What is data analytics?
The process of evaluating data and coming up with insights/conclusions that can help organizations.
What are the 3V’s of Data?
Volume - size
Velocity - frequency
Variety - different types
How does data analytics affect auditing?
- Enhances audit quality by being able to test complete sets of data as opposed to samples.
- Identifying anomalies or trends
How does DA affect financial reporting?
- Better estimates of collectability (bad-debt), and other things like contingent liabilities, etc.
- Better understanding of business environment through social media.
- Identify risks or opportunities
How does DA affect Tax?
- Sophisticated tax planning strategies
- Better understanding of international transactions, investments, M&As
3.
What is the IMPACT acronym?
and what is its purpose?
- Identify the questions
- Master the data
- Perform test plan
- Address and refine results
- Communicate insights
- Track outcomes
Purpose: it’s a data analytics model. Essentially, it provides a framework or methodology for addressing data analytic questions
I in IMPACT
Identify the Questions
Understand the business problems that need to be addressed.
Having a concrete, specific question that is potentially answerable by Data Analytics is an important first step.
M in IMPACT
Mastering the data
Knowing what data is available inside the ERP of the company and how they relate to the problem you’re trying to solve.
P in IMPACT
Perform the test plan
Analyzing the data: selecting the appropriate model to find a target variable
Using all available data, we see if we can identify a relationship between the response or dependent variables and those items that affect the response (also called predictor, explanatory, or independent variables). To do so, we’ll generally make a model, or a simplified representation of reality, to address this purpose.
A in IMPACT
Address and Refine Results
It is an iterative process to arrive at the best answer
ask further questions, explore data, rerun analyses
But once that is complete, we have the results ready to communicate to interested stakeholders.
C & T in IMPACT
Communicate Insights and Track Results
A picture’s worth a thousand words, that being said don’t neglect substance for the superficial though.
(visualizations)
Tracking outcomes - how frequently should you perform the analysis, what are the trends?, what has changed since you did your analysis?
Big Data
Datasets that are too large and complex for businesses’ existing systems to handle utilizing their traditional capabilities to capture, store, manage, and analyze these datasets.
Classification
A data approach that attempts to assign each unit in a population into a few categories potentially to help with predictions
Clustering
a data approach that attempts to divide individuals (like customers) into groups (or clusters) in a useful or meaningful way.
co-occurrence grouping
A data approach that attempts to discover associations between individuals based on transactions involving them.
Data dicitionary
Centralized repository of descriptions for all of the data attributes of the dataset.
data reduction
A data approach that attempts to reduce the amount of information that needs to be considered to focus on the most critical items (i.e., highest cost, highest risk, largest impact, etc.)
link prediction
A data approach that attempts to predict a relationship between two data items.
profiling
A data approach that attempts to characterize the “typical” behavior of an individual, group or population by generating summary statistics about the data (including mean, standard deviations, etc.).
predictor (or independent or explanatory) variable
A variable that predicts or explains another variable.
response (or dependent) variable
A variable that responds to, or is dependent, on another
regression
A data approach used to predict a specific dependent variable value based on independent variable inputs using a statistical model.
similarity matching
A data approach that attempts to identify similar individuals based on data known about them.
Effects of data analytics on audit
- Shift from checking for errors, material misstatements, fraud and risk in financial statements to collecting and analyzing data much like a business analyst does and providing insights to clients for decision making. This is expected to change the length of engagements with clients for external auditors
- It also improves capabilities in fraud detection as you no longer need to limit yourself to testing a sample only (substantive and detailed testing improved)
