Learning Objectives
By the end of this chapter, you will be able to:
- Differentiate between structured, semi-structured, and unstructured decisions.
- Identify the three phases of the decision-making process.
- Explain how business analytics can support each phase of the decision-making process.
The Nature of Managerial Decision Making
Decision-making is a fundamental and essential part of a manager’s job. Managers at all levels, from frontline supervisors to senior executives, are responsible for making decisions that affect the organization. Business analytics provides the tools and insights to make these decisions more effective and data-driven.
Figure 1: Managers and Decision Making
Types of Decisions
Managerial decisions can be categorized based on their structure:
flowchart TB
subgraph TYPES["Types of Decisions"]
direction LR
STRUCT["⚙️ Structured\nRoutine, Rule-based\nCan be automated"]
SEMI["📊 Semi-structured\nData + Judgment\nPartially automated"]
UNSTRUCT["🧠 Unstructured\nComplex, Novel\nRequires insight"]
end
STRUCT --> LOW["Lower Management"]
SEMI --> MID["Middle Management"]
UNSTRUCT --> TOP["Senior Executives"]
SEMI --> BA["📊 Business Analytics\nMost Valuable Here"]
UNSTRUCT --> BA
style BA fill:#2e7d32,color:#fff
Figure 2: Decision Types and Analytics Value
- Structured Decisions
These are repetitive, routine decisions for which a clear procedure and a set of rules have been established. Because they are well-defined, they can often be automated. These decisions are typically made by lower-level managers.
- Example: Deciding whether to grant overtime pay to an employee. The rules are clear: if an employee works more than 40 hours a week, they get overtime. A system can easily handle this.
- Semi-structured Decisions
These are decisions where only part of the problem has a clear-cut answer provided by an accepted procedure. They require a combination of data-driven analysis and human judgment.
- Example: Deciding on a marketing budget for a new product. Part of the decision can be based on data from past campaigns, but it also requires judgment about the market conditions and the product’s potential.
- Unstructured Decisions
These are non-routine, complex decisions that require a great deal of judgment, evaluation, and insight. There is no agreed-upon procedure for making the decision, and the problem is often novel and not well understood. These decisions are typically made by senior executives.
- Example: Deciding whether to enter a new market, acquire another company, or invest in a new, unproven technology.
Business analytics is most valuable for semi-structured and unstructured decisions, where it can provide the data and insights needed to support a manager’s judgment.
The Decision-Making Process
Nobel laureate Herbert Simon described a classic model of the decision-making process that consists of three main phases:
flowchart LR
INT["🔍 Intelligence\nIdentify Problem"]
DES["⚙️ Design\nDevelop Alternatives"]
CHO["✅ Choice\nSelect Solution"]
IMP["🚀 Implementation\nPut into Action"]
INT -->|"Descriptive\nAnalytics"| DES
DES -->|"Predictive\nAnalytics"| CHO
CHO -->|"Prescriptive\nAnalytics"| IMP
IMP -.->|"Monitor & Feedback"| INT
style INT fill:#1565c0,color:#fff
style DES fill:#6a1b9a,color:#fff
style CHO fill:#2e7d32,color:#fff
Figure 3: The Decision-Making Process with Analytics Support
- Intelligence Phase
In this first phase, the decision-maker identifies and defines the problem or opportunity. It involves searching the internal and external environment to understand what needs to be decided.
- How Analytics Helps: Business analytics can be used to scan large amounts of data to identify trends, patterns, or deviations that signal a problem or an opportunity (Descriptive Analytics).
- Design Phase
Once the problem is defined, the decision-maker develops and analyzes possible alternative courses of action. This involves brainstorming solutions and then evaluating their feasibility and potential consequences.
- How Analytics Helps: Analytics can be used to model the potential outcomes of different alternatives (“what-if” analysis), allowing the decision-maker to compare them on a quantitative basis (Predictive Analytics).
- Choice Phase
In the final phase, the decision-maker selects one of the alternatives. This is the act of making the decision.
- How Analytics Helps: Analytics can provide reports, dashboards, and visualizations that help the decision-maker understand the trade-offs between different choices and select the one that best aligns with the organization’s goals (Prescriptive Analytics).
Some models also include a fourth phase, Implementation, where the chosen solution is put into action and its performance is monitored. This monitoring then feeds back into the intelligence phase, creating a continuous loop.
Summary
Managerial decision-making varies in structure, from routine structured decisions to complex unstructured ones. Business analytics is most powerful when applied to semi-structured and unstructured problems. The classic decision-making model involves three phases: Intelligence (defining the problem), Design (finding alternatives), and Choice (selecting an alternative). Analytics provides crucial support at each phase, from identifying problems with descriptive data to modeling outcomes with predictive tools and guiding selections with prescriptive insights.
Key Takeaways
- Decisions can be structured, semi-structured, or unstructured.
- Analytics is most valuable for semi-structured and unstructured decisions.
- The decision-making process consists of Intelligence, Design, and Choice phases.
- Different types of analytics can support each phase of the process.
Discussion Questions
- Provide an example of a semi-structured decision that a university administrator might have to make.
- How can a manager use analytics in the “Intelligence” phase to identify a new business opportunity?
- Why is human judgment still important in the “Choice” phase, even with the support of analytics?
