SECTION 5: Data and Database Fundamentals (13%)

Value of Data, Database overview, backups

Data

• These are raw facts and figures

Information

• These are processed data with meaning

Critical Data

• A type of data that is critical for operations

• Ex: Customer data, Financial data, Operational data

Non-Critical Data

• A type of data that is nice to have but non-essential

• Ex: Marketing preferences, Archived emails

Data Capture and Collection

• The process of gathering data from various sources

• Data can be collected from online forms/surveys, point-of-scale systems, website analytics and tracking

• Customer interactions and transactions

• Sensors, surveys, and observations

Data Correlation

• The process of finding relationships between data sets, identifying patterns and connections, reveal insights not obvious individually

Meaningful Reporting

• The process of converting data into actionable insights

• Visual presentations and dashboards

• Tailored to audience needs

Direct Data Monetization

• A type of data monetization that involves selling raw data to third parties, like advertisers or market researchers

• Ex: Social Media platforms sell user data to advertisers aiming to target specific demographics

Indirect Data Monetization

• A type of data monetization where instead of selling data, companies use it to enhance internal processes or develop new offerings

• Ex: E-commerce sites analyze customer purchase data to suggest products, which helps boost sales

Data Analytics

• This is the process of examining data for insights, statistical analysis and pattern recognition, and transform raw data into knowledge

Descriptive Analytics

• A type of data analytics that summarizes historical data to spot trends and patterns

• Ex: Understanding past sales performance/analyzing customer behavior

Diagnostic Analytics

• A type of data analytics that investigates underlying causes of trends and patterns

• Ex: Identifying reasons for a drop in sales during a specific period

Predictive Analytics

• A type of data analytics that forecasts future outcomes based on historical data

• Ex: An online retailer predicting future sales using seasonal trends

Prescriptive Analytics

• A type of data analytics where it offers actionable recommendations for optimizing outcomes

• Ex: Recommending a targeted marketing strategy to boost sales

Big Data

• These are extremely large and complex data sets

• Traditional tools cannot process these effectively and requires specialized technologies

Volume

(3 Types of Vs of Big Data)

• In Big data: Massive amounts of data - petabytes/exabytes

• Ex: Facebook generates vast amounts of data every second

Velocity

(3 Types of Vs of Big Data)

• In Big Data: Rapid speed of data generation and processing

• Ex: Data from sensors on self-driving cars or financial transactions

Variety

(3 Types of Vs of Big Data)

• In Big Data: Diverse types of data. from structured (databases) to unstructured (emails, videos, social media posts)

Database

• Organized collection of structured data

• enables efficient storage and retrieval

• foundation of modern information systems

Creating a Database

• This is when you define the database structure with tables, fields, and relationships

• Add new records, input fresh data from various sources, and expand database with new information

• Ex: For customer management system - create tables for customers, orders, and products with fields like “Customer Name”, “Order Date”, etc.

Importing/Inputting Data in Database

• This is when you bring data in to the database from external sources such as spreadsheets, text files, and other databases

• Bulk data entry and migration

• Ex: Import Customer data from a CRM to ensure all departments access the same information

Querying a Database

• This is when you search and retrieve data in a database

• Filter records based on criteria

• Answer questions based on criteria

• Ex: Query to find customers who purchased last month or identify top-selling products in a specific region

Generating Reports

• This is when you produce formatted presentation of data to summarize and analyze information

• Professional output for stakeholders

• Ex: Sales report showing monthly revenue, helping management track business performance over time

Flat File Storage

• A type of data storage structure that is:

• Plaintext storage, often in CSV format, with data separated by delimiters like commas or tabs

• Generally limited to single-use access

• Difficult to expand, performance declines as data grows making it cumbersome

• Slower for large datasets

• Simple data types only

Database Storage

• A type of data storage structure that is:

• Organized in multiple tables with defined relationship indexes, and advanced management features, managed by systems like MySQL or Oracle database

• Many users can access safely; handle user coordination; built for multi-user access

• Highly scalable, designed to grow and handle increasing data volume

• Optimized for fast retrieval; uses advanced indexing and optimization

• Complex relationships supported and can handle multiple data formats

Storage

• How and where data is physically kept

• Affects performance and accessibility, critical for data management strategy

Database Persistence in Databases

• Database data survives power loss unlike RAM

• Essential for business records and transactions

• Why your bank account survives system restarts

Data Availability

• It is when and how data can be accessed

• Affects business operations

• Balance between access and security

• Cloud or Local Storage

• Online or Offline Access

Structured Data

• Are highly organized data in predefined format, have a fixed schema with specific fields, easy to use operations such as querying, manipulation and analysis

• Every datapoint follows a specific, consistent format

• Requires planning before implementation

• Ex: spreadsheets, relational databases

• Ex: Customer Records = name, age, address fields in tables

Semi-Structured Data

• These data doesn’t follow a strict table format but has some organization

• More flexible than structured data - uses tags or keys to provide structure

• Easier to modify than structured data

• Self-describing format

• Ex: XML, JSON files

Non-Structured Data

• Data that has no predefined organizational format

• Free-form content without fixed schema

• The most flexible data format but difficult to search automatically

• Requires special tools for analysis

Relational Database

• This is how data is organized in related tables with ROWS and COLUMNS

• structured approach with fixed schema

• Most common database type

Schema

• In a relational database, this is the blueprint defining database structure, specifies tables, fields and relationships

• Determines the types of data that can be stored or accessed

• Must be designed before data entry

• Ensures data consistency, enforces business rules, and provides clear data organization

Tables

• In a relational database, these are collections of related records and acts as the core structure

• Are organized in rows and columns

• each table represents an entity type

Rows/Records

• In a relational database, these are individual entries in a table

• Each entry represents a single entity instance

• Ex: In a “Customers” table, each row is an individual customer

Columns/Fields

• In a relational database, these are individual data elements

• Each column has a specific data type

• Consistent across all rows

• Ex: “Customers” table have the columns: Name, Age, Phone Number

Primary Key

• In a relational database, it is the unique identifier for each record in a table

• cannot be null or duplicate

• ensures record uniqueness

• Ex: Customer ID as primary key for “Customers” table

Foreign Key

• In a relational database, these are links to a primary key in another table

• This creates relationships between tables

• Maintains referential integrity, prevents orphaned records, enforces data relationships, enable table joins

• Ex: An “Order” table may have a foreign key to the “Customers” table to identify the customer who placed each order

Constraints

• In a relational database, these are rules that limit allowable data

• Ensures data quality and integrity

• Ex: NOT NULL ensures columns cannot have empty values, UNIQUE ensures all values in a column are not duplicate, CHECK validates that values in a column meet a specified condition, and FOREIGN KEY establishes relationship between tables

Non-Relational Databases

• These databases doesn’t require a fixed schema

• Handle diverse data types efficiently

• Known for flexibility and adaptability in managing data that doesn’t fit neatly into tables

Key/Value Database

• A non-relational database that has a simple structure with key-value pairs

• fast retrieval using unique keys

• limited query capabilities

• Ex: Redis, Amazon, DynamoDB

Document Database

• A non-relational database that stores data as documents, commonly in JSON or BSON

• Flexible schema within documents

• Natural fit for application objects, easy to scale horizontally

• Ex: MongoDB

Data Backup

• This is the process of creating copies of important data to have protection against data loss, hardware failure, recover from accidental deletions and restoration after malware attacks

• Essential for business continuity

File Backup

• A type of backup in which you copy every single file in designated location (folder/drive)

• Comprehensive but consumes more storage and time

• Allows browsing and selecting specific files/folders to restore

System Backup

• A type of backup which captures the entire OS, settings, programs, and files

• Allows for full restoration to a previous state after system failures or corruption

Cloud Backup

• A type of backup in which you put your files remotely via internet, access files by logging in to your account, and the data is stored in the provider’s data center

Data Restoration

• The process of recovering backed-up information and returning data to usable state.

• Critical when original data is lost