1
Data-driven Control
Summary
1
, 2
Inhoudsopgave
Week 1 ...................................................................................................................................................................... 3
Chapter 5. Introduction to Data Analytics in Accounting ..................................................................................... 3
Vasarhelyi et al. (2015) Big Data in Accounting. An Overview ............................................................................. 8
Geerts G.L. (2011) A design science research methodology and its application to accounting information
systems research ................................................................................................................................................ 12
Week 2 .................................................................................................................................................................... 16
Dai & Vasarhelyi (2017) Toward Blockchain-Based Accounting and Assurance ................................................ 16
Chapter 6.Transforming Data ............................................................................................................................. 22
Chapter 7. Data Analysis and Presentation ........................................................................................................ 26
Week 3 .................................................................................................................................................................... 32
Kuhn and Sutton (2010) Continuous Auditing in ERP System Environments: The Current State and Future
Directions ............................................................................................................................................................ 32
Singh, Best & Bojilov (2014) Continuous Auditing and Continuous Monitoring in ERP Environments: Case
Studies of Application Implementations ............................................................................................................ 36
Week 4 .................................................................................................................................................................... 42
Appelbaum et al. (2017) Impact of business analytics and enterprise systems on managerial accounting ..... 42
Chapter 1. Conceptual Foundations of Accounting Information Systems ......................................................... 49
Chapter 2. Overview of Transaction Processing and Enterprise Resource Planning Systems ........................... 55
Week 5 .................................................................................................................................................................... 61
Baader & Kremar (2018) Reducing false positives in fraud detection. Combining the red flag approach with
process mining .................................................................................................................................................... 61
Gray & Debreceny (2014) A taxonomy to guide research on the application of data mining to fraud detection
in financial statement audits .............................................................................................................................. 64
Chapter 4. Relational Databases ........................................................................................................................ 72
Week 6 .................................................................................................................................................................... 79
Chapter 18. General Ledger and Reporting System ........................................................................................... 79
Vasarhelyi, Chan & Krahel (2012) Consequences of XBRL Standardization on Financial Statement Data ........ 85
Week 7 .................................................................................................................................................................... 89
Van der Aalst, Bichler & Heinzl (2018) Robotic Process Automation................................................................. 89
Huang and Vasarhelyi (2019) Applying robotic process automation (RPA) in auditing: A framework ............. 90
2
, 3
Week 1
Chapter 5. Introduction to Data Analytics in Accounting
Introduction
This chapter explores data analytics and accompanying toolsets needed to turn this mountain of data into useful
information.
Using data appropriately has become especially important for accountants. Accountants in all different practice
areas are using data in exciting ways. For example:
• Auditors can test full populations of transactions rather than a small sample by using data analytics and
automation.
• Corporate accountants use data to make better decisions. Increased data allows them to make more
accurate assessments of risks and to identify opportunities to preserve and enhance value.
To understand the scope of the data revolution, it is important to consider the four V’s of Big Data:
1. Volume
2. Velocity
3. Variety
4. Veracity
Big data is the term companies use to describe the massive amounts of data they now capture, store and analyse.
Data volume refers to the amount of data created and stored by an organization. Data velocity refers to the
pace at which data is created and stored. Data variety refers to the different forms data can take. Data veracity
refers to the quality or trustworthiness of data.
Big data alone does not lead to improvements; rather, business professionals must analyse the big data to reveal
insights that lead to these improvements. To be successful in the future with big data, it is important to
understand more than tools and techniques; it is critical to develop an appropriate mindset that allows you to
think about data holistically.
A critical mindset for future accountants to develop is the analytics mindset. The Center for Audit Quality defines
an analytics mindset as the “ability to visualize, articulate, conceptualize, or solve both complex and simple
problems by making decisions that are sensible given the available information and ability to identify trends
through analysis of data/information.
According EY, an analytics mindset is the ability to:
• Ask the right questions
• Extract, Transform, and Load relevant data
• Apply appropriate data analytic techniques
• Interpret and share the results with stakeholders.
Ask the Right Questions
Data is defined as facts that are collected, recorded, stored and processed by a system. As such, data by
themselves offer little value. Only when data is transformed into information does it provide value.
To start the process of transforming data into information, one must have a question or desired outcome. Asking
the right question is the first step of the analytics mindset.
To define “right” or “good” questions in the context of data analytics, start by establishing objectives that are
SMART. A good data analytic question is:
• Specific – needs to be direct and focused to produce a meaningful answer
• Measurable – must be amenable to data analysis and thus the inputs to answering the question must
be measurable with data
3
, 4
• Achievable – should be able to be answered and the answer should cause a decision maker to take an
action
• Relevant – should relate to the objectives of the organization or the situation under consideration
• Timely – must have a defined time horizon for answering.
Extract, Transform, and Load relevant Data
ETL Process – a set of procedures for blending data. The acronym stands for extract, transform and load data.
Extracting data
Extracting data is the first step in the ETL process. The extraction process has three steps:
• Understand data needs and the data available
• Perform the data extraction
• Verify the data extraction quality and document what you have done
Understand data needs and the data available
Before extracting data, data needs should be carefully defined. Defining the question well makes it easier to
define what data is needed to address the question. Without defining the data well early in the process, it is
more likely that the wrong data or incomplete data will be extracted.
After defining the needed data, the next step is to understand the data itself, which entails understanding things
like location, accessibility and structure of the data.
A data warehouse generally describes the storage of structured data from many different sources in an
organization.
Structured data refers to data that is highly organized and fits into fixed fields. For example, accounting data in
the general ledger, data in a relational database and most types of spreadsheet data.
Unstructured data is data that has no uniform structure. Examples include images, audio files, documents,
emails.
Semi-structured data is organized in some ways but is not fully organized to be inserted into a relational
database. Examples include data stored in csv, xml and various streamed data (such as logs or machine-generated
operation data).
Data warehouses typically store only structured data or data that has been transformed into structured data.
Given the immense size of data warehouses, it is often more efficient to process data in smaller data repositories
holding structured data, called data marts. The smaller size of data marts makes it faster to access the data. It
also provides tighter internal control by making it easier to restrict user access to only data relevant to their
position.
A data lake is a collection of structured, semi-structured and unstructured data stored in a single location. All
data from inside the organization but also relevant data from outside the organization gets stored.
The size of data lakes can cause problems if they become so large that it allows important data to become dark
data. Dark data is information the organization has collected and stored that would be useful for analysis but is
not analysed and is thus generally ignored. Data lakes can also become data swamps, which are data repositories
that are not accurately documented so that the stored data cannot be properly identified and analysed.
Data goes dark or turns into a data swamp for many reasons, including the organization not understanding the
value of data analysis or not devoting sufficient resources to maintaining and analysing the data.
4
Data-driven Control
Summary
1
, 2
Inhoudsopgave
Week 1 ...................................................................................................................................................................... 3
Chapter 5. Introduction to Data Analytics in Accounting ..................................................................................... 3
Vasarhelyi et al. (2015) Big Data in Accounting. An Overview ............................................................................. 8
Geerts G.L. (2011) A design science research methodology and its application to accounting information
systems research ................................................................................................................................................ 12
Week 2 .................................................................................................................................................................... 16
Dai & Vasarhelyi (2017) Toward Blockchain-Based Accounting and Assurance ................................................ 16
Chapter 6.Transforming Data ............................................................................................................................. 22
Chapter 7. Data Analysis and Presentation ........................................................................................................ 26
Week 3 .................................................................................................................................................................... 32
Kuhn and Sutton (2010) Continuous Auditing in ERP System Environments: The Current State and Future
Directions ............................................................................................................................................................ 32
Singh, Best & Bojilov (2014) Continuous Auditing and Continuous Monitoring in ERP Environments: Case
Studies of Application Implementations ............................................................................................................ 36
Week 4 .................................................................................................................................................................... 42
Appelbaum et al. (2017) Impact of business analytics and enterprise systems on managerial accounting ..... 42
Chapter 1. Conceptual Foundations of Accounting Information Systems ......................................................... 49
Chapter 2. Overview of Transaction Processing and Enterprise Resource Planning Systems ........................... 55
Week 5 .................................................................................................................................................................... 61
Baader & Kremar (2018) Reducing false positives in fraud detection. Combining the red flag approach with
process mining .................................................................................................................................................... 61
Gray & Debreceny (2014) A taxonomy to guide research on the application of data mining to fraud detection
in financial statement audits .............................................................................................................................. 64
Chapter 4. Relational Databases ........................................................................................................................ 72
Week 6 .................................................................................................................................................................... 79
Chapter 18. General Ledger and Reporting System ........................................................................................... 79
Vasarhelyi, Chan & Krahel (2012) Consequences of XBRL Standardization on Financial Statement Data ........ 85
Week 7 .................................................................................................................................................................... 89
Van der Aalst, Bichler & Heinzl (2018) Robotic Process Automation................................................................. 89
Huang and Vasarhelyi (2019) Applying robotic process automation (RPA) in auditing: A framework ............. 90
2
, 3
Week 1
Chapter 5. Introduction to Data Analytics in Accounting
Introduction
This chapter explores data analytics and accompanying toolsets needed to turn this mountain of data into useful
information.
Using data appropriately has become especially important for accountants. Accountants in all different practice
areas are using data in exciting ways. For example:
• Auditors can test full populations of transactions rather than a small sample by using data analytics and
automation.
• Corporate accountants use data to make better decisions. Increased data allows them to make more
accurate assessments of risks and to identify opportunities to preserve and enhance value.
To understand the scope of the data revolution, it is important to consider the four V’s of Big Data:
1. Volume
2. Velocity
3. Variety
4. Veracity
Big data is the term companies use to describe the massive amounts of data they now capture, store and analyse.
Data volume refers to the amount of data created and stored by an organization. Data velocity refers to the
pace at which data is created and stored. Data variety refers to the different forms data can take. Data veracity
refers to the quality or trustworthiness of data.
Big data alone does not lead to improvements; rather, business professionals must analyse the big data to reveal
insights that lead to these improvements. To be successful in the future with big data, it is important to
understand more than tools and techniques; it is critical to develop an appropriate mindset that allows you to
think about data holistically.
A critical mindset for future accountants to develop is the analytics mindset. The Center for Audit Quality defines
an analytics mindset as the “ability to visualize, articulate, conceptualize, or solve both complex and simple
problems by making decisions that are sensible given the available information and ability to identify trends
through analysis of data/information.
According EY, an analytics mindset is the ability to:
• Ask the right questions
• Extract, Transform, and Load relevant data
• Apply appropriate data analytic techniques
• Interpret and share the results with stakeholders.
Ask the Right Questions
Data is defined as facts that are collected, recorded, stored and processed by a system. As such, data by
themselves offer little value. Only when data is transformed into information does it provide value.
To start the process of transforming data into information, one must have a question or desired outcome. Asking
the right question is the first step of the analytics mindset.
To define “right” or “good” questions in the context of data analytics, start by establishing objectives that are
SMART. A good data analytic question is:
• Specific – needs to be direct and focused to produce a meaningful answer
• Measurable – must be amenable to data analysis and thus the inputs to answering the question must
be measurable with data
3
, 4
• Achievable – should be able to be answered and the answer should cause a decision maker to take an
action
• Relevant – should relate to the objectives of the organization or the situation under consideration
• Timely – must have a defined time horizon for answering.
Extract, Transform, and Load relevant Data
ETL Process – a set of procedures for blending data. The acronym stands for extract, transform and load data.
Extracting data
Extracting data is the first step in the ETL process. The extraction process has three steps:
• Understand data needs and the data available
• Perform the data extraction
• Verify the data extraction quality and document what you have done
Understand data needs and the data available
Before extracting data, data needs should be carefully defined. Defining the question well makes it easier to
define what data is needed to address the question. Without defining the data well early in the process, it is
more likely that the wrong data or incomplete data will be extracted.
After defining the needed data, the next step is to understand the data itself, which entails understanding things
like location, accessibility and structure of the data.
A data warehouse generally describes the storage of structured data from many different sources in an
organization.
Structured data refers to data that is highly organized and fits into fixed fields. For example, accounting data in
the general ledger, data in a relational database and most types of spreadsheet data.
Unstructured data is data that has no uniform structure. Examples include images, audio files, documents,
emails.
Semi-structured data is organized in some ways but is not fully organized to be inserted into a relational
database. Examples include data stored in csv, xml and various streamed data (such as logs or machine-generated
operation data).
Data warehouses typically store only structured data or data that has been transformed into structured data.
Given the immense size of data warehouses, it is often more efficient to process data in smaller data repositories
holding structured data, called data marts. The smaller size of data marts makes it faster to access the data. It
also provides tighter internal control by making it easier to restrict user access to only data relevant to their
position.
A data lake is a collection of structured, semi-structured and unstructured data stored in a single location. All
data from inside the organization but also relevant data from outside the organization gets stored.
The size of data lakes can cause problems if they become so large that it allows important data to become dark
data. Dark data is information the organization has collected and stored that would be useful for analysis but is
not analysed and is thus generally ignored. Data lakes can also become data swamps, which are data repositories
that are not accurately documented so that the stored data cannot be properly identified and analysed.
Data goes dark or turns into a data swamp for many reasons, including the organization not understanding the
value of data analysis or not devoting sufficient resources to maintaining and analysing the data.
4
