Samenvatting Data Analytics in Accountancy ('23-'24) (Handelsingenieur en TEW) (18/20)

CHAPTER 1: Data analytics in accounting and businesses:
1.What is data analytics:

Data analytics = the process of evaluating data with the purpose of drawing conclusions to address
business questions
 provides a way to search through large structured (predefined data format) and unstructured
(pdf, text formats,…) data to identify unknown patterns or relationships
 format does not really matter

Goal: transform data into valuable knowledge to make more informed business decisions

Big data = datasets which are too large and complex to be analyzed traditionally
 59 billion terabytes of data in 2020 while 2 in 2010

Remember the 4 V’s:
 Volume = size of the dataset
 Velocity = speed of processing
 Variety = different types of data
 Veracity = data quality

2.Effect of data analytics:

2.1 EFFECT ON BUSINESSES:

Importance:
 85% of CEOs put a high value on data analytics
 86% of CEOs place data mining and analysis as 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
 SO it is not only about having the data but also using it in the best way possible

2.2 EFFECT ON AUDITING

General: enhances audit quality + expanded services + added value to clients
 audit process is changing from a traditional process towards a more automated one
1. allows audit professionals to focus more on the logic and rationale behind data queries and
less on gathering the actual data
2. expanded capabilities: testing for fraudulent transactions + automating compliance-
monitoring activities
3. analyze complete dataset rather than sampling financial data

2.3 MANAGEMENT ACCOUNTING

 data analytics and management accounting have quit similar task descriptions

Enhancements: cost analysis, better decision-making, better forecasting, budgeting, production and
sales

, 2.4 FINANCIAL REPORTING

 better estimates of collectability, write-downs, …
 better understand business environment through social media and other external data
sources
 analysts identify risks and opportunities through analysis of internet searches

3.The impact model:



The impact model:
 Identify the questions
 Master the data
 Perform the test plan
 Address and refine results
 Communicate insights
 Track outcomes




STEP 1: IDENTIFY THE QUESTIONS:

= understanding the business problems that need to be addressed

 attributes to consider:
 do we have the right data to answer the question?
 Who is the audience that will use the results?
 Is the scope of the question too narrow or to broad ?
 How will the results be used  analyze risk, make decisions, …

Examples 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?

STEP 2: MASTER THE DATA

= know what data are available and how they relate to the problem

Consider the following 7 elements:
 Data available in internal ERP systems
 Data available in external networks and data warehouses