I. Chapter 1: Intro Data Analytics for Accounting .............................................................................2
1. General Introduction ...............................................................................................................2
2. The Impact Model ...................................................................................................................3
II. Chapter 2: Mastering the Data .....................................................................................................8
1. How are data used and stored in the accounting cycle? ..........................................................8
2. Extract, Transform and Load ..................................................................................................9
3. What ethical issues do we encounter in data collection and use? .......................................... 12
III. Performing the Test Plan and Analyzing the Results ............................................................. 13
1. The 4 main types of Data Analytics ....................................................................................... 13
2. Descriptive and Diagnostic Analytics..................................................................................... 15
3. Predictive and Prescriptive Analytics..................................................................................... 19
IV. Communicating Results ........................................................................................................ 22
1. Determine the results ........................................................................................................... 22
2. Choosing the Right Chart ..................................................................................................... 24
V. The Modern Accounting Environment ........................................................................................ 27
1. Modern Data Environment .................................................................................................... 27
2. Enterprise Data .................................................................................................................... 28
3. Automating Data Analytics .................................................................................................... 29
4. Continuous Monitoring Techniques ....................................................................................... 29
VI. Audit Analytics...................................................................................................................... 30
VII. Managerial Analytics ............................................................................................................ 33
1. Application of the IMPACT Model ......................................................................................... 33
2. Identifying Management Accounting Questions ..................................................................... 35
3. Digital Dashboards, KPI’s and Balanced Scorecards ............................................................ 36
VIII. Financial Statement Analytics ............................................................................................... 38
1. Financial Statement Analysis ................................................................................................ 38
2. Ratio Analysis ...................................................................................................................... 38
3. Vertical Analysis ................................................................................................................... 39
4. Horizontal (Trend) Analysis................................................................................................... 39
5. Combination of Techniques and Visualization ....................................................................... 40
6. Text Mining and Sentiment Analysis ..................................................................................... 41
7. XBRL and Data Quality......................................................................................................... 41
IX. Tax Analytics ........................................................................................................................ 42
1. Tax Analytics ........................................................................................................................ 42
2. Mastering the Data through Tax Data Management .............................................................. 43
3. Tax Data Analytics Visualizations ......................................................................................... 44
4. Tax KPI’s.............................................................................................................................. 44
5. Tax Planning: using data to minimize taxes .......................................................................... 45
1
,I. Chapter 1: Intro Data Analytics for Accounting
1. General Introduction
o Data Analytics : the process of transforming and evaluating data with the
purpose of drawing conclusions to address business questions. Turning
data into knowledge to solve specific business problems and ultimately
turning this knowledge into value.
Purpose: 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.
Remember:
- 4 V’s = volume, velocity, variety and veracity
- Big Data: refers to datasets which are too large and complex
to be analyzed traditionally
o How does data analytics affect business?
By numbers: data analytics generates up to 2 trillion USD in value
per year. In 2024, volume of data created will be 149 zettabytes (1
zettabyte = 1 billion terabytes)
Auditing:
- Audit quality: more comprehensive testing and real-time
- exception detection
- Automation: more time for evaluating results instead of
collecting data
- To clients: expanded services, enhanced audits and more
efficient detection of operational inefficiencies
Example: E-commerce company with millions of transactions
using data-analytics company can evaluate every single
transaction and flag suspicious activities. This improves risk
assessment both in substantive and detailed testing.
Management accounting:
2
, - Cost analysis: combines internal and external datasets to
streamline processes
- Decision making: uses real-time dashboards for hiring,
product launches etc.
- Forecasting, budgeting, production and sales: enables
possibility to model different scenarios
Financial reporting:
- Better estimates of collectability, write-downs, etc.
- Understanding the business: managers can better understand
the environment through social media and other external data
sources
- Risk and opportunity identification through analysis of internet
searches
2. The Impact Model
o Developed by Isson and Harriott
Step 1. Identify the Questions
Purpose: understand the business problems that need to be
addressed
Attributes to consider:
- Audience: CFO, internal auditor, financial analyst,…
- Scope: is the question too narrow or too broad?
3
, - Use: how will the results be used?
- Data: what data do we need to answer the question?
Step 2. Master the Data
Purpose: Requires one to know what data are available and
whether those data might be able to help address the business
problem
Consider following 8 elements:
- Know data availability and how they relate to the problem
- Review data availability in internal systems
- Revies data availability in external networks and data
warehouses
- Examine data dictionaries (provides details about the
variables)
- ETL – Extraction, Transformation and Loading (to understand
the time requirements from this step)
- Data validation and completeness
- Data normalization
- Data preparation and scrubbing (can take up to 50-90% of
analyst’s time, very time consuming)
Step 3. Perform the Test Plan
Purpose: Identify a relationship between the response variable
(dependent var.) and those items that affect the response
(independent var.)
Generally: make a simplified representation of reality to address
this purpose
Example: How to predict the performance on the next
accounting exam?
- Dependent variable: score on the exam
- Independent variable: study time, IQ, score on last exam
etc.
4
1. General Introduction ...............................................................................................................2
2. The Impact Model ...................................................................................................................3
II. Chapter 2: Mastering the Data .....................................................................................................8
1. How are data used and stored in the accounting cycle? ..........................................................8
2. Extract, Transform and Load ..................................................................................................9
3. What ethical issues do we encounter in data collection and use? .......................................... 12
III. Performing the Test Plan and Analyzing the Results ............................................................. 13
1. The 4 main types of Data Analytics ....................................................................................... 13
2. Descriptive and Diagnostic Analytics..................................................................................... 15
3. Predictive and Prescriptive Analytics..................................................................................... 19
IV. Communicating Results ........................................................................................................ 22
1. Determine the results ........................................................................................................... 22
2. Choosing the Right Chart ..................................................................................................... 24
V. The Modern Accounting Environment ........................................................................................ 27
1. Modern Data Environment .................................................................................................... 27
2. Enterprise Data .................................................................................................................... 28
3. Automating Data Analytics .................................................................................................... 29
4. Continuous Monitoring Techniques ....................................................................................... 29
VI. Audit Analytics...................................................................................................................... 30
VII. Managerial Analytics ............................................................................................................ 33
1. Application of the IMPACT Model ......................................................................................... 33
2. Identifying Management Accounting Questions ..................................................................... 35
3. Digital Dashboards, KPI’s and Balanced Scorecards ............................................................ 36
VIII. Financial Statement Analytics ............................................................................................... 38
1. Financial Statement Analysis ................................................................................................ 38
2. Ratio Analysis ...................................................................................................................... 38
3. Vertical Analysis ................................................................................................................... 39
4. Horizontal (Trend) Analysis................................................................................................... 39
5. Combination of Techniques and Visualization ....................................................................... 40
6. Text Mining and Sentiment Analysis ..................................................................................... 41
7. XBRL and Data Quality......................................................................................................... 41
IX. Tax Analytics ........................................................................................................................ 42
1. Tax Analytics ........................................................................................................................ 42
2. Mastering the Data through Tax Data Management .............................................................. 43
3. Tax Data Analytics Visualizations ......................................................................................... 44
4. Tax KPI’s.............................................................................................................................. 44
5. Tax Planning: using data to minimize taxes .......................................................................... 45
1
,I. Chapter 1: Intro Data Analytics for Accounting
1. General Introduction
o Data Analytics : the process of transforming and evaluating data with the
purpose of drawing conclusions to address business questions. Turning
data into knowledge to solve specific business problems and ultimately
turning this knowledge into value.
Purpose: 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.
Remember:
- 4 V’s = volume, velocity, variety and veracity
- Big Data: refers to datasets which are too large and complex
to be analyzed traditionally
o How does data analytics affect business?
By numbers: data analytics generates up to 2 trillion USD in value
per year. In 2024, volume of data created will be 149 zettabytes (1
zettabyte = 1 billion terabytes)
Auditing:
- Audit quality: more comprehensive testing and real-time
- exception detection
- Automation: more time for evaluating results instead of
collecting data
- To clients: expanded services, enhanced audits and more
efficient detection of operational inefficiencies
Example: E-commerce company with millions of transactions
using data-analytics company can evaluate every single
transaction and flag suspicious activities. This improves risk
assessment both in substantive and detailed testing.
Management accounting:
2
, - Cost analysis: combines internal and external datasets to
streamline processes
- Decision making: uses real-time dashboards for hiring,
product launches etc.
- Forecasting, budgeting, production and sales: enables
possibility to model different scenarios
Financial reporting:
- Better estimates of collectability, write-downs, etc.
- Understanding the business: managers can better understand
the environment through social media and other external data
sources
- Risk and opportunity identification through analysis of internet
searches
2. The Impact Model
o Developed by Isson and Harriott
Step 1. Identify the Questions
Purpose: understand the business problems that need to be
addressed
Attributes to consider:
- Audience: CFO, internal auditor, financial analyst,…
- Scope: is the question too narrow or too broad?
3
, - Use: how will the results be used?
- Data: what data do we need to answer the question?
Step 2. Master the Data
Purpose: Requires one to know what data are available and
whether those data might be able to help address the business
problem
Consider following 8 elements:
- Know data availability and how they relate to the problem
- Review data availability in internal systems
- Revies data availability in external networks and data
warehouses
- Examine data dictionaries (provides details about the
variables)
- ETL – Extraction, Transformation and Loading (to understand
the time requirements from this step)
- Data validation and completeness
- Data normalization
- Data preparation and scrubbing (can take up to 50-90% of
analyst’s time, very time consuming)
Step 3. Perform the Test Plan
Purpose: Identify a relationship between the response variable
(dependent var.) and those items that affect the response
(independent var.)
Generally: make a simplified representation of reality to address
this purpose
Example: How to predict the performance on the next
accounting exam?
- Dependent variable: score on the exam
- Independent variable: study time, IQ, score on last exam
etc.
4
