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Data Analytics for Accounting, 3rd Edition by Vernon Richardson
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TEST BANK For Data Analytics for Accounting, 3rdEdition by Vernon Richardson
The Data Analytics Process - ANSWER: 1. Data Analytics
2.Intro to Accounting Data
3.Types of Data
4.Preparing Data for Analysis
5.Types and Tools of Data Analytics
6.Descriptive Analytics
7.Diagnostic Analytics
8.Predictive Analytics
9.Prescriptive Analytics
10.Share the Story
11.Putting it All Together
2.5 Quintillion bytes of data are created every day, - ANSWER: and that number
continues to grow with no sign of slowing down.
The data that now floods the Internet every second is equivalent to - ANSWER: the
data stored on the entire Internet 20 years ago.
The abundance of data can be helpful in addressing company questions, problem,
and challenges to the extent that - ANSWER: accountants can harness and analyze
the available data.
The increasing amount of data may cause - ANSWER: information overload and
hinder the work of the accountant.
How to deal with automation: - ANSWER: While computers increasingly collect the
data, they do not have the accounting expertise that accountants provide.
Data Analytics is one way for accountants to develop and exhibit this needed
expertise.
Data is not the answer. It may help answer question, but that data is a tool of the
analyst.
Bloom's Taxonomy - ANSWER: 1. Remember
2. Understand
3. Apply
4. Analyze
5. Evaluate
6. Create
, The basic academia accounting curriculum most directly addresses to the three
lowest levels of Bloom's taxonomy. - ANSWER: Remember, Understand, and Apply.
Data analytics moved us into the higher order thinking skills. - ANSWER: Accountants
simply cannot analyze, evaluate, and create if they do not already have the basic
accounting knowledge and understanding required by the lower level skills.
The AMPS Model: - ANSWER: 1. Ask the Question
2. Master the Data
3. Perform the Analysis
4. Share the Story
Ask the Question - ANSWER: Your Data Won't Speak Unless You Ask It The Right Data
Analytics Questions"
-Diagnostic Analytics
-Predictive Analytics
-Prescriptive Analytics
Diagnostic Analytics - ANSWER: Why did it happen? What are the root causes of past
results?
Predictive Analytics - ANSWER: Will it happen in the future? What is the probability
something will happen? Is it forecastable?
Prescriptive Analytics - ANSWER: What should we do, based on what we expect will
happen? How do we optimize our performance based on potential constraints?
Master the Data - ANSWER: Can the data answer/address the question?
Does the data exhibit data integrity (accurate, valid and consistent)?
Cost of Acquiring vs. Benefit of Using the Data
Type of Data: Categorical vs. Numerical
Does the data exhibit data integrity (accurate, valid and consistent)? - ANSWER: Does
the data have errors?
Is data missing?
Is the data biased?
Cost of Acquiring vs. Benefit of Using the Data - ANSWER: Who owns the data?
Is the data hard to access?
Type of Data: Categorical vs. Numerical - ANSWER: What type of analysis does the
data allow us to do?
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