SUMMARY DATA
ANALYTICS IN
ACCOUNTING
Prof. Wets and Vanhaverbeke
Abstract
This is a complete summary of the course Data analytics in account [D0B04a]
Jules Van Echelpoel
,Inhoud
Chapter 1: ...............................................................................................................................1
Why is data analytics important for accountants ...................................................................1
The impact model ................................................................................................................2
Chapter 2: Mastering the data ..................................................................................................4
How is data stored and organized? .......................................................................................4
ETL ......................................................................................................................................5
Chapter 3: Analyzing the results ...............................................................................................7
Descriptive and Diagnostic analytics ....................................................................................7
Predictive and Prescriptive analytics .....................................................................................8
Chapter 4: Communicating results......................................................................................... 10
Determine the purpose ...................................................................................................... 10
Choosing the right chart ..................................................................................................... 11
Chapter 5: Modern accounting – Enterprise Data .................................................................... 12
Chapter 6: Audit Analytics ..................................................................................................... 13
Applying the IMPACT model on auditing: ............................................................................. 13
Chapter 7: Managerial Analytics ............................................................................................. 15
Chapter 8: Financial Statement Analytics ............................................................................... 17
Chapter 9: Tax analytics ......................................................................................................... 20
Chapter 1:
Why is data analytics important for accountants
What is data analytics?
• The process of transforming and evaluating data with the purpose of drawing
conclusions to address business questions.
Essentially turning data into knowledge, using Big Data.
Big data uses the 4 v’s, (Volume = size, Velocity = speed, Variety = different types, Veracity =
quality)
Numbers:
• Global volume of data of the last 2 years > volume of data in all the years before
• Could generate up to $2 trillion per year
P.1
Jules Van Echelpoel
,Data analytics effects different kinds of domains such as:
• Auditing
o Enhances quality, helps in automation
• Management accounting
o Helps cost analysis, decision-making and forecasting
• Financial reporting
o Helps better understand the environment of the business, helps identifying risks
and opportunities
The impact model
The impact model stands for:
• Identify the questions
• Master the data
• Perform the test plan
• Address and refine results
• Communicate insights
• Track outcomes
The impact model is a cycle that is never finished! You can
cycle back in A if the question is not answered correctly or see
if your predictions were right in step T.
Identify the questions
Understand the business problems that need to be addressed!
What to consider?
• What data do we need to answer the question? (does it exist?)
• Who is the audience that will use the results (management or customers?)
• Is the scope of the question too narrow or too broad?
• How will the results be used?
Examples of questions:
• Are there suspicious transactions?
• Are we getting paid on time?
• …
P.2
Jules Van Echelpoel
, Master the data
Very important step! In this step we prepare the data for the analysis.
There are 8 elements to consider:
1. Know what data are available and how they relate to the problem
2. Data available in internal systems
3. Data available in external networks and data warehouses
4. Data dictionaries (place where all the data is defined, for example sort of variable)
5. ETL (Extract – Transform – Load)
6. Data validation and completeness
7. Data normalization
8. Data preparation and scrubbing (most important and time consuming step)
Perform the test plan
Identify a relationship between the response (dependent) variable and the items that affect the
response (predictor, explanatory or independent variables)
8 key approaches to data analytics depending on the question:
1. Classification (assign each unit in a population to a PRE-DEFINED category, example:
loan approved or denied)
2. Regression (predict a dependent variable’s value based on a model with independent
variables)
3. Similarity matching (identify similar individuals based on known data, example: St. =
Street)
4. Clustering (divide individuals into groups without pre-defined categories)
5. Co-occurrence grouping (discover relationship between individuals based on shared
transactions, example: ‘other people also bought this’)
6. Link prediction (predict connection between two data items, example: people you might
know)
7. Profiling (characterize the typical behavior of someone by generating summary statistics
like mean or median)
8. Data reduction (reduce the amount of data to focus on the most important data)
Adress and Refine results
Identify issues with the analyses and refine the model, explore the data and ask further
questions that could lead to extra insights. If this is the case rerun the analyses.
Communicate insights
Communicate effectively using clear language and visualization like dashboard, summaries etc.
P.3
Jules Van Echelpoel
ANALYTICS IN
ACCOUNTING
Prof. Wets and Vanhaverbeke
Abstract
This is a complete summary of the course Data analytics in account [D0B04a]
Jules Van Echelpoel
,Inhoud
Chapter 1: ...............................................................................................................................1
Why is data analytics important for accountants ...................................................................1
The impact model ................................................................................................................2
Chapter 2: Mastering the data ..................................................................................................4
How is data stored and organized? .......................................................................................4
ETL ......................................................................................................................................5
Chapter 3: Analyzing the results ...............................................................................................7
Descriptive and Diagnostic analytics ....................................................................................7
Predictive and Prescriptive analytics .....................................................................................8
Chapter 4: Communicating results......................................................................................... 10
Determine the purpose ...................................................................................................... 10
Choosing the right chart ..................................................................................................... 11
Chapter 5: Modern accounting – Enterprise Data .................................................................... 12
Chapter 6: Audit Analytics ..................................................................................................... 13
Applying the IMPACT model on auditing: ............................................................................. 13
Chapter 7: Managerial Analytics ............................................................................................. 15
Chapter 8: Financial Statement Analytics ............................................................................... 17
Chapter 9: Tax analytics ......................................................................................................... 20
Chapter 1:
Why is data analytics important for accountants
What is data analytics?
• The process of transforming and evaluating data with the purpose of drawing
conclusions to address business questions.
Essentially turning data into knowledge, using Big Data.
Big data uses the 4 v’s, (Volume = size, Velocity = speed, Variety = different types, Veracity =
quality)
Numbers:
• Global volume of data of the last 2 years > volume of data in all the years before
• Could generate up to $2 trillion per year
P.1
Jules Van Echelpoel
,Data analytics effects different kinds of domains such as:
• Auditing
o Enhances quality, helps in automation
• Management accounting
o Helps cost analysis, decision-making and forecasting
• Financial reporting
o Helps better understand the environment of the business, helps identifying risks
and opportunities
The impact model
The impact model stands for:
• Identify the questions
• Master the data
• Perform the test plan
• Address and refine results
• Communicate insights
• Track outcomes
The impact model is a cycle that is never finished! You can
cycle back in A if the question is not answered correctly or see
if your predictions were right in step T.
Identify the questions
Understand the business problems that need to be addressed!
What to consider?
• What data do we need to answer the question? (does it exist?)
• Who is the audience that will use the results (management or customers?)
• Is the scope of the question too narrow or too broad?
• How will the results be used?
Examples of questions:
• Are there suspicious transactions?
• Are we getting paid on time?
• …
P.2
Jules Van Echelpoel
, Master the data
Very important step! In this step we prepare the data for the analysis.
There are 8 elements to consider:
1. Know what data are available and how they relate to the problem
2. Data available in internal systems
3. Data available in external networks and data warehouses
4. Data dictionaries (place where all the data is defined, for example sort of variable)
5. ETL (Extract – Transform – Load)
6. Data validation and completeness
7. Data normalization
8. Data preparation and scrubbing (most important and time consuming step)
Perform the test plan
Identify a relationship between the response (dependent) variable and the items that affect the
response (predictor, explanatory or independent variables)
8 key approaches to data analytics depending on the question:
1. Classification (assign each unit in a population to a PRE-DEFINED category, example:
loan approved or denied)
2. Regression (predict a dependent variable’s value based on a model with independent
variables)
3. Similarity matching (identify similar individuals based on known data, example: St. =
Street)
4. Clustering (divide individuals into groups without pre-defined categories)
5. Co-occurrence grouping (discover relationship between individuals based on shared
transactions, example: ‘other people also bought this’)
6. Link prediction (predict connection between two data items, example: people you might
know)
7. Profiling (characterize the typical behavior of someone by generating summary statistics
like mean or median)
8. Data reduction (reduce the amount of data to focus on the most important data)
Adress and Refine results
Identify issues with the analyses and refine the model, explore the data and ask further
questions that could lead to extra insights. If this is the case rerun the analyses.
Communicate insights
Communicate effectively using clear language and visualization like dashboard, summaries etc.
P.3
Jules Van Echelpoel
