DATA ANALYTICS FOR ACCOUNTING
Axl Paesen
CHAPTER 1. DATA ANALYTICS FOR ACCOUNTING AND BUSINESS
1.1 WHY IS DATA ANALYTICS IMPORTANT FOR ACCOUNTANTS
WHAT IS DATA ANALYTICS?
Data Analytics = 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 = Datasets which are too large and complex to be analyzed traditionally.
Remember the 4 V’s:
1) Volume
2) Velocity
3) Variety
4) Veracity
The goal is to transform (big) data into valuable knowledge to make more informed business decisions
HOW DOES DATA ANALYTICS EFFECTS BUSINESS?
- The global volume of data created is in the hundreds of zettabytes per year.
- 85% of CEOs put a high value on Data Analytics.
- 86% of CEOs place data mining and analysis as the second-most important strategic
technology.
- Business analytics tops CEO’s list of priorities.
- Data Analytics could generate up to $2 trillion in value per year.
HOW DOES DATA ANALYTICS AFFECT AUDITING?
- Data analytics enhances audit quality
- The audit process is changing from a traditional process toward a more automated one
- Data analytics enables enhanced audits, expanded services, and added value to clients
HOW DOES DATA ANALYTICS AFFECT MANAGEMENT ACCOUNTING?
- Data analytics enhances cost analysis
- Data analytics enables better decision-making
- Data analytics enables better forecasting, budgeting, production, and sales
HOW DOES DATA ANALYTICS AFFECT FINANCIAL REPORTING?
- Accountants make better estimates of collectability, write-downs, etc.
- Managers better understand the business environment through social media and other
external data sources
- Analyst identify risks and opportunities through analysis of internet searches
,1.2 THE IMPACT MODEL
HOW DOES DATA ANALYTICS MAKE AN IMPACT?
The IMPACT model:
- Identify the questions
- Master the data
- Perform the test plan
- Address and refine results
- Communicate insights
- Track outcomes
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?
2. Master the data
consider these 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
- Data dictionaries
- ETL- extraction, transformation and loading
- Data validation and completeness
- Data normalization
- Data preparation and scrubbing
3. Perform the test plan
Identify a relationship between the response variable (dependent variable) and those items that
affect the response (predictor, explanatory, independent variables).
e.g. predict the performance on the next accounting exam:
the response/dependent variable: score on the exam
the independent variables: study time, IQ, score on the exam, etc
8 key approaches to data analytics depending on the question (Provost and Fawcett)
1) Classification – assign each unit in a population to a specific category or class
2) Regression – predict a continuous dependent variables value based on independent
variable inputs using a statistical model
3) Similarity matching – identify similar individuals or items based on known data
4) Clustering – divide individuals or items into meaningful or useful groups (without
predefined categories)
5) Co-occurrence grouping – discover associations or relationships between individuals or
items based on shared transactions
6) Link prediction – predict connections between two data items
7) Profiling – characterize the typical behavior of an individual, group or population by
generating summary statistics about data
8) Data reduction – reduce the amount of information being analyzed to focus on the most
critical and relevant element
,4. Address and refine results
- Identify issues with the analyses, possible issues and refine the model
- Ask further questions
- Explore the data
- Rerun analyses
5. Communicate insights
communicate effectively using clear language and visualizations
- Dashboards
- Static reports
- Summaries
6. Track outcomes
follow up on the results of the analysis
- How frequently should the analysis be preformed?
- Have the analytics changed?
- What are the trends?
WHAT DATA ANALYTIC SKILLS DO ACCOUNTANTS NEED?
- Articulate business problems
- Communicate with data scientists
- Draw appropriate conclusions
- Present results in an accessible manner
- Develop an analytics mindset
DEVELOP ANALYTICAL MINDSET?
There are 7 important areas
1) Know when and how data analytics can address business questions
2) Data scrubbing and data preparation
3) Data quality
4) Descriptive data analysis
5) Data analysis through data manipulation
6) Statistical data analysis competency
7) Data visualization and data reporting
, 1.3 HANDS ON EXAMPLE OF THE IMPACT MODEL
Follow the 6 steps from the IMPACT cycle and form a correct conclusion. Be precise and exact.
Remember that the impact cycle is never done and that all the steps follow up on each other.
In the PowerPoint they use the example of LendingClub and the use of the impact model to determine
if someone is likely to get a loan or not. In other words, what are the characteristics of rejected loans?
1.4 SUMMARY
Value of Data Analytics for businesses and accountants in business, auditing, managerial
accounting, financial accounting, and tax accounting.
IMPACT model introduced as a method to address accounting questions.
Importance of identifying the question emphasized, with the first steps of the IMPACT model
discussed.
Eight data analysis approaches introduced: classification, regression, similarity matching, clustering,
co-occurrence grouping, profiling, link prediction, and data reduction.
Data analytics in auditing: full transaction testing, anomaly detection, and impact on financial and tax
accounting.
Data analytics skills for accountants
- Developed analytics mindset
- Data scrubbing & preparation
- Data quality
- Descriptive data analysis
- Data manipulation
- Statistical data analysis
- Data visualization & reporting
Practical example: IMPACT cycle applied to rejected loans at LendingClub.
Axl Paesen
CHAPTER 1. DATA ANALYTICS FOR ACCOUNTING AND BUSINESS
1.1 WHY IS DATA ANALYTICS IMPORTANT FOR ACCOUNTANTS
WHAT IS DATA ANALYTICS?
Data Analytics = 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 = Datasets which are too large and complex to be analyzed traditionally.
Remember the 4 V’s:
1) Volume
2) Velocity
3) Variety
4) Veracity
The goal is to transform (big) data into valuable knowledge to make more informed business decisions
HOW DOES DATA ANALYTICS EFFECTS BUSINESS?
- The global volume of data created is in the hundreds of zettabytes per year.
- 85% of CEOs put a high value on Data Analytics.
- 86% of CEOs place data mining and analysis as the second-most important strategic
technology.
- Business analytics tops CEO’s list of priorities.
- Data Analytics could generate up to $2 trillion in value per year.
HOW DOES DATA ANALYTICS AFFECT AUDITING?
- Data analytics enhances audit quality
- The audit process is changing from a traditional process toward a more automated one
- Data analytics enables enhanced audits, expanded services, and added value to clients
HOW DOES DATA ANALYTICS AFFECT MANAGEMENT ACCOUNTING?
- Data analytics enhances cost analysis
- Data analytics enables better decision-making
- Data analytics enables better forecasting, budgeting, production, and sales
HOW DOES DATA ANALYTICS AFFECT FINANCIAL REPORTING?
- Accountants make better estimates of collectability, write-downs, etc.
- Managers better understand the business environment through social media and other
external data sources
- Analyst identify risks and opportunities through analysis of internet searches
,1.2 THE IMPACT MODEL
HOW DOES DATA ANALYTICS MAKE AN IMPACT?
The IMPACT model:
- Identify the questions
- Master the data
- Perform the test plan
- Address and refine results
- Communicate insights
- Track outcomes
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?
2. Master the data
consider these 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
- Data dictionaries
- ETL- extraction, transformation and loading
- Data validation and completeness
- Data normalization
- Data preparation and scrubbing
3. Perform the test plan
Identify a relationship between the response variable (dependent variable) and those items that
affect the response (predictor, explanatory, independent variables).
e.g. predict the performance on the next accounting exam:
the response/dependent variable: score on the exam
the independent variables: study time, IQ, score on the exam, etc
8 key approaches to data analytics depending on the question (Provost and Fawcett)
1) Classification – assign each unit in a population to a specific category or class
2) Regression – predict a continuous dependent variables value based on independent
variable inputs using a statistical model
3) Similarity matching – identify similar individuals or items based on known data
4) Clustering – divide individuals or items into meaningful or useful groups (without
predefined categories)
5) Co-occurrence grouping – discover associations or relationships between individuals or
items based on shared transactions
6) Link prediction – predict connections between two data items
7) Profiling – characterize the typical behavior of an individual, group or population by
generating summary statistics about data
8) Data reduction – reduce the amount of information being analyzed to focus on the most
critical and relevant element
,4. Address and refine results
- Identify issues with the analyses, possible issues and refine the model
- Ask further questions
- Explore the data
- Rerun analyses
5. Communicate insights
communicate effectively using clear language and visualizations
- Dashboards
- Static reports
- Summaries
6. Track outcomes
follow up on the results of the analysis
- How frequently should the analysis be preformed?
- Have the analytics changed?
- What are the trends?
WHAT DATA ANALYTIC SKILLS DO ACCOUNTANTS NEED?
- Articulate business problems
- Communicate with data scientists
- Draw appropriate conclusions
- Present results in an accessible manner
- Develop an analytics mindset
DEVELOP ANALYTICAL MINDSET?
There are 7 important areas
1) Know when and how data analytics can address business questions
2) Data scrubbing and data preparation
3) Data quality
4) Descriptive data analysis
5) Data analysis through data manipulation
6) Statistical data analysis competency
7) Data visualization and data reporting
, 1.3 HANDS ON EXAMPLE OF THE IMPACT MODEL
Follow the 6 steps from the IMPACT cycle and form a correct conclusion. Be precise and exact.
Remember that the impact cycle is never done and that all the steps follow up on each other.
In the PowerPoint they use the example of LendingClub and the use of the impact model to determine
if someone is likely to get a loan or not. In other words, what are the characteristics of rejected loans?
1.4 SUMMARY
Value of Data Analytics for businesses and accountants in business, auditing, managerial
accounting, financial accounting, and tax accounting.
IMPACT model introduced as a method to address accounting questions.
Importance of identifying the question emphasized, with the first steps of the IMPACT model
discussed.
Eight data analysis approaches introduced: classification, regression, similarity matching, clustering,
co-occurrence grouping, profiling, link prediction, and data reduction.
Data analytics in auditing: full transaction testing, anomaly detection, and impact on financial and tax
accounting.
Data analytics skills for accountants
- Developed analytics mindset
- Data scrubbing & preparation
- Data quality
- Descriptive data analysis
- Data manipulation
- Statistical data analysis
- Data visualization & reporting
Practical example: IMPACT cycle applied to rejected loans at LendingClub.
