Data Analytics in Accounting
Chapter 1: General Introduction
Chapter Objectives
Define Data Analytics
Understand why Data Analytics matters to business
Explain why Data Analytics matters to accountants
Describe the Data Analytics Process using the IMPACT cycle
Explain how to translate common business questions into fields and
values
What is Data Analytics?
Data Analytics is the process of transforming and evaluating data with the
purpose of drawing conclusions to address business questions.
Effective Data Analytics provides a way to search through large structured
and unstructured data to identify unknown patterns or relationships.
Big Data refers to datasets which are too large and complex to be
analyzed traditionally.
Volume (refers to size of dataset)
Velocity (refers to speed of processing)
Variety (refers to different types of data)
Veracity (refers to the data quality)
Goal is to transform big data into valuable knowledge to make more
informed business decisions.
We want to extract meaningful insights from data to answer business
questions
Transform raw data into meaningful insights (purpose) -> turn data into
knowledge that creates value (data analytics)
How does data analytics affect business?
Data Analytics is expected to have dramatic effects on auditing and
financial reporting as well as tax and managerial accounting
1
,More data is created in the last 2 years, than in the entire previous history
of the human race
How does data analytics affect auditing?
Auditors can now check all the transactions and not just a few like
previously, and this gives some advantages
o Data analytics enhances audit quality
o The audit process is changing from a traditional process toward a
more automated one
o Data analytics enables enhanced audits, expanded services, and
added value to clients
How does data analytics affect management accounting?
o Data analytics enhances cost analysis
o Data analytics enables better decision-making
o Data analytics enables better forecasting, budgeting, production and
sales
How does data analytics affect financial reporting?
o Accountants make better estimates of collectability, write-downs, etc
o Managers better understand the business environment through
social media and other external data sources
o Analysts identify risks and opportunities through analysis of Internet
searches
Data Analytics is really changing how accountants work and this is not just
a passing trend but a durable change
2
,Chapter 1: The IMPACT Model
How does data analytics make an IMPACT?
Identify the questions
-> Start with a clear/concrete question that data can
potentially answer
Master the data
-> Determine what data is available? How to access
it? Is it complete and reliable?
Perform the test plan
-> We want to use the appropriate method to
investigate the data and draw insights
Address and refine results
-> We want to interpret the findings and cycle back if the question isn’t
answered
Communicate insights
-> Present your results in a clear/ understandable way
Track outcomes
-> Follow up on how well your recommendations or predictions do it over
time and adjust if necessary
This is a cycle, the process never finishes (because you always get a new
question from a first question)
Step 1: Identify the Question
Understand the business problems that need to be addressed
Attributes to consider:
o What data do we need to answer the question?
o Who is the audience that will use the results?
o Is the scope of the question too narrow or too broad?
o How will the results be used?
Depending on these attributes we will use different questions or different
approaches
Example of questions:
Are employees circumventing internal controls over payments?
3
, Are there any suspicious travel and entertainment expenses?
Are our customers paying us in timely manner?
How can we predict the allowance for loan losses for our bank loans?
How can we find transactions that are risky in terms of accounting
issues?
Who authorizes checks above $100,000?
How can errors be identified?
Step 2: Master the Data
Consider the following 8 elements:
o Know what data are available and how they relate to the problem
o Data available in Internal systems
o Data available in External networks and data warehouses
o Data dictionaries
o ETL – Extraction, Transformation and Loading
o Data validation and completeness
o Data normalization
o Data preparation and scrubbing most crucial and time consuming
(it can take up to 50% - 90% of your time)
Step 3: Perform the Test Plan
Identify a relationship between the response (or dependent) variable and
those items that affect the response (also called the predictor,
explanatory, or independent variables)
Generally, we make a model, or a simplified representation of reality, to
address this purpose.
EXAMPLE: Predict the performance on the next accounting exam:
response/ dependent variable: score on the exam
independent variables: study time, IQ, score on last exam, etc
Provost and Fawcett, (Data Science for Business), identify 8 key
approaches to Data Analytics depending on the question:
1. Classification – assign each unit in a population to a specific
(pre-defined) category or class
4
Chapter 1: General Introduction
Chapter Objectives
Define Data Analytics
Understand why Data Analytics matters to business
Explain why Data Analytics matters to accountants
Describe the Data Analytics Process using the IMPACT cycle
Explain how to translate common business questions into fields and
values
What is Data Analytics?
Data Analytics is the process of transforming and evaluating data with the
purpose of drawing conclusions to address business questions.
Effective Data Analytics provides a way to search through large structured
and unstructured data to identify unknown patterns or relationships.
Big Data refers to datasets which are too large and complex to be
analyzed traditionally.
Volume (refers to size of dataset)
Velocity (refers to speed of processing)
Variety (refers to different types of data)
Veracity (refers to the data quality)
Goal is to transform big data into valuable knowledge to make more
informed business decisions.
We want to extract meaningful insights from data to answer business
questions
Transform raw data into meaningful insights (purpose) -> turn data into
knowledge that creates value (data analytics)
How does data analytics affect business?
Data Analytics is expected to have dramatic effects on auditing and
financial reporting as well as tax and managerial accounting
1
,More data is created in the last 2 years, than in the entire previous history
of the human race
How does data analytics affect auditing?
Auditors can now check all the transactions and not just a few like
previously, and this gives some advantages
o Data analytics enhances audit quality
o The audit process is changing from a traditional process toward a
more automated one
o Data analytics enables enhanced audits, expanded services, and
added value to clients
How does data analytics affect management accounting?
o Data analytics enhances cost analysis
o Data analytics enables better decision-making
o Data analytics enables better forecasting, budgeting, production and
sales
How does data analytics affect financial reporting?
o Accountants make better estimates of collectability, write-downs, etc
o Managers better understand the business environment through
social media and other external data sources
o Analysts identify risks and opportunities through analysis of Internet
searches
Data Analytics is really changing how accountants work and this is not just
a passing trend but a durable change
2
,Chapter 1: The IMPACT Model
How does data analytics make an IMPACT?
Identify the questions
-> Start with a clear/concrete question that data can
potentially answer
Master the data
-> Determine what data is available? How to access
it? Is it complete and reliable?
Perform the test plan
-> We want to use the appropriate method to
investigate the data and draw insights
Address and refine results
-> We want to interpret the findings and cycle back if the question isn’t
answered
Communicate insights
-> Present your results in a clear/ understandable way
Track outcomes
-> Follow up on how well your recommendations or predictions do it over
time and adjust if necessary
This is a cycle, the process never finishes (because you always get a new
question from a first question)
Step 1: Identify the Question
Understand the business problems that need to be addressed
Attributes to consider:
o What data do we need to answer the question?
o Who is the audience that will use the results?
o Is the scope of the question too narrow or too broad?
o How will the results be used?
Depending on these attributes we will use different questions or different
approaches
Example of questions:
Are employees circumventing internal controls over payments?
3
, Are there any suspicious travel and entertainment expenses?
Are our customers paying us in timely manner?
How can we predict the allowance for loan losses for our bank loans?
How can we find transactions that are risky in terms of accounting
issues?
Who authorizes checks above $100,000?
How can errors be identified?
Step 2: Master the Data
Consider the following 8 elements:
o Know what data are available and how they relate to the problem
o Data available in Internal systems
o Data available in External networks and data warehouses
o Data dictionaries
o ETL – Extraction, Transformation and Loading
o Data validation and completeness
o Data normalization
o Data preparation and scrubbing most crucial and time consuming
(it can take up to 50% - 90% of your time)
Step 3: Perform the Test Plan
Identify a relationship between the response (or dependent) variable and
those items that affect the response (also called the predictor,
explanatory, or independent variables)
Generally, we make a model, or a simplified representation of reality, to
address this purpose.
EXAMPLE: Predict the performance on the next accounting exam:
response/ dependent variable: score on the exam
independent variables: study time, IQ, score on last exam, etc
Provost and Fawcett, (Data Science for Business), identify 8 key
approaches to Data Analytics depending on the question:
1. Classification – assign each unit in a population to a specific
(pre-defined) category or class
4
