Master the Data: Data Types Used in Accounting

A Look Back

  • Chapter 2 introduced Master the Data step of the AMPS model.

  • Accountants need to be aware of available data to answer accounting questions.

Data Sources for Accounting Decision Making

  • Accountants analyze internal and external data.

A Look at This Chapter

Overview of Data Types

  • Introduces categorical (nominal, ordinal) and numerical (interval, ratio) data.

  • Distinguishes between structured and unstructured data.

  • Data dictionaries are critical for understanding data in databases.

  • Data types determine appropriate data visualization and summarization methods.

A Look Ahead

  • Chapter 4 introduces the ETL (Extract, Transform, Load) process for data preparation.

  • Covers accessing internal and external data.

Master the Data: Data Types Used in Accounting

The Changing Role of Accountants

  • Accountants can now directly work with raw data due to technology, reducing reliance on IT.

  • Cooperation between IT and accounting saves time.

Database Concepts & Data Dictionaries

  • Data dictionaries/catalogs help interpret raw data for reports (e.g., Excel PivotTables).

    • Data Dictionary: Defines the structure and type of data.

    • Data Map: A metadata repository.

  • Accountants must understand data types to create effective reports for decision-making.

The Nature of Data: Structured vs. Unstructured

  • Structured Data: Organized in tables/databases, includes categorical and numerical types.

  • Unstructured Data: Lacks internal organization (e.g., social media text, images, video files), requires advanced processing.

Types of Data

  1. Categorical Data

    • Definition: Classifies items, represented by words/labels without true numerical value.

    • Includes:

      • Nominal Data: Labels that cannot be ranked.

        • Examples: Gender, transaction types (sales/returns), Invoice Number, AssetID, product category, audit report types, Amazon Prime eligibility (Y/N).

        • Summary Methods: Counting, grouping, proportion.

      • Ordinal Data: Categorical data with a natural order or ranking.

        • Examples: Letter grades (A, B, C), Olympic medals, product ratings (on a scale).

        • Summary Methods: Counting, grouping, proportions, ranking.

    • Accounting Applications: Transaction_ID and AssetID are treated as categorical data.

  2. Numerical Data

    • Definition: Meaningful numbers, subject to mathematical operations.

    • Includes:

      • Interval Data where the difference between values is meaningful and consistent, but there is no absolute zero point, meaning zero does not signify the complete absence of the measured quantity.

        • Example: Temperature (0∘C0∘C does not mean no temperature).

        • Summary Methods: Counting, grouping, summing, averaging, finding ranges.

          •           

        • Ratio Data: Has a true zero point, allowing for meaningful ratios and comparisons of magnitude.

        • Examples: Transaction amount, net income, product ratings (1-5 stars average), quantity on hand, sales, earnings per share (EPS).

        • Accounting Applications: Used extensively in financial statements; EPS and other financial values are ratio data.

        • Summary Methods: Counting, grouping, summing, averaging, finding ranges.

Data Summarization Methods

  • Counting and grouping.

  • Proportion (e.g., return transactions / total transactions; division by total observations).

  • Summing and averaging.

  • Ranking (especially for ordinal data).

Data Properties & Accounting Applications (General)

  • Recognizing calculated vs. raw data fields.

  • Interpretation of net book value.