Management Information Systems Review

Flashcards covering key definitions and concepts from the Management Information Systems lecture notes, including data types, organizational dimensions, competitive strategies, data analytics, and database technologies.

Data

Raw facts.

Information

Data within context.

Knowledge

Understanding derived from information, allowing for predictions and conclusions.

Wisdom

Integration of knowledge, experience, and deep understanding to predict and prepare for situations.

Customer Moment of Value

A point in the customer journey where the customer experiences significant benefit or satisfaction from a product or service, defined by time, location, form, and perfect delivery.

Key Resources of Management Information Systems

Information, information technology, and people.

Knowledge Worker

A person whose main capital is knowledge and whose job involves creating, applying, and using knowledge, expertise, and critical thinking rather than performing manual labor.

Information-Literate Knowledge Worker

An individual who can define what information they need, know how and where to obtain it, understand it once received, and act appropriately to gain an advantage.

Information Flows

The movement of information within an organization in four directions: upward (staff to management), downward (management to employees), horizontal (between departments), and inward/outward (with external partners).

Information Granularity

The level of detail in data or information; high granularity means very detailed and specific data, while low granularity means summarized data.

Value Chain

Defines industry structure and relationships through five primary activities (inbound logistics, operations, outbound logistics, marketing and sales, after sales service) and three support activities (procurement, technology development, human resource management).

Porter's 5 Forces

A model analyzing industry competitiveness: rivalry among competitors, threat of new entrants, threat of substitutes, bargaining power of customers, and bargaining power of suppliers.

Cost Leadership Strategy

A competitive strategy focused on lowering operational costs with IT, passing savings to customers, or increasing costs for competitors.

Differentiation Strategy

A competitive strategy involving creating unique products/services, focusing on niche markets, and reducing competitors' differentiation advantages using IT.

Innovation Strategy

A competitive strategy using IT for new products, services, and radical process changes that transform industry standards.

Growth Strategy

A competitive strategy involving expanding production, global markets, new products, or integrating related services, often supported by IT.

Alliance Strategy

A competitive strategy focused on forming business networks, partnerships, joint ventures, and virtual companies facilitated by IT.

Value of Analytics

The ability to turn data into rapid insights that drive better decisions, optimize operations, innovate products, and build competitive advantages for companies.

Database

A collection of information that can be organized and accessed according to the logical structure of the information.

Data Warehouse

A logical collection of information gathered from many different operational databases that supports business analysis activities and decision-making tasks.

Data Mart

A data warehouse that is limited in scope.

OLTP (Online Transaction Processing)

A system used for gathering input, processing information, and updating existing information, typically for operational tasks and daily activities.

OLAP (Online Analytical Processing)

A system used for manipulating information to support decision-making and creating information and knowledge through analytical processing, typically for insights and strategic planning.

Logical Structure of Data

Information viewed as collections of characters, fields, records, files, databases, and data warehouses, representing how users perceive data organization.

Physical Structure of Data

The structure of information as it resides on storage media, representing its actual storage arrangement.

Character (Logical Structure)

The smallest logical unit of information, such as a letter, number, or symbol.

Field (Logical Structure)

A logical grouping of characters, representing a specific piece of information, like a name or address.

Record (Logical Structure)

A logical grouping of fields, representing a complete set of information about a single entity.

File (Logical Structure)

All logically associated records, forming a collection of similar data.

Relational Database Model

A database model that uses a series of two-dimensional tables (or files) to store information, where each table is structured with rows and columns (relations).

Data Warehousing Necessity

The requirement to separate operational and informational systems and data to improve performance and support business analysis effectively.

Operational System

A system used to run a business in real time, based on current data, also known as a system of record.

Informational System

A system designed to support decision making based on historical, point-in-time, and prediction data, often through complex queries and data mining.

Big Data

Extremely large data sets that, due to their volume, velocity, variety, and veracity, cannot be processed with traditional computing methods.

Volume (Big Data V)

The amount of data generated and stored, often measured in terabytes or petabytes.

Velocity (Big Data V)

The speed at which new data is created, collected, and processed, sometimes in real time, and how fast it must be analyzed.

Variety (Big Data V)

The different types and formats of data available, ranging from structured numeric data to unstructured text, video, and audio.

Veracity (Big Data V)

The quality, accuracy, and trustworthiness of the data, addressing its reliability and uncertainty due to inconsistency or errors.

Data Mining

The process of analyzing data from multiple perspectives to summarize it into useful information for decision-making and to find correlations or patterns in large databases.

Market Segmentation (Data Mining)

A use of data mining to identify groups of customers with similar buying behaviors.

Customer Churn Prediction (Data Mining)

A use of data mining to predict which customers may switch to competitors.

Fraud Detection (Data Mining)

A use of data mining to identify fraudulent banking transactions or other anomalous activities.

Direct Marketing (Data Mining)

A use of data mining to optimize mailing lists to target likely buyers, thereby reducing waste.

Interactive Marketing (Data Mining)

A use of data mining to personalize website content based on user interests.

Marketing-Based Analysis (Data Mining)

A use of data mining to find products commonly purchased together.

Trend Analysis (Data Mining)

A use of data mining to track changes in buying patterns over time.

Descriptive Modeling

A mathematical process that describes real-world events and the relationships between factors responsible for them, often used in customer segmentation.

Customer Segmentation (Descriptive Modeling)

A descriptive modeling technique that partitions a customer base into groups with various impacts on marketing and service.

Value-Based Segmentation (Descriptive Modeling)

A descriptive modeling technique that identifies and quantifies the value of a customer to the organization.

Behavior-Based Segmentation (Descriptive Modeling)

A descriptive modeling technique that analyzes customer product usage and purchasing patterns.

Needs-Based Segmentation (Descriptive Modeling)

A descriptive modeling technique that identifies ways to capitalize on motives that drive customer behavior.

Predictive Modeling

A process that uses data mining and probability to forecast outcomes, applied in areas like spam filters, fraud detection, and customer relationship management.

Prescriptive Modeling

The process of using data not only to predict what might happen but also to recommend the best course of action to achieve desired outcomes and reduce risk.

Business Intelligence (BI)

Applications and technologies that transform raw data into meaningful information, enabling businesses to make better, data-driven decisions and improve efficiency and strategy.

Structured Data

Data that is organized in a fixed format, such as rows and columns with labels, making it easily searchable and analyzable by computers.

Unstructured Data

Data that lacks a predefined format, including text documents, videos, and social media posts, making it harder to analyze but representing about 80% of all data.

ETL (Extract, Transform, Load)

The process of extracting raw data from multiple sources, transforming it into a standardized format for consistency, and loading it into a centralized data warehouse or data mart for analysis.

Hadoop

An infrastructure technology for storing and processing large datasets distributed across multiple servers using a cluster system, enabling flexible and scalable data handling.

Text Analytics (Text Mining)

The process of analyzing unstructured textual data (like social media posts, reviews, documents) to find patterns, sentiments, and gain insights.

Data Visualization

The graphical representation of data and analysis results using charts, graphs, and other visual elements to make complex information easier to understand.

Dashboard

An interactive interface displaying specific analyses in real time, allowing users to explore data, track metrics, and drill down into details without needing technical skills.

In-Memory Processing

A technology that uses RAM or solid-state memory instead of traditional hard drives for data storage and processing, enabling much faster data query and analysis speeds for BI applications.