1  How to use this material?

Why should you study (accounting) information systems & analytics?

FoMo, short for Fear of missing out, has been used since 2004 to describe the phenomenon observed on social networking sites of continuously wanting to be connected with what others are doing. FoMo is defined as ´pervasive apprehension that others might be having rewarding experiences from which one is absent´. Although the social media aspects to which FoMo refers to are documented to have serious implications for mental health (Gupta and Sharma (2021)), I would argue that there is a real potential for MO (Missing Out) when one does not study accounting information systems & analytics. Not investing time to discover this field can lead you to Miss Out on becoming a valued professional, or in better words, you can Miss Out on becoming ‘So good that they [employers, investors] can’t ignore you’.

1.1 The context

What is accounting? What are information systems? What is data analytics? How are these things connected?

I think we can use one word to connect all these items: MEMORY. In a 2009 well-published article, Sudipta Basu and colleagues made the case that accounting is a memory-aid which allowed humans to create and store ‘transactional records outside their brains for at least 10,000 years’ (Basu et al. 2009). Furthermore, in his great book on Data management: Databases and analytics (Watson 2023), Richard T. Watson introduces the term organizational memory to represent all the data of an organization, and connects this term to different types of information systems, from relational databases to business intelligence systems. Next, the analytics part refers to how we use what we store in memory.

Information systems in an umbrella term for every element that allows us to collect, store, process and share data and information. In order to get a quick understanding of the field of information systems, I recommend the chapters on Managing Data and Information from the book of Richard T. Watson (Watson 2023). Still, these chapters do not explain why this reader uses an accounting filter to discuss information systems & analytics. I’ll piggyback on something that Watson wrote to explain the accounting focus:

In the information society, which is based on innovation, knowledge, and services, the key determinant of success has shifted from product quality to decision quality.

I will add to this a twist, arguing that decision quality is dependent on information quality. And, the way I see it, accounting (with its auditing cousin) is (almost) all about information quality.

1.2 Why study Accounting information systems & analytics?

I once had to teach a group of experienced auditors. I started the session by asking them ‘What is your competitive advantage as accountants?’ It was a tough question and not many of them volunteered an answer. What do accountants do that ads value?

In a 2014 issue of the New York Times, the opinion columnist David Brooks wrote a column inspired by the bestselling book of Mason Currey, ‘Daily Rituals: How Artists Work’. Mason Currey describes in his book the daily rituals of highly inspirational people, from Maya Angelou to Benjamin Franklin. The emphasize in the book is on the extreme discipline that characterizes successful poets, philosophers and scientists (e.g., writing 250 words every fifteen minutes for three hours). In his column, David Brooks makes the point that discipline and rutine is fundamental for creativity and uses the phrase ‘[Creative people] think like artists but work like accountants’. Here, accountants seem to be viewed as gatekeepers of routines. Indeed, accountants do have a ritualistic job (e.g., the ritual of making budgets, the ritual of preparing financial statements) but this ritualistic aspect of accountants seems to not be unique, being used by many other professions.

So, what do accountants do that ads value? My claim is that accountants add value in two ways. First, they really understand the business. Second, they provide reliable information. They really understand the business because they need to translate it to a different language, the accounting language of financial reports. You cannot translate something which you do not understand. They also need to understand business processes in order to make sure the information provided by these processes is reliable. This leads us to the focus of this reader.

1.3 Activities

Given my claim that accountants bring value to the business because they really understand the business and because they provide reliable information, how can we make sure we strengthen this competitive advantage? I have devised the following activities in order to strengthen the competitive advantage of the accountant:

  1. We will start with understanding process diagrams. Process diagrams can help us visualize, and hence, help us understand how a business functions. These diagrams are simple and use circles to represent economic events (e.g., a customer making a sales order) and squares to represent documents (e.g., a Sale order document). I have specifically opted to represent processes with simple diagrams and not with more rigorous modelling approaches (such as the Business Process Modelling Notation BPMN). Simplified diagrams allows us to focus on the essence of the process and this is sufficient for our purposes. If you, on the other hand think ‘I wanna know more about this!’, then I’ll share with you another resources where I talk more about these diagrams.
  2. To these processes we attach information systems vernacular. One of the most frequently used textbooks in the information systems domain, Laudon and Laudon (2021), defined information systems as ‘a set of interrelated components that collect, process, store, and distribute information to support decision making and control in an organization’. For our purposes, we’ll focus on how we collect and store accounting data, how we process, or analyse, said accounting data, and how we communicate information based on the accounting data collected. The field of information systems is much more ample and can span from discussions on how to collect data with radio-frequency identification (RFID) tags to client/server computing. We will have a brief look at the field of information systems in the first chapters of Watson´s book on Data Management: Databases and Analytics. But how I am an avid disciple of deep work (holler at Cal Newport), I’ve focused this material on the collection, storage, processing/analysis, and communication of accounting data and information.
  3. Even more, we’ll focus specifically on the collection, storage, processing/analysis, and communication of accounting data and information in R. The choice of using a programming language for this material is essentially motivated by the fact that the software gives us feedback (e.g., through coding errors) and feedback is fundamental for learning. The choice for R (as opposed to Python or other programming languages) is excellently motivated by Westland (2020) in the section titled ‘Computers in Auditing and the Birth of Audit Analytics’.
  4. When it comes to processing (or analysing data) we’ll focus on the analyses done in the accounting domain which is engrosed in the business of information reliability: the auditing domain. As such, we’ll focus on analysing accounting data from an auditing perspective. This is very auspicious as it allows us to build the applications of this material on the book of J. Christopher Westland, Audit Analytics - Data Science for the Accounting Profession (Westland (2020)). Several aspects recommend this book. The author, J. Christopher Westland is a professor in the Department of Information & Decision Sciences at the University of Illinois and has professional experience as a certified public accountant and technology consultant. Impressively, the book has a very nice foreword by Erik Brynjolfsson, the author of the book ‘Race Against the Machine’. Plus, the R code in the book uses mostly base R although it does have influences from the great Hadley Wickham. Nevertheless, the book is published in the Use R! series which is fundamentally aimed at practitioners and people with experience in both R and auditing. So we’ll have to do some R and auditing groundwork ourselves in chapter two and three of this reader.
  5. For communicating information, we’ll use the wonderful work of Cole Nussbaumer Knaflic on storytelling with data.

1.4 Objectives

What should you be able to do after covering this material? Ideally, you would be able to:

  1. Reproduce the diagrams of business processes discussed. Yes, I know, the question is why do we need to remember anything nowadays, when every answer can be provided by GPT4? Well, why do you need to learn a new language when you have Google Translate? Ideally, you want to learn a new language so that you can use it without a computer attached to you. This step is fundamental because knowing how to put processes into diagrams helps you to understand how ‘normal’ data is being generated and what questions you should ask of the data to make sure it provides reliable information.

  2. Use R code to analyse accounting data. We’ll focus here on specific work related to the analysis of accounting data: we’ll simulate accounting data and then we’ll use data analytics methods to perform analytical procedures and substantive tests for auditing purposes.

  3. Based on accounting datasets, answer questions related to:

    • What you are interested to know from the data,
    • possible Analyses that can be used to process the data such that you find out what you want to know,
    • interpret the Results of the analyses obtained

    We can use the unfortunate acronym of W.A.R. to describe these learning goals:

  4. Use storytelling to communicate accounting information.

1.5 Structure

For obtaining an overview of information systems, we’ll use the book Data Management: Databases and Analytics of Richard T. Watson (Watson 2023). Next, in order to achieve our objectives, we’ll focus on three important processes: the record-to-report process (and the simulation of accounting data), the purchase-to-pay process (and the analytics of Inventory) and the order-to-cash process (and the analytics of Accounts receivables). This part will go hand in hand with Audit Analytics - Data Science for the Accounting Profession (Westland 2020). For the last part, we’ll turn our attention to the Storytelling with data book (Knaflic Nussbaumer 2015). The first book is freely available online and the last two books are available for students using the university library search.

The relevant chapters for our purposes from Data Management: Databases and Analytics (Watson 2023) are the chapters on Managing Data and Information.

Which chapters should you read from Westland (2020) ? Below, I offer a short overview of the chapters which are relevant for our purposes.

  1. The chapter Fundamentals of Auditing Financial Reports contains a very nice historical overview of accounting. It’s very interesting to read how accounting played such an important role in history. It also reviews some fundamental concepts such as the main financial reports produced by the accounting process.

  2. The chapter Foundations of Audit Analytics focuses on the format used to store data in R. This is important as the type of data and the format in which the data is stored has consequences for the analysis of the data.

  3. The chapter Risk Assessment and Planning has a section on Accessing the SEC’s EDGAR Database of Financial Information which presents a good case study on how data stored in an XBRL format can be used for data analytics purposes.

  4. In the chapter of Substantive Testing, transactions related to sales and accounts receivable are explored using analytics. This is a relevant chapter for us. The chapter Simulated Transactions for Auditing Service Organizations is especially important as it helps us simulate the accounting data that we want to analyse.

    To sum up, we’ll mainly focus on five chapters of the Audit Analytics - Data Science for the Accounting Profession.

From the Storytelling with data book (Knaflic Nussbaumer (2015)) we’ll cover:

  1. Chapter one, the importance of context, as it contains important elements for any communication with data: the Big Idea, the 3-minute story and the Storyboard
  2. Chapter two, choosing an effective visual, discusses the advantages and disadvantages of different types of visualizations
  3. Chapter seven, lessons in storytelling, which introduces the art of storytelling with data
  4. Chapter eight, pulling it all together, which discussed a comprehensive example of storytelling with data

1.6 Question

Which important concepts are discussed in the chapter on Managing Data and the chapter on Information from the book of Richard T. Watson (Watson 2023)?