Chapter 6 :Perform the Analysis - Descriptive Analytics

Chapter 5 introduced the third step of the AMPS model: Perform the Analysis.
Four Types of Analytics:
- Descriptive Analytics: Addresses "What is happening?" or "What happened?".
- Diagnostic Analytics: Explains why something has happened.
- Predictive Analytics: Forecasts future occurrences based on patterns.
- Prescriptive Analytics: Recommends actions based on analysis of data.
Concluded with an introduction to basic statistics important for data analysis and hypothesis testing.

This chapter focuses on how descriptive analytics answers questions regarding past and current performance.
Tools and techniques in descriptive analytics will be explained.
This serves as the first step in the Perform the Analysis component of the AMPS model, potentially leading to more advanced analytics.

Chapter 7 will introduce diagnostic analytics, focusing on techniques to explore anomalies, drill into details, and establish causal relationships.

After reading this chapter, you should be able to:

LO 1: Define descriptive analytics.
LO 2: Describe the data, tools, and techniques used to perform descriptive analytics.
LO 3: Illustrate through examples of descriptive analytics.
LAB 1: Perform Accounts Receivable Aging analysis in Excel, Tableau, and Power BI.
LO 4: Describe vertical, horizontal, and DuPont analysis as tools for descriptive analytics.
LAB 2: Conduct horizontal and vertical analysis in Excel with Sparklines.
LAB 3: Complete DuPont analysis of financial performance.
LO 5: Describe the potential next steps following descriptive analytics.

Definition: Descriptive analytics is the assessment aimed at addressing "What happened?" or "What is happening?"
It characterizes, organizes, and summarizes data features to facilitate understanding.
It serves as a preliminary analysis before moving to diagnostic, predictive, or prescriptive stages.
For effective decision-making, accounting must provide relevant insights that faithfully represent real occurrences.

Financial statements are rich sources of data for summarization:
- Balance Sheet: Shows assets available, liabilities, and stockholders’ equity.
- Income Statement: Summarizes revenues, expenses, and taxes.
- Statement of Cash Flows: Reveals cash inflows and outflows.
- Statement of Stockholders’ Equity: Explains retained earnings and dividends paid.
- Footnotes: Provide policy details (e.g., inventory, depreciation) and segment performance information.
Annual reports and 10-K filings to the SEC also provide valuable insights into company performance.

Basic Tools: Summarization and statistical tools.
- Descriptive Statistics defined as brief factoids summarizing a dataset, classified into:
- Measures of Central Tendency: Such as mean, median, and mode.
- Measures of Variability: Such as range (maximum and minimum), standard deviation, quartiles, and deciles.
  - Example calculations:
  - Mean Salary: Average based on HR data.
  - Minimum/Maximum Sales: Helps identify outlier transactions.
- Counts: Total occurrences of a data item, e.g., customer count.
- Totals and Sums: Summary measures, e.g., annual net income.
- Graphs: Visual tools like line, bar, or pie charts to present data relationships.
  - E.g., bar chart showing revenue changes.
- Percentage Change: Measures the rate of change between periods.
- Pivot Tables and Charts: Facilitate data reorganization for summary analytics.
- Histograms: Show data frequency distributions visually.
- Ratio Analysis: Evaluates financial status (liquidity, solvency, profitability).
- Vertical and Horizontal Analysis: Help contextualize financial data based on a relevant base.

Financial Performance Analysis: Tables and graphs show changes in income and sales.
- Example of Amazon’s financial performance: A table showing Net Income and Sales from 2008 to 2020.
- A bar and line chart visualization to display performance trends.
Comparison Groups: Assess performance relative to prior years or competitors (e.g., comparing Amazon to Walmart or the overall market).
Aged Receivables Analysis: Understanding credit sales collectability by categorizing outstanding receivables into time buckets (1-30 days, etc.).

Diagnostic Analytics Transition: Often follows descriptive analysis to understand market behaviors or anomalies.
- Identify customer payment issues or account disputes using CRM data.

Horizontal Analysis: Tracks changes across income statements or balance sheets; calculates dollar and percentage changes.
Vertical Analysis: Expresses financial information as a percentage of a relevant base (income statement vs. revenue, balance sheet vs. assets).
DuPont Analysis: Breaks down return on equity into profitability, asset turnover, and financial leverage.
- ROE = Profit Margin * Asset Turnover * Financial Leverage.

Descriptive analytics launches subsequent analyses by establishing a foundational understanding of data and trends.
Tools used, such as pivot tables, graphs, ratio analyses, and exploratory tables, provide essential insights for decision-making and further analysis.

Aged Receivables: Analysis technique to gauge credit sales collectability.
Descriptive Statistics: Summarization of dataset characteristics.
Horizontal Analysis: Year-over-year change analysis of financial statement line items.
Vertical Analysis: Percentile representation of financial data relative to a base.
DuPont Ratio Analysis: Breakdown of ROE into its three components.