Summary Data analytics
Chapter 1: General introduction......................................................................................................................................3
1. General introduction...............................................................................................................................................3
2. The impact model....................................................................................................................................................4
3. Hands-on example of the impact model..................................................................................................................7
Chapter 2: Mastering the Data........................................................................................................................................9
1. How is data organized and stored in the accounting cycle?...................................................................................10
2. How are data stored in relational databases?........................................................................................................10
3. Extract, transform and load...................................................................................................................................11
4. What ethical issues do we encounter in data collection and use?.........................................................................13
Chapter 3 – performing the test plan............................................................................................................................14
1. Introduction...........................................................................................................................................................14
2. Performing the test plan and analyzing the results................................................................................................14
3. Analyzing Results: Descriptive Analytics................................................................................................................14
4. Analysing results: diagnostic analytics...................................................................................................................16
5. Analyzing Results: Predictive Analytics..................................................................................................................18
6. Analyzing Results: Prescriptive Analytics...............................................................................................................20
Summary................................................................................................................................................................... 21
Chapter 4: communicating results and visualizations....................................................................................................22
0. introduction...........................................................................................................................................................22
1. Communicating Results: Determine the Purpose..................................................................................................23
3. Avoid misleading visualization...............................................................................................................................26
4. Refine your chart to communicate efficiently and effectively................................................................................27
5. Communicate your results in a written report.......................................................................................................28
Chapter 5: the Modern accounting environment..........................................................................................................29
1. Introduction...........................................................................................................................................................29
2. Automation as a cause to a data-rich environment...............................................................................................29
3. Different organizing methods for data...................................................................................................................30
4. continuous monitoring strategies and alert systems.............................................................................................31
Chapter 6 – audit analytics............................................................................................................................................32
0. Introduction...........................................................................................................................................................32
1. Audit analytics.......................................................................................................................................................32
2. The impact model..................................................................................................................................................32
2.1. Identify the problem...........................................................................................................................................32
.................................................................................................................................................................................. 32
2.2. master the data..................................................................................................................................................33
2.3. Perform the test plan..........................................................................................................................................33
2.4. Additional analyses.............................................................................................................................................37
Chapter 7 – managerial analytics...................................................................................................................................38
1. Introduction...........................................................................................................................................................38
, 2. IMPACT model in management accounting...........................................................................................................38
3. Identifying management accounting questions.....................................................................................................40
4. Balanced scorecard and key performance indicators.............................................................................................42
Chapter 8 – financial statement analytics......................................................................................................................44
0. Introduction...........................................................................................................................................................44
1. Descriptive analytics..............................................................................................................................................44
2. Diagnostic analytics...............................................................................................................................................47
3. Predictive analytics................................................................................................................................................47
4. Prescriptive analytics.............................................................................................................................................47
5. How can we visualize financial data?.....................................................................................................................48
6. how does text mining and sentiment analysis work?............................................................................................48
7. XBRL – digitalizing financial reports for analysis....................................................................................................49
Chapter 9 – Tax analytics...............................................................................................................................................51
0. Introduction...........................................................................................................................................................51
1. STEP 1: identify the questions...............................................................................................................................51
3. STEP 3: perform the test plan................................................................................................................................52
4. STEP 4: Visualizing tax data....................................................................................................................................53
5. Tax data analytics for tax planning.........................................................................................................................54
,Chapter 1: General introduction
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.
• Describe the skills needed by accountants.
• 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.
is often described using the 4V’s:
- 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
HOW DOES DATA ANALYTICS AFFECT BUSINESS?
By numbers:
- 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.
Data Analytics is expected to have dramatic effects on auditing and financial reporting as well as tax and
managerial accounting
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 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 business environment through social media and other external data sources.
- Analysts identify risks and opportunities through analysis of Internet searches.
, 2. The impact model
The impact of data analytics can be tracked by the IMPACT model
WHAT IS THE IMPACT MODEL?
Impact model is a means of performing Data Analytics that goes all the way from identifying the question, to
mastering the data, to performing data analyses and communicating and tracking results. It is recursive in nature,
suggesting that as questions are addressed, new, more refined questions may emerge that can be addressed in a
similar way.
1. Identify the questions.
2. Master the data.
3. Perform the test plan.
4. Address and refine results.
5. Communicate insights.
6. Track outcomes.
This loop will often be repeated as one question creates quick follow up questions
(1)STEP 1: IDENTITFY THE QUESTIONS
What?
Understand the business problems that need to be addressed start with a clear and concrete question that data
can potentially answer
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?
Example of questions:
- Are employees circumventing internal controls over payments?
- Are there any suspicious travel and entertainment expenses?
- Are our customers paying us in a 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?
(2)STEP 2: MASTER THE DATA
what? We need a comprehensive understanding of the data
- Data needs to be aligned with the business problem
- determine what data is available, how to access it and whether its reliable and available
important? This step is very crucial but time-consuming (can take around 80% of your time)
consider the 8 following 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 give information about the variables and gives more information on data structure
- ETL - Extraction, transformation, and loading.
- Data validation and completeness.
- Data normalization.
- Data preparation and scrubbing leave out redundancies and inconsistencies and irrelevant information
Note: By the year 2024, the volume of data created, captured, copied, and consumed worldwide will be 149
zettabytes
Chapter 1: General introduction......................................................................................................................................3
1. General introduction...............................................................................................................................................3
2. The impact model....................................................................................................................................................4
3. Hands-on example of the impact model..................................................................................................................7
Chapter 2: Mastering the Data........................................................................................................................................9
1. How is data organized and stored in the accounting cycle?...................................................................................10
2. How are data stored in relational databases?........................................................................................................10
3. Extract, transform and load...................................................................................................................................11
4. What ethical issues do we encounter in data collection and use?.........................................................................13
Chapter 3 – performing the test plan............................................................................................................................14
1. Introduction...........................................................................................................................................................14
2. Performing the test plan and analyzing the results................................................................................................14
3. Analyzing Results: Descriptive Analytics................................................................................................................14
4. Analysing results: diagnostic analytics...................................................................................................................16
5. Analyzing Results: Predictive Analytics..................................................................................................................18
6. Analyzing Results: Prescriptive Analytics...............................................................................................................20
Summary................................................................................................................................................................... 21
Chapter 4: communicating results and visualizations....................................................................................................22
0. introduction...........................................................................................................................................................22
1. Communicating Results: Determine the Purpose..................................................................................................23
3. Avoid misleading visualization...............................................................................................................................26
4. Refine your chart to communicate efficiently and effectively................................................................................27
5. Communicate your results in a written report.......................................................................................................28
Chapter 5: the Modern accounting environment..........................................................................................................29
1. Introduction...........................................................................................................................................................29
2. Automation as a cause to a data-rich environment...............................................................................................29
3. Different organizing methods for data...................................................................................................................30
4. continuous monitoring strategies and alert systems.............................................................................................31
Chapter 6 – audit analytics............................................................................................................................................32
0. Introduction...........................................................................................................................................................32
1. Audit analytics.......................................................................................................................................................32
2. The impact model..................................................................................................................................................32
2.1. Identify the problem...........................................................................................................................................32
.................................................................................................................................................................................. 32
2.2. master the data..................................................................................................................................................33
2.3. Perform the test plan..........................................................................................................................................33
2.4. Additional analyses.............................................................................................................................................37
Chapter 7 – managerial analytics...................................................................................................................................38
1. Introduction...........................................................................................................................................................38
, 2. IMPACT model in management accounting...........................................................................................................38
3. Identifying management accounting questions.....................................................................................................40
4. Balanced scorecard and key performance indicators.............................................................................................42
Chapter 8 – financial statement analytics......................................................................................................................44
0. Introduction...........................................................................................................................................................44
1. Descriptive analytics..............................................................................................................................................44
2. Diagnostic analytics...............................................................................................................................................47
3. Predictive analytics................................................................................................................................................47
4. Prescriptive analytics.............................................................................................................................................47
5. How can we visualize financial data?.....................................................................................................................48
6. how does text mining and sentiment analysis work?............................................................................................48
7. XBRL – digitalizing financial reports for analysis....................................................................................................49
Chapter 9 – Tax analytics...............................................................................................................................................51
0. Introduction...........................................................................................................................................................51
1. STEP 1: identify the questions...............................................................................................................................51
3. STEP 3: perform the test plan................................................................................................................................52
4. STEP 4: Visualizing tax data....................................................................................................................................53
5. Tax data analytics for tax planning.........................................................................................................................54
,Chapter 1: General introduction
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.
• Describe the skills needed by accountants.
• 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.
is often described using the 4V’s:
- 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
HOW DOES DATA ANALYTICS AFFECT BUSINESS?
By numbers:
- 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.
Data Analytics is expected to have dramatic effects on auditing and financial reporting as well as tax and
managerial accounting
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 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 business environment through social media and other external data sources.
- Analysts identify risks and opportunities through analysis of Internet searches.
, 2. The impact model
The impact of data analytics can be tracked by the IMPACT model
WHAT IS THE IMPACT MODEL?
Impact model is a means of performing Data Analytics that goes all the way from identifying the question, to
mastering the data, to performing data analyses and communicating and tracking results. It is recursive in nature,
suggesting that as questions are addressed, new, more refined questions may emerge that can be addressed in a
similar way.
1. Identify the questions.
2. Master the data.
3. Perform the test plan.
4. Address and refine results.
5. Communicate insights.
6. Track outcomes.
This loop will often be repeated as one question creates quick follow up questions
(1)STEP 1: IDENTITFY THE QUESTIONS
What?
Understand the business problems that need to be addressed start with a clear and concrete question that data
can potentially answer
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?
Example of questions:
- Are employees circumventing internal controls over payments?
- Are there any suspicious travel and entertainment expenses?
- Are our customers paying us in a 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?
(2)STEP 2: MASTER THE DATA
what? We need a comprehensive understanding of the data
- Data needs to be aligned with the business problem
- determine what data is available, how to access it and whether its reliable and available
important? This step is very crucial but time-consuming (can take around 80% of your time)
consider the 8 following 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 give information about the variables and gives more information on data structure
- ETL - Extraction, transformation, and loading.
- Data validation and completeness.
- Data normalization.
- Data preparation and scrubbing leave out redundancies and inconsistencies and irrelevant information
Note: By the year 2024, the volume of data created, captured, copied, and consumed worldwide will be 149
zettabytes
