Chapter 1: Intro Data Analytics for Accounting Flashcards
(21 cards)
What is data analytics in accounting?
process transforming + evaluating data
to draw conclusions
to answer business questions
help decision making
What are the 4 V’s of Big Data?
Volume: size of data
Velocity: speed of processing
Variety: different data types
Veracity: data quality
Why is Data Analytics important for businesses?
helps in improving
decision-making
efficiency,
and performance
through insights drawn from large datasets.
Impact DA on auditing
Audit quality
exception detection
automation: more time for evaluating results instead of collecting data
to clients: expanded services, enhanced audits & more efficient detection
Impact DA on management accounting
Better cost analysis
Improved decision making: real-time dashboards
Forecasting, budgeting, enable possibility to model different scenarios
Give the 6 step cycle of the IMPACT model
Identify the questions
Master the data
Perform the test plan
Address and Refine *results *
Communicate insights
Track outcomes
what is the purpose of step 1: identify the questions?
understand the business problems that need to be addressed
what is the purpose of step 2: master the data
–> what data is availbale and how can it help with our bunsiness problem?
requires one to know what data available and whether those data might help adress a business problem.
TE IN DEPTH
Give the 8 elements of step 2: master the data
Data availability
Internal and external sources
Data dictionaries
ETL process
Validation and completeness
Data normalization
Data preparation
Data scrubbing
Purpose Step 3: perform the Test Plan?
–> goal: identify relationship between variables
((identify significant relationships between the response variable and the items that affect the response)))
step 3: perform the test plan – give the 8 DA techniques
Classification
Regression
Similarity Matching
Clustering
Co-occurrence Grouping
Link Prediction
Profiling
Data Reduction
What is Classification?
Assigning data to pre-defined categories (e.g., approve/deny loan)
what is regression?
Predicting a continuous variable based on other variables (e.g., house price).
what is similarity matching?
identification of similar individuals/items based on KNOWN data
What is Clustering?
dividing individuals/items into useful groups (without predefined categories)
What is Co-occurrence Grouping?
discover associations or relationships between individuals/items based on frequently appear together
What is link prediction?
predicting new connections/relationshps are liely to form in network based on existing data
what is profiling
show normal behaviour look at idividual and vergelijk with populations
–> use descriptive things such as mean, max, min of pop and compare with individual
what is data reduction
reduce amount data and focus on critical and relevant items
what skills are needed?
accountants be able to…
Articulate business problems
communicate w data scientist –> BREACH THE GAP
draw good conclusion
present results in tailored manner –> based on audience (cfo vs stakeholders)
develop analytics mindset (seek data solutions actively)
develop analytical mindset:
know when and how to leverage data analy
cleaning data: scrubbing and preparation
descriptive data analysis
data analysis through data manipulation (rearrange data)
statistical data analysis competency (identifying appropriate statistical model to draw conclusions)
data visualization and data reporting
