Chapter 12 Business Analytics IS-300

The Manager’s Job and Decision Making

Mintzberg (1973) labelled managerial functions into three basic roles

1. interpersonal: leader, liaison, figurehead

2. informational: monitor, disseminator, spokesperson

3. decisional: entrepreneur, disturbance handler, resource allocator, negotiator 

   

The process and phases in decision making

• intelligence phase: what is the problem

• design phrase: what are my options

• choice phrase: pick an option and decide how to implement it 

  

Why managers need IT support

• too many alternatives to evaluate manually

• time pressure for decisions

• uncertainty and rapid change in environment

• need for remote access, expert consultation, and group decision-making

Decision Framework

• Structured: routine, repetitive (e.g., payroll)

• Semi-structured: mix of standard & judgment (e.g., loan approval)

• Unstructured: Novel, complex (e.g,. entering new markets)

Nature of Decisions - Three broad categories of managerial decisions

1. Operational Control: executing specific tasks efficiently and effectively

2. Management Control: acquiring and use resources efficiently in accomplishing organizational goals

3. Strategic Planning: the long-range goal and policies for growth and resource allocation

Business Analytics

the process of developing actionable decisions or recommendations for actions based on insights generated from historical data

4 Types of Data Analytics

1. Descriptive Analytics: What’s happening in my business

2. Diagnostic Analytics: Why is it happening

3. Predictive: What’s likely to happen

4. Prescriptive: What do I need to do

   

Descriptive Analytics

def: analyzes past data, present in dashboards/reports to identify patterns and anomalies

tools: OLAP, DSS, Data Mining

Example: 

• Coca-cola: uses sales data to refine marketing

• Amex analyzes transaction data for customer segmentation

• Cardlytics: tracks consumer buying behaviors

Predictive Analytics

def: forecasts future outcomes using patters, trends, and statistical models

tools: data mining, variety of statistical procedures such as regression, multiple regression, and logistic regression

example:

• COVID-19: Predicted infection trends and healthcare needs

• Amazon: Product recommendations

• Netflix: Content suggestions

• UPS: Optimized delivery routes

Case Study: Target

• predicted teen pregnancy based on purchase behavior —> marketing opportunities 

Prescriptive Analytics

def: recommends actions and shows likely outcomes.

tools: statistical procedures—optimization, simulation, decision trees

example:

• P&G: Optimized inventory & supply chains

• IBM Watson: Personalized healthcare treatments

• AirBnB: pricing strategies for rentals

• Google: self-driving car decision-making

Descriptive Analytics Vs. Predictive Analytics Vs. Prescriptive Analytics

	Descriptive Analysis	Predictive Analysis	Prescriptive Analysis
Summary	What happened?	What’s going to happen?	What should happen?
Function	It uses data mining and data aggregation to discover historical data.	It looks at historical data and analyzes past data trends to predict what could happen.	It takes the conclusions gleaned from descriptive and predictive analysis and recommends the best future course of action.
Pros	It’s easy to employ in daily operations. Little experience is needed.	It’s a valuable forecasting tool.	It offers critical insights into making the best, most informed decisions.
Cons	It offers a limited view and doesn't go beyond the data’s surface.	It needs lots of historical data to work. It will never be 100% accurate.	It requires a lot of past data and often cannot account for all possible variables.

Decision Support Systems (DSS)

• Sensitivity Analysis: Effect of change in inputs

• What-If Analysis: Predicts impacts of hypothetical scenarios

• Goal-seeking Analysis: works backward from a desired output 

Data Mining

• def: process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learnings, statistics, and database management systems

• 2 basic operations:

  • identifying previously unknown patterns (descriptive analytics)

  • predicting trends and behaviors (predictive analytics )

Data Mining techniques

1. Associate Rule Learning (if a customer buys bread, they are 80% likely to also buy butter)

2. Classification: assigns data to categories (e.g. spam folder)

3. Clustering: groups similar data points (e.g. customer segmentation)

4. Regression: predicts numeric values

5. Anomaly Detection: identifies unusual behavior (e.g fraud detection)

6. Sequential Patterns: discovers recurring events

7. Prediction

8. Decision trees

9. Decision rules

10. Artificial Neural Networks: detect complex patterns

Direct Marketing

• can benefit from several data mining techniques: including not limited to: cluster analysis, regression analysis, classification, and decision trees

Customer Churn

• identifies which customers may be leaving you

• can learn why and/or try to retain them by

• artificial neural networks & decision trees techniques can help companies identify customers who are likely to churn then take proactive action to retain them 

Data Mining Deviation Analysis

• DM techniques can help identify fraudulent transactions by studying credit cards or various behaviors as a previous transaction history data set

Neural Networks

• utilize geo-spatiality to provide immediate information on crimes to enhance law enforcement decision making

• can predict specific types of crime using location and time information and predict a crime’s location when given the crime and time of day 

The Capabilities of Dashboards

• Drill down

• CSF’s

• KPI’s

• Status access

• Trend analysis

• Exception reporting

Geographic Information System (GIS)

def: a computer-based system for capturing, integrating, manipulating, and displaying data using digitized maps. Its most distinguishing characteristic is that every record or digital object has an identified geographical location

ex: Children’s National Health Center: enabled clinic to identify hotspots where burn injuries were occurring on a Map, ex, the Hispanic community, a prevention program could be developed and tailored to reduce the risk