DATA ANALYTICS IN ACCOUNTING
CHAPTER 1
Data analytics is revolutionizing both the business landscape and the accounting profession.
This chapter:
- Understand the core concepts of data analytics and its transformative impact.
- Defining data analytics + insights it provides to businesses and accounting professionals.
- Importance of adopting an analytics mindset.
- IMPACT cycle: data analytics process and how it can be applied to address critical questions in
business and accounting.
1. GENERAL INTRODUCTION
Define data analytics
- Data analytics = the process of transforming and evaluating data with the purpose of drawing
conclusions to address business questions
- Effective DA provides a way to search through large structured and unstructured data to identify
unknown patterns or relationships
- Goal: transform (big) data into valuable knowledge to make more informed business decisions
- Big data = datasets which are too large and complex to be analysed traditionally
- Remember the 4V’s:
o Volume = size
o Velocity = speed of processing
o Variety = different types of data
o Veracity = data quality
How does DA affect business?
- By the numbers:
o Global volume of data created: hundreds of zettabytes/year
o 85% of CEOs put a high value on DA
o 86% of CEOs place data mining and analysis as 2 nd most important strategic technology
o Business analytics tops CEO’s list of priorities
o DA could generate up to $2 trillion in value per year
- DA is expected to have dramatic effects on auditing and financial reporting + tax and managerial
accounting
How does DA affect auditing?
- DA enhances audit quality
- Audit process is changing from a traditional toward a more automated process
- DA enables enhanced audits, expanded services, and added value to clients
1
,How does DA affect management accounting?
- DA enhances cost analysis
- DA enables better decision-making
- DA enables better forecasting, budgeting, production, and sales
How does DA affect financial reporting?
- Accountants make better estimates of collectability, write-downs, …
- Managers better understand the business environment through social media + other external
date sources
- Analysts identify risks and opportunities through analysis of internet searches
2. INTRODUCTION IMPACT MODEL
How does DA make an IMPACT
- I: identify the questions
- M: master the data
- P: perform the test plan
- A: address and refine results
- C: communicate insights
- T: track outcomes
Step 1: identify the questions
Understand the business problems that need to be addressed
Attributes to consider:
- What data do we need to answer the question?
- Who is the audience that will use the results?
- Is the scope of the question too narrow or too broad?
- How will the results be used?
Step 2: master the data
Consider the following 8 elements
- Know what data are available and how they relate to the problem
- Data available in Internal systems
- Data available in External networks and data warehouses (e.g. government data)
- Data dictionaries (details about variables: categorial, …)
- ETL (extraction, transformation, and loading)
- Data validation and completeness (ensure that data is reliable)
- Data normalization (reduce data redundancy)
- Data preparation and scrubbing (cleaning data to remove errors, irrelevant information)
Step 3: perform the test plan
2
,Identify a relationship between the response (or dependent) variable and those items that affect the
response (predictor = explanatory = independent variables)
Generally, we make a model, or a simplified representation of reality to address this purpose
For example, predict the performance on the next accounting exam:
- The response/dependent variable: score on the exam
- The independent variables: study time, IQ, score on last exam, …
8 key approaches to DA depending on the question (Provost and Fawcett):
- Classification: assign each unit in a population to a specific pre-defined category or class
o E.g. whether a law should be approved or denied
- Regression: predict a continuous dependent variable’s value based on independent variable
inputs using a statistical model
o Can help to set a relationship between variables
- Similarity matching: identify similar individuals or items based on known data
o Focusses on pairwise matching, not big groups
o E.g. address data “123 Main St.” Vs. “123 Main Street”)
- Clustering: divide individuals or items into meaningful or useful groups (without predefined
categories)
o Segmenting customers based on shopping frequency, …
o No predefined categories
- Co-occurrence grouping: discover relationships between individuals/items based on shared
transactions
o Observed associations
- Link Prediction: predict connections/relationships between two data items
o Unobserved associations
- Profiling: characterise the typical behaviour of an individual, group, or population by generating
summary statistics about the data
o Describe, rather than group
- Data reduction: reduce the amount of information being analysed to focus on the most critical
and relevant elements
3
, Step 4: address and refine results
- Identify issues with the analyses, possible issues, and refine the model
- Ask further questions
- Explore the data
- Rerun analyses
Step 5 & 6: communicate insights & track outcomes
Step 5:
- Communicate effectively using clear language and visualisations
- Dashboards
- Static reports
- Summaries
Step 6:
- Follow up on the results of the analysis
- How frequently should the analysis be performed
- Have the analytics changed
- What are the trends
What DA skills do accountants need
- Articulate business problems
- Communicate with data scientists
- Draw appropriate conclusions
- Present results in an accessible manner
- Develop an analytics mindset 7 important areas:
o Know when and how data analytics can address business questions
o Data scrubbing and data preparation
o Data quality
o Descriptive data analysis
4
CHAPTER 1
Data analytics is revolutionizing both the business landscape and the accounting profession.
This chapter:
- Understand the core concepts of data analytics and its transformative impact.
- Defining data analytics + insights it provides to businesses and accounting professionals.
- Importance of adopting an analytics mindset.
- IMPACT cycle: data analytics process and how it can be applied to address critical questions in
business and accounting.
1. GENERAL INTRODUCTION
Define data analytics
- Data analytics = the process of transforming and evaluating data with the purpose of drawing
conclusions to address business questions
- Effective DA provides a way to search through large structured and unstructured data to identify
unknown patterns or relationships
- Goal: transform (big) data into valuable knowledge to make more informed business decisions
- Big data = datasets which are too large and complex to be analysed traditionally
- Remember the 4V’s:
o Volume = size
o Velocity = speed of processing
o Variety = different types of data
o Veracity = data quality
How does DA affect business?
- By the numbers:
o Global volume of data created: hundreds of zettabytes/year
o 85% of CEOs put a high value on DA
o 86% of CEOs place data mining and analysis as 2 nd most important strategic technology
o Business analytics tops CEO’s list of priorities
o DA could generate up to $2 trillion in value per year
- DA is expected to have dramatic effects on auditing and financial reporting + tax and managerial
accounting
How does DA affect auditing?
- DA enhances audit quality
- Audit process is changing from a traditional toward a more automated process
- DA enables enhanced audits, expanded services, and added value to clients
1
,How does DA affect management accounting?
- DA enhances cost analysis
- DA enables better decision-making
- DA enables better forecasting, budgeting, production, and sales
How does DA affect financial reporting?
- Accountants make better estimates of collectability, write-downs, …
- Managers better understand the business environment through social media + other external
date sources
- Analysts identify risks and opportunities through analysis of internet searches
2. INTRODUCTION IMPACT MODEL
How does DA make an IMPACT
- I: identify the questions
- M: master the data
- P: perform the test plan
- A: address and refine results
- C: communicate insights
- T: track outcomes
Step 1: identify the questions
Understand the business problems that need to be addressed
Attributes to consider:
- What data do we need to answer the question?
- Who is the audience that will use the results?
- Is the scope of the question too narrow or too broad?
- How will the results be used?
Step 2: master the data
Consider the following 8 elements
- Know what data are available and how they relate to the problem
- Data available in Internal systems
- Data available in External networks and data warehouses (e.g. government data)
- Data dictionaries (details about variables: categorial, …)
- ETL (extraction, transformation, and loading)
- Data validation and completeness (ensure that data is reliable)
- Data normalization (reduce data redundancy)
- Data preparation and scrubbing (cleaning data to remove errors, irrelevant information)
Step 3: perform the test plan
2
,Identify a relationship between the response (or dependent) variable and those items that affect the
response (predictor = explanatory = independent variables)
Generally, we make a model, or a simplified representation of reality to address this purpose
For example, predict the performance on the next accounting exam:
- The response/dependent variable: score on the exam
- The independent variables: study time, IQ, score on last exam, …
8 key approaches to DA depending on the question (Provost and Fawcett):
- Classification: assign each unit in a population to a specific pre-defined category or class
o E.g. whether a law should be approved or denied
- Regression: predict a continuous dependent variable’s value based on independent variable
inputs using a statistical model
o Can help to set a relationship between variables
- Similarity matching: identify similar individuals or items based on known data
o Focusses on pairwise matching, not big groups
o E.g. address data “123 Main St.” Vs. “123 Main Street”)
- Clustering: divide individuals or items into meaningful or useful groups (without predefined
categories)
o Segmenting customers based on shopping frequency, …
o No predefined categories
- Co-occurrence grouping: discover relationships between individuals/items based on shared
transactions
o Observed associations
- Link Prediction: predict connections/relationships between two data items
o Unobserved associations
- Profiling: characterise the typical behaviour of an individual, group, or population by generating
summary statistics about the data
o Describe, rather than group
- Data reduction: reduce the amount of information being analysed to focus on the most critical
and relevant elements
3
, Step 4: address and refine results
- Identify issues with the analyses, possible issues, and refine the model
- Ask further questions
- Explore the data
- Rerun analyses
Step 5 & 6: communicate insights & track outcomes
Step 5:
- Communicate effectively using clear language and visualisations
- Dashboards
- Static reports
- Summaries
Step 6:
- Follow up on the results of the analysis
- How frequently should the analysis be performed
- Have the analytics changed
- What are the trends
What DA skills do accountants need
- Articulate business problems
- Communicate with data scientists
- Draw appropriate conclusions
- Present results in an accessible manner
- Develop an analytics mindset 7 important areas:
o Know when and how data analytics can address business questions
o Data scrubbing and data preparation
o Data quality
o Descriptive data analysis
4
