Acc 300 Midterm Study Guide

What is Accounting?

an information system that provides reports to users about the
economic activities and condition of a business.

Journal Entries:

Transaction input into an accounting system to capture and record economic activity

Trial Balance

report that lists the balances of all general ledger accounts
of a company at a certain point in time

Financial Statements

a collection of summary-level reports about an organization's financial results, financial position, and cash flows

The art of data analytics

- know what questions to ask
- frame hypotheses
- explore and discover
- make data-driven decisions

Why analytics for accounting?

improve judgments and efficiency, reduce costs, and enhance understanding of the business

Why do we focus on communication skills?

Professionals tend to express disappointment with writing
communication skills of accounting students

Top 3 Skills

Analytical thinking

Resilience, flexibility, and agility

Leadership and social influence

AMPS Model

1. Ask the Question
2. Master the Data
3. Perform the Analysis
4. Share the Story

Examples of how accounting data analytics can be used

find fraud, valuation, estimates, audits, and predictions

Types of accounting data used in accounting analytics

Journal entries
General Ledger
Trial Balance
F/s

What is a subledger

provides a detailed record of specific financial transactions, which then roll up into the general ledger through a control account.

Types of non-accounting data used in accounting analytics

- Macro economic data
- Current and historical stock prices
- Social media data
- Analyst eps forecasts
- Customer reviews

Structured Data

Highly organized data that fits nicely in a table or in a database

Still, structured data often has to be reformatted for analysis
• Examples: journal entries, financial statements

Unstructured Data

Text data without internal organization
• Examples: transcripts of earnings calls, tweets, Instagram posts

Categorical Data

Data that tends to “categorize” items represented by words
• (“dimension” in Tableau)
• Example: hair colors (blonde, brunette, etc.)

Numerical Data

Data that takes the form of meaningful numbers
(measure in Tableau)
• Example: net income, age, exam score

Nominal Data (categorical)

categorical data that cannot be ranked

Ordinal Data (categorical)

categorical data that can be ranked

Interval data (numerical)

data that has an equal and definitive interval
between each data point, but no meaningful 0 (i.e., 0 does not mean “the absence of something”)

ex: temp, SAT score

Ratio Data

data that has an equal and definitive interval between each data point and a meaningful 0, allowing for the calculation of ratio

How do accountants get access to data?

If you work internally – you will have access to the company systems
If you are an auditor or consultant – you will have to ask for the data

Database

a structured dataset that can be accessed by many
potential users via a computer system or network

Four powerful tools for analyzing data

Excel, Alteryx, Tableau and Power BI

excel

analyzing and exploring data

alteryx

tidying and analyzing data

tableau

analyzing data
biggest advantage is data visualization
not possible to create raw data

power BI

powerful tool for analyzing data.

Biggest advantages are data visualization and step documentation

Hard and not possible to create raw data

Relational database

database that breaks data into separate tables, each containing a unique list of items stored
(instead of storing all the data in one massive table).

Tables

data organized into sets of columns (fields) and rows
(records).

Fields

also called variables; columns that contain descriptive characteristics about the observations in the table.

Records

the rows, with each observation corresponding to
a record, or unique instance, of what is being described in the data.

Primary key

any field that functions as a unique identifier
in a table.

Foreign key

exists to create relationships between two
tables.

Data integrity and three characteristics

truth in data

is free from error and accurate, complete, and neutral

Preventative internal control

Example: suppliers receiving checks are verified by a company’s system – can’t write a check to a supplier that isn’t in the supplier table

Security around data entry

ex: IT group can limit access – who has permission to edit the table?

Reduced redundancy = less room for errors

Example: if I had a Student Table where I kept all of your preferred names, and only had your student ID in all of my grade spreadsheets, etc., that would limit the errors I could make regarding your names.

Version control

Example: if two people are editing a table at the same time, a database can
handle that, Excel cannot.

(ETL)

Extract, Transform, Load

ETL meaning

extract data, transform data so its ready for use, load data into the tool you wish to use for analysis.

Descriptive analytics definition

characterizes, summarizes, and organizes features and properties of the data to understanding results and underlying data

descriptive analytics helps answer the question…

what happened?

descriptive analytics examples in accounting

-Financial statements
-The balance of inventory on hand
-The average balance of A/P over the year
-The dollar value of A/R balances over 30 days old
-The federal taxes paid last year

financial statements, inventory balance, a/p balance, a/r balance over 30 days, fed taxes paid

Diagnostic analytics definition

investigate the underlying reasons for past results that cant be answered by simply looking at the descriptive data

Diagnostic analytics answers the question…

Why did it happen?

Diagnostic analytics examples

Why did wage expense increase this quarter compared to last quarter?
• Why did our A/R over 30 days old grow compared to last year?
• Why did our sales increase in Illinois, but not in California?

Two broad categories for diagnostic analytics

Identify anomalies/outliers
Find linkages, patterns, or relationships between and among variables
ex:benfords law, duplicate transactions, sequence checks

Predictive analytics definition

Provide foresight by identifying patterns in historical data to judge likelihood or probability of future events

Predictive analytics answers the question of

will it happen in the future?

Predictive analytics examples in accounting

What is our predicted future cash flow?

Will the SEC inspect us next year?

Prescriptive analytics definition

Identify the best possible options given constraints or changing conditions.

Prescriptive analytics answers the question

What should we do?

prescriptive examples in accounting

Should the company buy or lease its office building?

Should the company move operations to Ireland to minimize taxes?

Descriptive analytics – variance analysis

an analysis of the difference between
actual numbers and some baseline/ expectation

Descriptive analytics – vertical analysis

expresses financial information in relation to some relevant figure or base

ex: cogs as a percentage of sales on the income statement

Descriptive analytics – horizontal analysis

comparative changes about various line items

Anomaly

something that deviates from what is expected

Outlier

an observation that differs from other members of the group

anomaly: internal controls testing

Expectation: senior management does not
make journal entries
Violation: the CEO made a journal entry

anomaly: exact matching

Expectation: vendors and employees do not
share an address
Violation: employee address is the same as
vendor address

anomaly: sequence checks and sequence analysis

Expectation: checks to vendors are sequential

Violation: the list of payments is missing a check number

anomaly: Duplicate transactions

Expectation: a vendor will not be paid more
than once per month

Violation: the list of payments indicates that a
vendor was paid 3 times for the same amount
in one month

anomaly: Benford’s law

Expectation: there will be more numbers starting with 1s and 2s than other numbers

Violation: the list of payments has more digits that start with the number 9 than any other number

anomaly: Variance analysis

Expectation: expenses will be the same this year compared to last year

Violation: expenses increased this year compared to last year

anomaly: Cash/bank reconciliation

Expectation: cash per the G/L equals cash per the bank statement

Violation: cash per the G/L does not equal cash per the bank statement

Null hypothesis

The hypothesized relationship does not exist
There is no significant difference between two samples

Alternative hypothesis

opposite from the null hypothesis

The hypothesized relationship does exist
There is a significant difference between two samples

T statistic

tells us how many SDs we are away from the mean, which then tells us the probability that our observations are due to chance

A two-sample t-test

used to determine if the means of two different populations are the same or statistically different from each other

P-value

Probability that variation is due to chance.
- If low (less than 0.05), results have significance.
- Based on a normal distribution

regression

Help measure the relationship between one output variable and various inputs.

y=mx+b

coefficient

Regressions produce a coefficient, which tells us the extent to which variables are related to each other.

dependent

y axis, outcome variable

independent variable

x axis, predictor variable

Artificial intelligence

the general ability of computers to emulate human
thought and perform tasks in real-world environments

Machine learning

the technologies and algorithms that enable systems to identify patterns, make decisions, and improve themselves through experience and data.
ML is a subset of the broader category of AI.

AMPS guide 1: what is step 1 and why? (A)

Ask the question
What decision needs to be made or what problem needs to be solved based on this information?

AMPS guide 1: What is the M?

Mastering the Data

AMPS guide 1: What are the 10 steps for mastering the data?

1.Determine the purpose of the analysis
2.Based on the purpose determined in step 1, decide what data you need and where
you can get the data
3. Retrieve your data
4. Check data for completeness
5. Determine whether the data are trustworthy/accurate
6. Are the data in analyzable formatting?
7. Does each column title have a name? Is it appropriate? Is it concise?
8. Do you need all the data fields?
9. Is the naming convention in all the cells standardized (within field)?
10. Are there any blank cells? Is that appropriate?
11. Check for duplicates

Completeness

Helps us answer the question: How do we know somebody didn’t delete a row from the Excel spreadsheet?
some common ways to test: sequence test, agreeing the sum, comparing to an independent expectation

Accuracy

Helps us answer the question: How do we know the data that is already on the Excel spreadsheet is correct
Common tests: source documentation, understanding where the data came from and if its trustworthy, understanding how the data was collected, controls over data entry and error detection

AMPS guide 2: 4 types of analytics

Descriptive, diagnostic, predictive, prescriptive

Amps guide 2: Steps to performing the analysis

1.go back to “answer the question”

2.On a piece of paper, draw a few charts or tables (without data) that illustrate what you would like to create considering the decision that needs to be made.
3.descriptive
4.diagnostic (anomalies and outliers)
5.Analyze datasets using drill-down and statistical techniques to discover unknown
patterns, links, and relationships
6.Create data visualizations (in Excel and Tableau) to inform business decisions.