Qualitative & Quantitative Data

• Qualitative data includes descriptions and characteristics that provide insights into a subject’s attributes, useful for categorical analysis and pattern recognition.
• Qualitative data helps understand vehicle design attributes and categorization in the automotive industry.

Nominal Data

• Categories or labels exist without inherent order such as types of vehicles (sedans, SUVs, trucks).
• Classifies products without implying ranking.

Ordinal Data

• Represents ordered categories without quantifiable differences.

• This data ranks vehicle models from economy to luxury based on customer preferences.

• Quantitative data, also known as numerical data, uses measurable quantities and includes values that can be expressed in numbers, facilitating objective measurement and statistical analysis.

Discrete Data

• Consists of distinct and separate values used for counting.
• Counting the number of vehicles produced or items in inventory uses discrete data.

Continuous Data

• Represents values within a range that can be measured with precision.
• Measurements of vehicle dimensions, engine sizes, or fuel capacities ensure product consistency and customer satisfaction.

Data Structure

• Data structure is the organization of data within a system. Determines how data is stored, accessed, and managed.

Structured Data

• Highly organized data is stored in a consistent format (databases or spreadsheets).
• Manages numerical values, dates, and other information in its inventory and financial systems.

Unstructured Data

• Lacks a specific format or organization (images, videos, social media content).
• Analyzes data for insights into customer sentiments and trends.

Semi-structured Data

• A mix of structured and unstructured data that includes elements like tags or labels which adds organization.
• Examples include XML files and JSON documents, which are used in various industries for flexible data integration and analysis.

Data Lifecycle

• Refers to the stages through which data passes from its collection/creation to disposal/archival.

Stage 1 - Collect

• Data is acquired or captured, marking the beginning of journey within the organization in the form of user input, sensor data, or database entries.
• Transaction details, customer preferences, and product information are collected when a customer makes a purchase, and is gathered from point-of-sale systems, website analytics, and customer feedback.

Stage 2 - Store

• Data can be stored various formats which depend on data volume, access requirements and data retention policies.
• Customer data is stored in its databases and customer relationship management system.

Stage 3 - Use

• Data is processed and analyzed to extract meaningful insights or support decision-making through viewing, processing, modifying, and saving information.
• Sales teams utilize customer data to personalize interactions and tailor marketing strategies and inventory managers leverage product data to optimize stock levels driving efficiency and enhancing customer experiences.

Stage 4 - Share

• Data is shared with internal and external stakeholders through reports or dashboards to foster synergy.
• Sales data is shared with the marketing team to align campaigns with customer trends.

Stage 5 - Archive

• Ensured that data is stored securely for the long term to meet legal or compliant reasons.
• Archived data may include previous sales reports, customer feedback, and market trends.

Stage 6 - Destroy

• Data needs to be securely deleted and disposed of to adhere to data protection regulations.

• Outdated or irrelevant data is securely and permanently deleted to ensure compliance with privacy standards and reduces the risk associated with retaining unnecessary data.

• A file-oriented system consists of sets of logically related records, such as employee payroll records.

• A database is a collection of interrelated, centrally coordinated data to store data with minimal redundancy.

• Databases were developed to resolve issues from traditional file-oriented systems, to prevent data redundancy and inefficiency.

Database Management Systems

• A database is a structured collection of electronically stored data to enhance the efficiency of data retrieval, management, and storage.
• A database management system (DBMS) acts as an interface between the database and the application programs that use it, streamlining data organization and accessibility.

Hierarchy of Data Relationships

• The hierarchical database model organizes data into a tree-like structure, beginning with the smallest unit and expanding into a complete database to facilitate structured navigation and querying.

Character

• Fundamental building block which may be a letter, number, or symbol.
• Every letter in a customer's name is considered a separate character.

Field

• Consists of a group of characters that represent a specific piece of information.
• Customer number or customer name each are clearly defined and contains specific data relevant to its descriptor.

Record

• A collection of related fields that aggregates all data pertaining to a single entity within the database.
• A customer record such compilation of fields like customer number, name, address, and phone number, to see a view of that customer's information.

Table

• Consists of multiple records, each containing the same type of entity.
• Serves as a collection point for records of similar structure, to facilitate storage and retrieval.

Database

• A database encompasses a set of interrelated tables.
• Each table contributes a different dimension of data to support data management tasks.

Relational Databases

• Relational databases facilitate access to data that is interconnected through predefined relationships.
• Organizations store data only once, create complex queries, enhance security and data access, and offer natural scalability and extensibility.
• Data is organized into tables of rows made up of columns, which are defined as fields or attributes, that describes a characteristic of an entity being described.
• A table called ‘vendor’ includes attributes such as the vendor number, vendor name, address, city, province, and postal code.
• Each row represents a record and contains data about a specific case of the entity.
• A row in the vendor table holds information for vendor, while rows in the inventory table details inventory items.
• A primary key is a unifier for each how in a table.
• A foreign key is a attribute in one table that is linked to the primary key of another table.

Database Management System (DBMS) Languages

• Specialized languages perform a variety of functions to maintain the structure, and security of database systems.

Data Definition Language (DDL)

• Builds data dictionaries, initializes databases, describes logical views, and specifies security constraints.

Data Manipulation Language (DML)

• Updates, inserts, and deletes portions of databases.

Data Query Language (DQL)

• Used for querying the database and retrieving data.

Data Control Language (DCL) and Transaction Control Language (TCL)

• Manage access rights and transaction processing, ensuring database integrity and security.

Advantages of Databases

• Database technology includes data integration, data sharing, reporting flexibility, minimal data redundancy, data independence, central management of data, and cross-functional analysis.

Data integration

• Achieved by combining files into larger “pools” of data that many application programs can access.
• Consolidates data formerly contained in payroll, personnel, and job skills master files which makes it easier for information to be combined in unlimited ways.

Data sharing

• Integrating data makes it easier to share data with all authorized users.

Reporting flexibility

• Reports are revised easily and generated as needed and the database can be easily browsed.

Minimal data redundancy and data inconsistencies

• Minimize data redundancy and inconveniences because data items are usually stored only once.

Data independence

• Programs are independent from one another allowing each to be changed without affecting or changing each other.

Central management of data

• Data management is more efficient because a database administrator manages the data.

Cross-functional analysis

• Relationships, such as the association between selling costs and promotional campaigns, can be explicitly defined and used.

Other Databases

• Beyond traditional relational databases, there are Object-Oriented Databases and NoSQL Databases

Object-Oriented Databases

• Combines database capabilities with object-oriented programming principles, storing data in objects rather than tables.

NoSQL Databases

• These databases excel in handling large volumes of diverse and rapidly changing data, making them suitable for big data and real-time web applications.

• Entity-Relationship Diagrams (ERDs) are a tool used in database modeling to visually represent the structure of database systems.

Entities

• Objects or concepts that can have data stored.
• An entity is depicted as a rectangle in an ERD with a name that defines its role within the database.

Attributes

• Properties or characteristics of an entity represented by ovals connected to their respective entities by a line.
• Student ID, Name, and Date of Birth can be attributes for a Student.

Relationships

• How two entities share information in the database depicted by a diamond-shaped box connected to the involved entities with lines.
• A relationship named "Enrolls" connects the "Student" entity with the "Course" entity, indicating that students enroll in courses.

Cardinality

• The attributes of the relationship between entities.

• Describes how many instances of one entity can be associated with one instance of another entity.

• One-to-One each instance of one entity is associated with one instance of another entity. -One social security number per person.

• One-to-Many one instance of an entity can be associated with many instances of another entity.

• One teacher can teach many students.

• Many-to-Many many instances of one entity can be associated with many instances of another entity. -Students can enroll in multiple courses, and each course can have multiple students.

Participation Constraints

• The rules about the participation of an entity in a relationship which specifies optional or mandatory participation.

• Participation is mandatory for the Instructor but optional for the Course in a "teaches" relationship between "Instructor" and "Course".

• ERDs help database designers understand the database structure, making it easier to create, modify, and maintain database systems.

• Facilitate communication between various stakeholders to ensure that all requirements are met and understood.

Crow's Foot Notation

• A widely used method for depicting Entity-Relationship Diagrams (ERDs).
• Uses graphical symbols to represent entities, attributes, and their relationships.

Entities

• Represented by rectangles that are labeled each labeled with the entity name (e.g. "Customer" or "Order").

Attributes

• Shown as ovals connected to their entity rectangle by a line.
• Primary keys, which uniquely identify a record in an entity, are often underlined.

Relationships

• How entities are related to each other, represented by lines connecting entities.
• The nature of the relationship is described by the symbols at either end of the line.

Crow's Foot

• Indicates the "many" side of a relationship.
• If one entity can be associated with many entities on the other side of the relationship, you'll see this symbol.

Line without Crow's Foot

• Represents the "one" part of the relationship.

Optional vs. Mandatory

• The presence of a circle or a perpendicular bar on a line indicates whether participation in the relationship is optional or mandatory.

Circle (O)

• An entity does not need to have a corresponding entity in a relationship.

Bar (|)

• A bar indicates a mandatory relationship where an entity must have a corresponding entity.

Double Lines

• Represent total participation, indicating that all entities in the set are involved in the relationship.

• Crow's Foot Notation is especially helpful in relaying complex relationships between data entities, and ensures that data base development and use are accurate.

Qualitative data involves descriptions and characteristics for categorical analysis. Quantitative (numerical) data involves measurable quantities for statistical analysis. This includes nominal, ordinal, discrete and continuous data.