Chapter 12: Improving Decision Making and Managing Knowledge

Structured, Semistructured, and Unstructured

Types of Decisions

Structured Decisions

Repetitive, routine-based decisions that involve definite procedure for handling them so they do not have to be treated as new.

Structured Decisions Management Level

Operations Management

Course Grades, Inventory Reordering, Payroll

Examples of Structured Decisions

Semistructured Decisions

Decisions made when only part of the problem has clear-cut answers

Semistructured Decisions Management Level

Middle Management

Department Budget Planning, Hiring, Crisis Management

Examples of Semistructured Decisions

Unstructured Decisions

Novel, important decisions requiring good judgment and intelligent inputs

Unstructured Decisions Management Level

Senior Management

University Annual Budget, Market Entry/Exit, Long-Term Goals

Unstructured Decisions Examples

Data from Business Environment, Business Intelligence Infrastructure, Business Analytics Toolset, Managerial Users and Methods, Delivery Platform, and User Interface

6 Elements of Business Intelligence Environment

Data from Business Environment

Data that influences a company’s operations and performance

Data from Business Environment Examples

Call Centers, Web Data, Mobile Devices, Social Media Data, Stores, Suppliers, Governmental and Economic Data

Business Intelligence Infrastructure

Cleans and maintains data

Business Intelligence Infrastructure Examples

Databases, Data Warehouses, Data Marts, Analytic Plaforms

Business Analytics Toolset

Further interpretation of data

Business Analytics Toolset Examples

Models, Data Mining, OLAP, Reporting and Query Tools, Big Data Analytics

Managerial Users and Methods

Decide what tools to use to best answer questions and support decisions

Managerial Users and Methods Examples

Business Strategy, Performance Management, Balanced Score Card

Delivery Platform

Shares analytical results

Delivery Platform Examples

MSS, DSS, ESS

User Interface

Presents the information

User Interface Examples

Reports, Dashboards, Scorecards, Mobile, Web Portal, Social Media 

Business Intelligence (BI)

The technologies, applications, and practices for collecting, analyzing, and presenting business data.

Focuses on what has happened in the past and present; helps organizations understand their current performance and operations; descriptive

Business Analytics (BA)

Using statistical analysis and predictive modeling to turn raw data into actionable insights.

Focuses on what is likely to happen in the future; informs strategic decisions; predictive and prescriptive

Production Reports

Summarize the performance and progress of a manufacturing process, capturing key metrics to help managers optimize production efficiency.

Parameterized Reports

Dynamic reports that allow users to interact with data based on input parameters; enables users to filter, group, or modify the data presented, providing flexibility and customization.

Dashboards

Digital interfaces that consolidate critical data, statistics, and insights onto a single display; utilize graphs, charts, and other visual elements to present information pertinent to various business operations.

Balanced Scorecard

A management system aimed at translating an organization’s strategic goals into a set of organizational performance objectives, that, in turn, are measured, monitored, and changed (if necessary) to ensure that they are met

Financial Analysis, Customer Analysis, Internal Analysis, Learning and Growth

Four Perspectives of Balanced Scorecards

Ad-Hoc Query

User-defined search that is used to gain insight to a given data set without requiring any predefined dashboards, drill paths, or coding; unplanned and dynamic requests for information

Drill-Down

An interactive BI and BA functionality that enables users to explore datasets at various levels of detail; lets users navigate through layers of data to access more detailed information

Sensitivity Analysis

A technique used to determine how different values of an independent variable affect a particular dependent variable within a model; involves systematically changing input factors withing a model to observe the resulting change in output

Descriptive Analysis

Uses data aggregation, summarization, visualization, trend analysis, and segmentation to summarize, organize, and visualize data for easy interpretation

Predictive Analysis

Uses statistical algorithms, machine learning, and historical data to forecast future outcomes

Prescriptive Analysis

Combines data analytics with technology to enhance business decision-making; leverages algorithms, machine learning, and optimization technique; best suited for short-term solutions (reliability decreases with longer time frames)