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Samenvatting Data Analytics in Accounting 3e Bach HIR/TEW KUL

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2024/2025 Written for academic year
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Katholieke Universiteit Leuven (KU Leuven)
Course
Data Analytics in Accounting (D0B04A)
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full summary of the Data Analytics in Accounting course (D0b04a) given by Vanhaverbeke Steven and Wets Stephanie. This compulsory course is part of the Accounting and Finance Major from the 3rd bachelor HIR/TEW. The summary includes everything from the slides, the knowledge clips and the handbook.

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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

Table of contents

  1. 01 I. Chapter 1: Intro Data Analytics for Accounting 2
  2. 02 1. General Introduction 2
  3. 03 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. 2
  4. 04 o How does data analytics affect business? 2
  5. 05 2. The Impact Model 3
  6. 06 o Developed by Isson and Harriott 3
  7. 07 Step 1. Identify the Questions 3
  8. 08 Step 2. Master the Data 4
  9. 09 Step 3. Perform the Test Plan 4
  10. 10 Step 4. Address and Refine Results 6
  11. 11 Step 5. Communicate Insights 6
  12. 12 Step 6. Track Outcomes 6
  13. 13 o Accountants need to be able to: 7
  14. 14 o Develop Analytical Mindset’: Brings together all previous points! 7
  15. 15 o Microsoft Data Analytics Tools 7
  16. 16 II. Chapter 2: Mastering the Data 8
  17. 17 1. How are data used and stored in the accounting cycle? 8
  18. 18 o Understand the data by looking at how it is organized. 8
  19. 19 o Why relational DB’s instead of flat files? 8
  20. 20 o Four types of attributes. 9
  21. 21 1) Primary key: used to ensure that each row in the table is unique, so it is often referred as a “unique identifier”. Primary keys are typically made up of one column. 9
  22. 22 2) Foreign key: used to create the relationship between two tables. Whenever two tables are related, one of those two must contain a foreign key to point to a primary key in the other table. 9
  23. 23 3) Composite key: a combination of two foreign keys used for line items with much detail in it. 9
  24. 24 4) Descriptive attributes: includes everything else but is not necessary to build the data model, it provides the actual business information. 9
  25. 25 o Data Dictionaries: spells out exactly what each attribute is and why it exists. It specifies whether it is required, what kind of data type it has and how many characteristics it can stall. 9
  26. 26 2. Extract, Transform and Load 9
  27. 27 o The Requesting data is an iterative practice involving 5 steps. 9
  28. 28 o EXTRACT 9
  29. 29 Step 1. Determine the purpose and scope of the data request 9
  30. 30 Step 2. Obtain the data 10
  31. 31 o TRANSFORM 11
  32. 32 Step 3. Validate the data for completeness and integrity 11
  33. 33 Step 4. Clean the data 11
  34. 34 o LOAD 12
  35. 35 Step 5. Load the data for data analysis 12
  36. 36 3. What ethical issues do we encounter in data collection and use? 12
  37. 37 o Ensure that data is used responsibly and securely. 12
  38. 38 o Assess whether individuals have the right to restrict access to their personal information and to control the usage of it 12
  39. 39 o Questions suggested by the Institute of Business Ethics to allow a business to protect the privacy of it’s stakeholders: 12
  40. 40 III. Performing the Test Plan and Analyzing the Results 13
  41. 41 1. The 4 main types of Data Analytics 13
  42. 42 o 13
  43. 43 o Descriptive Analytics: are procedures that summarize existing data to determine what has happened in the past. 13
  44. 44 o Diagnostic Analytics: are procedures that explore the current data to determine why something has happened the way it has, typically comparing the data to a benchmark. It allows users to drill down in the data. 13
  45. 45 o Predictive Analytics: are procedures used to generate a model that can be used to determine what is likely to happen in the future, it moves us from hindsight to foresight. 14
  46. 46 o Prescriptive Analytics: are procedures that work to identify the best possible options for what should be done in the future. 15
  47. 47 2. Descriptive and Diagnostic Analytics 15
  48. 48 o Summary Statistics 15
  49. 49 o Data reduction 16
  50. 50 o Standardizing Data (Z-scores) 16
  51. 51 o Profiling 17
  52. 52 o Clustering 18
  53. 53 o Hypothesis Testing 18
  54. 54 3. Predictive and Prescriptive Analytics 19
  55. 55 o Data-specific terminology 19
  56. 56 o Regression 19
  57. 57 o Classification 20
  58. 58 o Decision Support Systems (DSS): 21
  59. 59 o Machine Learning and AI 21
  60. 60 IV. Communicating Results 22
  61. 61 1. Determine the results 22
  62. 62 o “Data Analytics are effective, but they are only as important and effective as we can communicate and make the data understandable.” 22
  63. 63 o Chart Types 22
  64. 64 2. Choosing the Right Chart 24
  65. 65 o Which charts are appropriate for different data? 24
  66. 66 Example: 24
  67. 67 o How to refine your charts? 25
  68. 68 o How can the use of words provide insight? 25
  69. 69 o Remember to use plain language throughout the IMPACT model: 26
  70. 70 o Consider your audience and tone: 26
  71. 71 o Writing and Revising: 26
  72. 72 V. The Modern Accounting Environment 27
  73. 73 1. Modern Data Environment 27
  74. 74 o Automation is transforming accounting & auditing 27
  75. 75 o Drivers of automation and data analytics 27
  76. 76 o Increasing importance of the Internal Audit 27
  77. 77 2. Enterprise Data 28
  78. 78 o Types of enterprise systems (ES): 28
  79. 79 o Common Data Model: tool used to map existing DB tables and fields from various systems to a standardized set of tables and fields for use with analytics. Example: ADS (Audit Data Standards) 29
  80. 80 3. Automating Data Analytics 29
  81. 81 o Following a standardized audit plan: 29
  82. 82 4. Continuous Monitoring Techniques 29
  83. 83 o Continuous monitoring: process used by internal auditors that provides real-time assurance over business processes and systems. 29
  84. 84 o Alarms and exceptions: 30
  85. 85 VI. Audit Analytics 30
  86. 86 See Chapter 3 for definitions etc. , 6th chapter just adds some things 30
  87. 87 o Why Audit Data Analytics. 30
  88. 88 o Identify the problem. 30
  89. 89 o Master the data. 31
  90. 90 o Perform the test plan. 31
  91. 91 o Address and Refine Results. 33
  92. 92 o Communicate Insights. 33
  93. 93 o Track Outcomes. 33
  94. 94 VII. Managerial Analytics 33
  95. 95 1. Application of the IMPACT Model 33
  96. 96 o Identify the Questions. 33
  97. 97 o Master the Data. 33
  98. 98 o Perform the Test Plan, 4 types of analytics. 33
  99. 99 o Address and Refine Results. (1) 34
  100. 100 o Communicate Insights. (1) 34
  101. 101 o Track Outcomes. (1) 34
  102. 102 2. Identifying Management Accounting Questions 35
  103. 103 o Relevant Costs. 35
  104. 104 o Performance Metrics. 35
  105. 105 3. Digital Dashboards, KPI’s and Balanced Scorecards 36
  106. 106 o Digital Dashboard: an interactive, visual report that displays an organization’s most important metrics in one place. Dashboards provide real-time insights into performance, helping users quickly understand how the company is doing across key areas. 36
  107. 107 o KPI Guidelines: 36
  108. 108 o Balanced Scorecard: a strategic performance management framework that ensures that we look at the business from multiple different angles. 37
  109. 109 VIII. Financial Statement Analytics 38
  110. 110 1. Financial Statement Analysis 38
  111. 111 o Financial Statement Analysis: translates raw financial numbers into meaningful insights for decision-making. Used by investors, analysts and auditors to evaluate performance and health of a company. 38
  112. 112 2. Ratio Analysis 38
  113. 113 o Descriptive tool used to show proportional relationships between financial statement numbers. 38
  114. 114 3. Vertical Analysis 39
  115. 115 o Descriptive tool where we create common-size financial statements where each item is expressed as a percentage of a base amount, this allows easy comparisons between companies or periods. 39
  116. 116 4. Horizontal (Trend) Analysis 39
  117. 117 o Descriptive tool which forms a bridge between descriptive and predictive analytics, it examens financial changes over time. 39
  118. 118 o Horizontal Analysis using an Index: 39
  119. 119 5. Combination of Techniques and Visualization 40
  120. 120 o Combination of Techniques: by combining ratio’s, trend analysis, year-over-year and peer comparison a much richer picture. It helps us see whether a change in company-specific or part of a bigger trend. 40
  121. 121 o How can we visualize financial data? 40
  122. 122 6. Text Mining and Sentiment Analysis 41
  123. 123 o Text mining: analyzing large amounts of text for pattern or counts 41
  124. 124 o Sentiment Analysis: an application of Text Mining, using a sentiment dictionary to classify words as positive, negative etc. to gauge the tone of a document. This results in a certain sentiment score. 41
  125. 125 7. XBRL and Data Quality 41
  126. 126 o XBRL: (Extensible Business Reporting Language) an XML-based standard for tagging financial data. It turns financial statements into machine-readable data by tagging each financial element with standardized labels else known as “Barcodes”. 41
  127. 127 o Data Quality Challenges: 42
  128. 128 o Standardized Metrics and Future XBRL: 42
  129. 129 IX. Tax Analytics 42
  130. 130 1. Tax Analytics 42
  131. 131 o What tax questions can Data Analytics help with? Examples 42
  132. 132 2. Mastering the Data through Tax Data Management 43
  133. 133 o Tax Data at Companies: 43
  134. 134 o Tax Data at Accounting Firms: 43
  135. 135 o Tax Data at Tax Authorities: 43
  136. 136 3. Tax Data Analytics Visualizations 44
  137. 137 o Dashboards: 44
  138. 138 o Geographical maps: 44
  139. 139 o Graphs: 44
  140. 140 4. Tax KPI’s 44
  141. 141 o Cost & Risk: 44
  142. 142 5. Tax Planning: using data to minimize taxes 45
  143. 143 o Tax planning: the process of analyzing possible future transactions or scenarios to minimize tax liability while complying with the law. It involves: 45
  144. 144 o What-if Scenarios: a technique to test the effect of changing certain inputs on an outcome. In tax planning, we build a model and then try different values for key inputs. 46
Connected book
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Vernon Richardson, Katie L. Terrell, Ryan A. Teeter ISE Data Analytics for Accounting
Publisher: 2022 ISBN: 9781265094454 Edition: Unknown

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