Data Analytics

Data Analytics in Intermediate Accounting III

Introduction

  • Course: Intermediate Accounting III

  • Institution: University of Georgia

Why Focus on Data Analytics?

  • Importance of data analytics in modern accounting

  • Ability to harness data to provide better information to decision makers

  • Surge in data generation equivalent to what existed on the entire Internet 20 years ago

The 4th Industrial Revolution

Overview

  1. 1.0: Based on mechanical production driven by water & steam power.

  2. 2.0: Based on mass production enabled by electrical energy.

  3. 3.0: Based on the use of computers and electronics to further enhance automation.

  4. 4.0: Based on cyber-physical systems.

Key Technologies

  • Space Technologies

  • Artificial Intelligence

  • Super Computing

  • 3D Printing

  • Augmented Reality (AR) & Virtual Reality (VR)

  • Advanced Materials

  • Automation & Robotics

  • Biotechnology

  • Internet of Things (IoT)

  • Geo Engineering

  • Battery & Energy Storage

  • Blockchain & Distributed Ledger Technology

Effects of the 4th Industrial Revolution on Business

Affected Areas

  • Security: Cybersecurity, Centralized Repository, IoT Security, Blockchain

  • Interface: Buttons, Voice & Gestures

  • Decision Making: Human drivers vs. Artificial Intelligence; Role of Autonomous Robots

  • Mobility in Factories: Data and Spreadsheet Utilization

Role of Accountants in Data Analytics

  • Accountants as critical information providers

  • Question of integrating data and machines for better decision-making

  • Need for accounting expertise amid automation

  • Data analytics = A pathway for accountants to showcase their expertise

Bloom's Taxonomy in Accounting

  • Hierarchy of critical thinking skills:

    • Create: Where Accountants Must Play

    • Evaluate: Accountants use judgment

    • Analyze: Accountants conduct in-depth analysis

    • Apply: Understanding & application of accounting principles

    • Understand & Remember: Where Machines Excel

Importance of Data Analytics in Accounting

  • Moves beyond the basic academic curriculum

  • Moves to higher-order thinking: analyze, evaluate, create

  • Accountants require foundational knowledge to leverage analytics

AMPS Framework for Data Analysis

  1. Ask the Question: What happened? What is happening? Why? What should we do?

  2. Master the Data:

    • Relevance vs. Reliability

    • Internal vs. External data considerations

    • Data integrity checks: accuracy, validity, consistency, etc.

  3. Perform the Analysis:

    • Types of analysis:

      • Descriptive

      • Diagnostic

      • Predictive

      • Prescriptive

    • Statistical Techniques and Tools: Excel, Tableau, etc.

  4. Share the Story:

    • Best practices for data communication: Visualizations, Reports, etc.

Types of Accounting Questions

  • Descriptive Analytics: What happened?

  • Diagnostic Analytics: Why did it happen?

  • Predictive Analytics: Forecasting future events

  • Prescriptive Analytics: Recommended actions based on predictions

Summary of Descriptive Analytics

  • Focuses on summarizing and organizing data to understand characteristics

  • Descriptive analytics often performed first before deeper analysis

Using Descriptive Analytics

  • Example: Suspecting companies manage earnings to report profits.

  • Visualization tools like histograms can illustrate findings effectively.

Definition of Diagnostic Analytics

  • Investigates "Why it happened?" and reasons for past results

  • Goes beyond descriptive data to uncover underlying reasons

Types of Diagnostic Analytics

  1. Identifying Anomalies/Outliers: Looking for unexpected results.

  2. Finding Linkages: Understanding patterns and relationships among variables.

Examples of Expectations vs. Anomalies in Accounting

  • Financial Accounting: Profits tripling unexpectedly.

  • Managerial Accounting: Job cost deviations due to overtime impacts.

Tools for Descriptive and Diagnostic Analytics

  • Histograms, mean, median, standard deviation, and correlation coefficients are essential tools for analysis.

Importance of Mean and Median in Analysis

  • Mean can be misleading due to outliers; comparing with medians can provide clarity.

  • Example: Employee salaries illustrating the impact of a high-earning CEO on average wage.

Mastering Data in the AMPS Framework

  • Importance of integrity, addressing errors, and making data accessible.

  • Distinguishing between types of data for effective analysis.

Performing Analysis with Data

  • Use visual tools like histograms, regression analysis, and what-if scenarios to gain insights.

Sharing Findings from Data Analysis

  • Effective communication methods include reports, graphs, tables, and dashboards.

Learning Data Analytics Skills

  • Engage hands-on: Come up with a question, use software, and refer to resources like StackOverflow, YouTube, and GitHub.