Data Analytics
6314M0413Y
Table of Contents
Week 1 ..................................................................................................................................................... 3
Lecture 1: Introduction to Data Analytics ............................................................................................ 3
Introduction and Motivation ........................................................................................................... 3
Managerial Decision Making ........................................................................................................... 3
Decision Support Systems ............................................................................................................... 4
Tutorial A: Decision Support System and Data Analytics Exercises ..................................................... 5
Information from assignments and readings ...................................................................................... 5
Week 2 ..................................................................................................................................................... 5
Lecture 2: Business Intelligence, Data Science, and Database Concepts ............................................ 5
Business Intelligence ....................................................................................................................... 5
Online Analytical Processing............................................................................................................ 6
Business Analytics and Big Data ...................................................................................................... 7
Data Science .................................................................................................................................... 7
Database Systems ............................................................................................................................ 7
Data Warehousing ........................................................................................................................... 8
Getting Access to the Data .............................................................................................................. 9
Database Concepts .......................................................................................................................... 9
Database Components .................................................................................................................... 9
Tutorial B: Business Intelligence and Data Visualization ................................................................... 10
MicroStrategy ................................................................................................................................ 10
Information from assignments and readings .................................................................................... 11
De Mauro et al. (2016) - A formal definition of Big Data based on its essential features ............. 11
EMC Report.................................................................................................................................... 11
Davenport & Patil (2012) - Data scientist: the sexiest job of the 21st century ............................. 11
Interactive videos .......................................................................................................................... 12
Week 3 ................................................................................................................................................... 12
Lecture 3: Data Modelling and Retrieval ........................................................................................... 12
Data Modelling .............................................................................................................................. 12
ERD Transformation ....................................................................................................................... 13
SQL Overview ................................................................................................................................ 14
Basic SQL Commands .................................................................................................................... 15
Page 1 of 38
, Sub-Queries and Set Operators ..................................................................................................... 16
Tutorial C: Data Modelling and Retrieval ........................................................................................... 16
Week 4 ................................................................................................................................................... 17
Lecture 4: Data Mining ...................................................................................................................... 17
Overview of Data Mining ............................................................................................................... 17
Statistics and Data Mining ............................................................................................................. 18
Machine learning ........................................................................................................................... 20
Classification Methods .................................................................................................................. 21
Decision Trees as an Example for Classification Algorithms .......................................................... 21
Tutorial D: Data Model Transformation and Mining.......................................................................... 22
Week 5 ................................................................................................................................................... 22
Lecture 5: Data and Text Mining ........................................................................................................ 22
Cluster Analysis .............................................................................................................................. 22
An Example of a Clustering Algorithm ........................................................................................... 22
Association Rule Mining ................................................................................................................ 22
An Example of an Association Rule Mining Algorithm .................................................................. 23
Software and Concluding Remarks ................................................................................................ 23
Text Mining Basics ......................................................................................................................... 23
Text Mining Core Concepts ............................................................................................................ 23
Natural Language Processing (NLP) ............................................................................................... 24
The Text Mining Process ................................................................................................................ 25
Tutorial E: Data and Text Mining........................................................................................................ 25
Altair AI Studio ............................................................................................................................... 25
Week 6 ................................................................................................................................................... 26
Lecture 6: Process Mining ................................................................................................................. 26
Business Process Modelling........................................................................................................... 26
Process Mining Basics .................................................................................................................... 27
Limitations for Using Process Mining ............................................................................................ 28
Tutorial F: Process Mining ................................................................................................................. 29
Disco .............................................................................................................................................. 29
Information from assignments and readings .................................................................................... 29
Process Mining Manifesto ............................................................................................................. 29
Gehrke and Werner (2013): Process Mining ................................................................................. 30
Multiple-choice questions ..................................................................................................................... 31
Answers ............................................................................................................................................. 35
Page 2 of 38
, Week 1
Lecture 1: Introduction to Data Analytics
Introduction and Motivation
There are a lot of technologies that affect the accounting field, such as blockchain, databases, ERP systems and XBRL.
• XBRL (eXtensible Business Reporting Language) standardizes financial data exchange, like HTML does for
web pages.
• In cloud computing, elasticity refers to the system’s ability to automatically scale resources up or down
based on current demand. This means you can increase capacity during high usage (like tax season for
accounting software) and decrease it when demand is low, ensuring efficient resource use and cost savings.
• Supervised machine learning uses labeled data to train models that predict specific outcomes, such as
classifying emails as spam or not spam. In contrast, unsupervised learning works with unlabeled data to
uncover hidden patterns or groupings, like segmenting customers by behavior. The main distinction lies in
whether the training data includes known outputs.
Recent developments in the profession are Business Intelligence (BI), Big Data Analytics, and artificial intelligence
regarding internal reporting and decision making. Moreover, enterprise resource planning (ERP) systems or accounting
information systems (AIS), business intelligence (BI) software and data warehouses form the foundation of current
corporate reporting. Cloud-based services provide access to digital capabilities to all kind of firms which have
previously only been accessible to large companies. This leads to virtually all data being digital and accessible. This
leads to a changing role of the accountant, as software takes over the task of processing and recording business
transactions as well as traditional bookkeeping activities.
Research shows that data analytics is still used quite rarely in audit firms. Barriers include limited ADA (audit data
analytics) knowledge, lack of regulatory guidance, time-consuming ETL processes (= the extraction of data), high
anomaly rates, and skepticism about improved audit quality. These challenges lead many auditors to avoid using ADA.
Meanwhile, audit firms face client expectations around cost and technology use, prompting some to invest in ADA to
stay competitive. Client preferences, fee expectations, and auditor availability also shape adoption strategies. But
most importantly, auditors don’t use ADA since they lack knowledge and find in difficult to use. Therefore we must
teach it and train it.
Managerial Decision Making
Management is a process by which organizational goals are
achieved by using resources. Decision making is selecting the
best solution from two or more alternatives, but to select the
best solution management requires sufficient information.
Managers usually make decisions by following a four-step process
1. Intelligence: Define the problem (or opportunity).
2. Design: Construct a model that describes the real-world
problem, define evaluation criteria and search for
alternative solutions.
3. Choice: Compare, choose, and recommend a potential
solution to the problem.
4. Implementation: Implement the chosen solution.
Page 3 of 38
, Decision making processes involve the inclusion of at least one (mathematical) model. A model is a simplified
representation or abstraction of reality. There are several benefits to a model:
• Manipulating a model is much easier than manipulating a real system.
• Simulation is easier and does not interfere with the organization’s daily operations.
• Compression of time: years of operations can be simulated in minutes or seconds. The cost is much lower
than experiments conducted on a real system.
• The consequences of making mistakes are less severe. Mathematical models enable the analysis of a very
large number of possible solutions.
• Models enhance and reinforce learning and training.
• Models and solution methods are readily available.
For the decision process we need
reliable information. To get this
information we can use different
technologies (i.e. decision support
systems) to support our decision
making process. The Decision
Support Framework by Gory and
Scott-Morton (1971) categorizes
decisions based on type of control
and type of decision. It helps clarify
where different decisions fall within
an organization and which systems
(MIS or DSS) best support them.
Management Information Systems
(MIS) primarily support structured
and semi-structured decisions,
while Decision Support Systems (DSS) focus on semi-structured and unstructured decisions. Lower and middle
managers rely more on MIS for operational and structured tasks, while top management uses DSS tools to support
complex, strategic, and often unstructured decisions. The framework underscores that as decisions become less
structured and more strategic, the need for flexible and analytical systems increases. The matrix is based upon
degrees of structuredness (Simon, 1977) and types of control (Anthony, 1965). The degree of structuredness is crucial
for understanding what kind of technological or human support is needed for different decisions, as highly structured
decisions are routine, rule-based, and programmable (e.g., payroll processing), while highly unstructured decisions are
novel, complex, and non-programmable (e.g., deciding to enter a new market). The types of control reflect who in the
organization is making the decision and for what purpose. Strategic Planning reflects long-term, high-level decisions
made by top management (e.g., mergers & acquisitions). Management Control, on the other hand, are mid-level
decisions that guide operations and resources (e.g., budget allocation). Lastly, operational Control concerns day-to-day
decisions, usually routine and handled by lower-level staff (e.g., order processing).
Decision Support Systems
A system is a set of two or more interrelated components interacting to achieve a goal. It consists of a boundary,
inputs and outputs, interactions with its environment, and it is governed by processes, rules and procedures.
Data are facts that are collected, recorded, stored and processed. Data is insufficient for decision making.
Information is processed data used in decision making. Too much information however, will make it more, not less,
difficult to make decisions. This is known as ‘data overload’ or ‘information overload’.
Decision Support Systems are interactive computer-based systems, which help decision makers utilize data and
models to solve unstructured problems. It couples the intellectual resources of individuals with the computational
capabilities of the computer to improve the quality of decisions.
Page 4 of 38
6314M0413Y
Table of Contents
Week 1 ..................................................................................................................................................... 3
Lecture 1: Introduction to Data Analytics ............................................................................................ 3
Introduction and Motivation ........................................................................................................... 3
Managerial Decision Making ........................................................................................................... 3
Decision Support Systems ............................................................................................................... 4
Tutorial A: Decision Support System and Data Analytics Exercises ..................................................... 5
Information from assignments and readings ...................................................................................... 5
Week 2 ..................................................................................................................................................... 5
Lecture 2: Business Intelligence, Data Science, and Database Concepts ............................................ 5
Business Intelligence ....................................................................................................................... 5
Online Analytical Processing............................................................................................................ 6
Business Analytics and Big Data ...................................................................................................... 7
Data Science .................................................................................................................................... 7
Database Systems ............................................................................................................................ 7
Data Warehousing ........................................................................................................................... 8
Getting Access to the Data .............................................................................................................. 9
Database Concepts .......................................................................................................................... 9
Database Components .................................................................................................................... 9
Tutorial B: Business Intelligence and Data Visualization ................................................................... 10
MicroStrategy ................................................................................................................................ 10
Information from assignments and readings .................................................................................... 11
De Mauro et al. (2016) - A formal definition of Big Data based on its essential features ............. 11
EMC Report.................................................................................................................................... 11
Davenport & Patil (2012) - Data scientist: the sexiest job of the 21st century ............................. 11
Interactive videos .......................................................................................................................... 12
Week 3 ................................................................................................................................................... 12
Lecture 3: Data Modelling and Retrieval ........................................................................................... 12
Data Modelling .............................................................................................................................. 12
ERD Transformation ....................................................................................................................... 13
SQL Overview ................................................................................................................................ 14
Basic SQL Commands .................................................................................................................... 15
Page 1 of 38
, Sub-Queries and Set Operators ..................................................................................................... 16
Tutorial C: Data Modelling and Retrieval ........................................................................................... 16
Week 4 ................................................................................................................................................... 17
Lecture 4: Data Mining ...................................................................................................................... 17
Overview of Data Mining ............................................................................................................... 17
Statistics and Data Mining ............................................................................................................. 18
Machine learning ........................................................................................................................... 20
Classification Methods .................................................................................................................. 21
Decision Trees as an Example for Classification Algorithms .......................................................... 21
Tutorial D: Data Model Transformation and Mining.......................................................................... 22
Week 5 ................................................................................................................................................... 22
Lecture 5: Data and Text Mining ........................................................................................................ 22
Cluster Analysis .............................................................................................................................. 22
An Example of a Clustering Algorithm ........................................................................................... 22
Association Rule Mining ................................................................................................................ 22
An Example of an Association Rule Mining Algorithm .................................................................. 23
Software and Concluding Remarks ................................................................................................ 23
Text Mining Basics ......................................................................................................................... 23
Text Mining Core Concepts ............................................................................................................ 23
Natural Language Processing (NLP) ............................................................................................... 24
The Text Mining Process ................................................................................................................ 25
Tutorial E: Data and Text Mining........................................................................................................ 25
Altair AI Studio ............................................................................................................................... 25
Week 6 ................................................................................................................................................... 26
Lecture 6: Process Mining ................................................................................................................. 26
Business Process Modelling........................................................................................................... 26
Process Mining Basics .................................................................................................................... 27
Limitations for Using Process Mining ............................................................................................ 28
Tutorial F: Process Mining ................................................................................................................. 29
Disco .............................................................................................................................................. 29
Information from assignments and readings .................................................................................... 29
Process Mining Manifesto ............................................................................................................. 29
Gehrke and Werner (2013): Process Mining ................................................................................. 30
Multiple-choice questions ..................................................................................................................... 31
Answers ............................................................................................................................................. 35
Page 2 of 38
, Week 1
Lecture 1: Introduction to Data Analytics
Introduction and Motivation
There are a lot of technologies that affect the accounting field, such as blockchain, databases, ERP systems and XBRL.
• XBRL (eXtensible Business Reporting Language) standardizes financial data exchange, like HTML does for
web pages.
• In cloud computing, elasticity refers to the system’s ability to automatically scale resources up or down
based on current demand. This means you can increase capacity during high usage (like tax season for
accounting software) and decrease it when demand is low, ensuring efficient resource use and cost savings.
• Supervised machine learning uses labeled data to train models that predict specific outcomes, such as
classifying emails as spam or not spam. In contrast, unsupervised learning works with unlabeled data to
uncover hidden patterns or groupings, like segmenting customers by behavior. The main distinction lies in
whether the training data includes known outputs.
Recent developments in the profession are Business Intelligence (BI), Big Data Analytics, and artificial intelligence
regarding internal reporting and decision making. Moreover, enterprise resource planning (ERP) systems or accounting
information systems (AIS), business intelligence (BI) software and data warehouses form the foundation of current
corporate reporting. Cloud-based services provide access to digital capabilities to all kind of firms which have
previously only been accessible to large companies. This leads to virtually all data being digital and accessible. This
leads to a changing role of the accountant, as software takes over the task of processing and recording business
transactions as well as traditional bookkeeping activities.
Research shows that data analytics is still used quite rarely in audit firms. Barriers include limited ADA (audit data
analytics) knowledge, lack of regulatory guidance, time-consuming ETL processes (= the extraction of data), high
anomaly rates, and skepticism about improved audit quality. These challenges lead many auditors to avoid using ADA.
Meanwhile, audit firms face client expectations around cost and technology use, prompting some to invest in ADA to
stay competitive. Client preferences, fee expectations, and auditor availability also shape adoption strategies. But
most importantly, auditors don’t use ADA since they lack knowledge and find in difficult to use. Therefore we must
teach it and train it.
Managerial Decision Making
Management is a process by which organizational goals are
achieved by using resources. Decision making is selecting the
best solution from two or more alternatives, but to select the
best solution management requires sufficient information.
Managers usually make decisions by following a four-step process
1. Intelligence: Define the problem (or opportunity).
2. Design: Construct a model that describes the real-world
problem, define evaluation criteria and search for
alternative solutions.
3. Choice: Compare, choose, and recommend a potential
solution to the problem.
4. Implementation: Implement the chosen solution.
Page 3 of 38
, Decision making processes involve the inclusion of at least one (mathematical) model. A model is a simplified
representation or abstraction of reality. There are several benefits to a model:
• Manipulating a model is much easier than manipulating a real system.
• Simulation is easier and does not interfere with the organization’s daily operations.
• Compression of time: years of operations can be simulated in minutes or seconds. The cost is much lower
than experiments conducted on a real system.
• The consequences of making mistakes are less severe. Mathematical models enable the analysis of a very
large number of possible solutions.
• Models enhance and reinforce learning and training.
• Models and solution methods are readily available.
For the decision process we need
reliable information. To get this
information we can use different
technologies (i.e. decision support
systems) to support our decision
making process. The Decision
Support Framework by Gory and
Scott-Morton (1971) categorizes
decisions based on type of control
and type of decision. It helps clarify
where different decisions fall within
an organization and which systems
(MIS or DSS) best support them.
Management Information Systems
(MIS) primarily support structured
and semi-structured decisions,
while Decision Support Systems (DSS) focus on semi-structured and unstructured decisions. Lower and middle
managers rely more on MIS for operational and structured tasks, while top management uses DSS tools to support
complex, strategic, and often unstructured decisions. The framework underscores that as decisions become less
structured and more strategic, the need for flexible and analytical systems increases. The matrix is based upon
degrees of structuredness (Simon, 1977) and types of control (Anthony, 1965). The degree of structuredness is crucial
for understanding what kind of technological or human support is needed for different decisions, as highly structured
decisions are routine, rule-based, and programmable (e.g., payroll processing), while highly unstructured decisions are
novel, complex, and non-programmable (e.g., deciding to enter a new market). The types of control reflect who in the
organization is making the decision and for what purpose. Strategic Planning reflects long-term, high-level decisions
made by top management (e.g., mergers & acquisitions). Management Control, on the other hand, are mid-level
decisions that guide operations and resources (e.g., budget allocation). Lastly, operational Control concerns day-to-day
decisions, usually routine and handled by lower-level staff (e.g., order processing).
Decision Support Systems
A system is a set of two or more interrelated components interacting to achieve a goal. It consists of a boundary,
inputs and outputs, interactions with its environment, and it is governed by processes, rules and procedures.
Data are facts that are collected, recorded, stored and processed. Data is insufficient for decision making.
Information is processed data used in decision making. Too much information however, will make it more, not less,
difficult to make decisions. This is known as ‘data overload’ or ‘information overload’.
Decision Support Systems are interactive computer-based systems, which help decision makers utilize data and
models to solve unstructured problems. It couples the intellectual resources of individuals with the computational
capabilities of the computer to improve the quality of decisions.
Page 4 of 38
