ACCT 3130 - Chapter 3: Performing the Test Plan and Analyzing the Results

What are the four main categories of data analytics?

1. Descriptive Analytics

2. Diagnostic Analytics

3. Predictive Analytics

4. Prescriptive Analytics

Descriptive Analytics

Are procedures that summarize existing data to determine what has happened in the past

Diagnostic Analytics

Are procedures that explore the current data to determine why something has happened the way it has, typically comparing the data to a benchmark

Predictive Analytics

Are procedures used to generate a model that can be used to determine that is likely to happen in the future

Prescriptive Analytics

Are procedures that model data to enable recommendations for what should be done in the future

From lowest of highest of value and difficulty?

• Descriptive Analytics

• Diagnostic analytics

• Predictive Analytics

• Prescriptive Analytics

Descriptive analytics examples:

• Summary statistics

• Data reduction or filtering

Summary Statistics

Describe a set of data in terms of their location (mean, median), range (standard deviation, min, max), shape (quartile), and size (count)

Data Reduction or Filterting

Is used to reduce the amount of observations to focus on relevant items (that is, highest cost, highest risk, largest impact, etc.)

How is data reduction/filtering done?

It does this by taking a large set of data (perhaps the population) and reducing it to a smaller set that has the vast majority of the critical information of the larger set

Diagnostic Analytics examples:

• Profiling

• Clustering

• Similarity Matching

• Co-occurence grouping

Profiling

Identifies the “typical” behavior of an individual, group, or population by compiling summary statistics about the data (including mean, standard deviations, etc.) and comparing individuals to the population

• used to discover patterns of behavior

Clustering

Helps identify groups (or clusters) of individuals (such as customers) that share common underlying characteristics - identifying groups of similar data elements and the underlying drivers of those groups

Similarity Matching

Is a grouping technique used to identify similar individuals based on data known about them

Co-occurence grouping

Discovers associations between individuals based on common events, such as transactions they are involved in

Predictive analytics example:

• Regression

• Classification

• Link Prediction

Regression

Estimates or predicts the numerical value of a dependent variable based on the slope and interest of a line and the value of an independent variable

Classification

Predicts a class or category for a new observation based on the manual identification of classes from previous observations

Link Prediction

Predicts a relationship between two data items, such as members of a social media platform

Prescriptive analytics examples:

• Decision support systems

• Machine Learning and Artificial Intelligence

Decision Support Systems

Are rule-based systems that gather data and recommend actions based on the input

Machine Learning and Artificial Intelligence

Are learning models or intelligent agents that adapt to new external data to recommend a course of action

Summary Statistics

Describe the location, spread, shape, and dependence of a set of observations

Data reduction involves the following steps:

• Identify the attribute you would like to reduce on or focus on

• Filter the results

• Interpret the results

• Follow up on results

Fuzzy Matching

Locates approximate matches

• Useful for identifying relationships in imperfect data

What can diagnostic analytics provide?

Insight into why things happened or how individual data values relate to the general population

What type of data is profiling primarily using?

Structured data - data that are stored in a database or spreadsheet and are readily searchable

Profiling relies on gathering summary statistics and identifying outliers:

• Identify the objects or activity you want to profile

• Determine the types of profiling you want to perform

• Set boundaries or threshold for the activity

• Interpret the results and monitor the activity and/or generate a list of exceptions

• Follow up on exceptions

What can help show spread and outliers?

Z-scores and box plots

What is an example of data profiling?

Variance Analysis

Benford’s law

Is a diagnostic analytics that compares actual to expected values

What does regression help with?

helps predict expected outcomes

Classification: Training data

Are existing data that have been manually evaluated and assigned a class

Classification: Test Data

Are existing data used to evaluate the modelClassification:

Classification: Decision Trees

Are used to divide data into similar groups

Classification: Decision Boundaries

Mark the split between one class and another

Classification: Pruning

Removes branches from a decision tree to avoid overfitting the model

Classification: Linear Classifiers

Are useful for ranking items rather than simply predicting class probability

• Useful for determining the important values

Classification: Support Vector Machine

Is a discriminating classifier that is define by a separating hyperplane that work first to find the widest margin and then works to find the middle line

How do we evaluate classifiers?

• Try to avoid overfitting, or model that are too accurate. They are bad at predicting a future observation

• Look for the sweet spot where we maximize the accuracy of the testing data

What do we do once other diagnostics and predictive analyses have been performed?

The decision process can be aided by rules-based decision support systems, machine learning models, or added to an existing artificial intelligence model to improve future predictions