Accounting Analytics Vocabulary

Comprehensive vocabulary flashcards generated directly from the Accounting Analytics lecture transcript covering Chapters 1 through 8.

Data Analytics

The process of evaluating data with the purpose of drawing conclusions to address business questions.

Unstructured Data

Data that is not systematically organized.

Big Data

Datasets which are too large and complex to be analyzed traditionally.

Velocity

One of the 3 V's of Big Data referring to frequency.

Volume

One of the 3 V's of Big Data referring to size.

Variety

One of the 3 V's of Big Data referring to different types of data.

Artificial Intelligence (AI)

The practice of using machines to mimic human intelligence to perform tasks.

Generative AI

AI that relies on algorithms to create new content such as text, images, audio, and video.

Prompt Engineering

The process of guiding AI to generate the most relevant and high-quality output.

Primary Key

Unique identifiers in a database table.

Foreign Key

Attributes in a database table that point to a primary key in another table.

Composite Keys

Primary keys that are combinations of two or more fields.

Descriptive Attributes

Non-key attributes in a database that include everything else besides keys.

Descriptive Analytics

Procedures that summarize existing data.

Diagnostic Analytics

Procedures that compare the data to the benchmark.

Predictive Analytics

Procedures used to generate a model that can be used to determine what has happened in the past and what is likely to happen in the future.

Prescriptive Analytics

Procedures that model data to enable recommendations for the future.

Unsupervised Approach

Data analytics approach used when you do not have a specific question.

Supervised Approach

Data analytics approach used when you are trying to predict future outcomes based on historic data.

Summary Statistics

Statistics that describe a set of data in terms of their location, range, shape, and size.

Data Reduction

Analytics technique used to reduce the amount of observations to focus on relevant items by taking a large set of data and reducing it to a smaller set containing critical information.

Profiling

An analytics procedure that relies on gathering summary statistics and identifying outliers.

Clustering

An analytics approach used to identify groups of similar data elements and the underlying drivers of those groups.

Target (dependent variable)

An expected attribute or value in a data model that we want to evaluate.

Class

A manually assigned category applied to a record based on an event.

Simple Regression Linear Model

Linear regression model represented by $Y = B_0 + B_1X + E$, where $Y$ is the dependent variable, $B_0$ is the intercept, $B_1$ is the slope coefficient, $X$ is the independent variable, and $E$ is the error term.

Multiple Regression

A regression model with two or more independent (predictor) variables that attempts to plot a two-dimensional plane instead of a one-dimensional line.

Training Data

Existing data that have been manually evaluated and assigned a class.

Test Data

Existing data used to evaluate a data model.

Decision Trees

Models used to divide data into smaller groups.

Decision Boundaries

Lines that mark the split between one class and another.

Pruning

The process of removing branches from a decision tree to avoid overfitting the model.

Linear Classifiers

Classifiers useful for ranking items rather than simply predicting class probability.

Support Vector Machine

A discriminating classifier defined by a separating hyperplane that works first to find the widest margin and then works to find the middle line.

Machine Learning

A process where systems learn from past data to better predict outcomes.

Qualitative Data

Categorical data which includes count, group, or rank.

Nominal Data

Simple qualitative data with no apparent ranking, such as hair color or state name.

Ordinal Data

Qualitative data that can be ranked, such as Gold, Silver, and Bronze.

Proportion

A qualitative metric showing the makeup of each category, such as $55\%$ cats and $45\%$ dogs.

Quantitative Data

Numerical data such as height or dollar amounts.

Ratio Data

Quantitative data that defines $0$ as the absence of a feature, such as cash.

Interval Data

Quantitative data where $0$ is just another number, such as temperature.

Discrete Data

Quantitative data represented only by whole numbers, such as points in a basketball game.

Continuous Data

Quantitative data showing numbers with decimals, such as height.

Declarative Visualization

Visualizations used to present findings, such as financial results.

Exploratory Visualization

Visualizations used to gain insights while interacting with data, such as identifying good customers.

Z-Scores

A diagnostic metric used to identify outliers by calculating standard distance from the mean.

Benford's Law

A principle stating that in real-world amounts, small first digits are more common.

Exact Matching

A matching procedure that joins tables and confirms records belong together.

Fuzzy Matching

A matching procedure that joins tables and catches records that are likely related.

Sequence Checks

An audit procedure used for locating gaps or duplicate transactions in pre-numbered documents.

Variance Analysis

An analysis procedure where managers compare actual results to determine whether a variance is favorable or unfavorable.

Key Performance Indicators (KPIs)

Specific type of performance metric used to measure performance at a company.

Digital Dashboard

Interactive report showing important metrics to help users understand how a company or organization is performing.

Ratio Analysis

A descriptive analytic used to evaluate relationships among different financial statement items to assess the financial health of a business.

Text Mining

Refers to a set of methods that convert text data to meaningful numeric metrics.

XBRL

A standard that uses tags to allow data to be quickly transmitted and received, serving as input for analytics models.