools.
b.
Identifying root causes in a situation in which there is a lot of noise or outliers in the data set.
c.
Using data insights to inform actions that can course correct an underlying problem or create value for the organization.
d.
Using existing data to find patterns that can be used to understand future outcomes.
21. Which data storage or software tool is used to unify vast amounts of raw data of all types
from across an organization?
a.
Data warehouse.
b.
Master data management (MDM). c.
Data lake.
d.
Database.
22. Which step of the data life cycle is concerned with pulling data together from a variety of sources, both internal and external?
a.
Identify.
b.
Capture.
c.
Manage.
d.
Utilize.
23. Which term describes the graphical representation of data?
a.
Data transparency.
b.
Data stewardship.
c.
Data integrity.
d.
Data visualization.
24. Which is not a basic data governance principle?
a.
Transparency.
b.
Accountability.
c.
Standardization.
d.
Comprehensiveness.
25. Which term describes the overall management of data availability, usability, integrity, and security?
a.
Data governance.
b.
Data quality.
c.
Data privacy.
d.
Data stewardship.
Your preview ends here
Eager to read complete document? Join bartleby learn and gain access to the full version
- Access to all documents
- Unlimited textbook solutions
- 24/7 expert homework help
Quiz Four: Passed with 80%
1.
What is the main reason for using data analytics in revenue analysis?
a.
To provide insights and understanding about revenue growth, revenue drivers, and revenue quality.
b.
To provide insights into how revenue recognition is performed in the organization.
c.
To provide insights into cash flow and how an organization derives free cash flow.
d.
To provide insights into historical revenue trends for financial reporting.
2.
When using data analytics to analyze revenue, what is the first task you would typically complete?
a.
Review historical revenue analysis and observations.
b.
Evaluate organizational capability and gaps that may affect the project.
c.
Define the objective of the project.
d.
Frame the revenue analysis questions.
3.
At what stage in the data analytics project for revenue analysis would you evaluate data visualization and business intelligence requirements?
a.
At the very beginning of the revenue analysis project.
b.
After reviewing historical revenue analysis but before framing the revenue analysis questions.
c.
After evaluating organizational capabilities and gaps but before considering other
factors.
d.
After gathering requirements and considering other factors.
4.
Which question will not help you assess organizational capabilities and identify gaps?
a.
What are the pricing trends and expectations?
b.
Who owns and maintains the data?
c.
Is historical revenue data available in different views?
d.
Is data on pricing and promotions available?
5.
When framing the revenue analysis questions, what is not a question you would ask?
a.
What are the pricing trends and expectations?
b.
Who owns the data, and how often is it updated?
c.
What is the projected revenue growth for a future time horizon?
d.
What other factors might affect the revenue trajectory?