Data, Information, and Knowledge Overview

Data, Information, and Knowledge

• Data is raw facts without context or intent.
  • It can be quantitative (numeric) or qualitative (descriptive)
• Information is data given context and is more specific.
• Knowledge is information that's been aggregated and analyzed to make decisions, set policies, and spark innovation.
• Wisdom is the combination of knowledge and experience that improves decision-making.

Data

• Data is used to generate knowledge for decision-making.
• Databases are organized collections of related information.
• Example: a university may use a separate database to maintain student financial records.

Database Reasons

• Control redundant data: If multiple spreadsheets are used, the same data exists multiple times, creating potential for corruption.
• Data integrity: If there’s a rule stating students must pay tuition before receiving marks, multiple files might miss this rule, leading to problems.
• Relies on human memory: It is inefficient for humans to remember data locations.

Relational Databases

• Data is organized into tables, also known as relations.
• Tables consist of records (rows) containing fields (categories).
• Example: A student table may have fields like Student ID, Course ID, and Grade Earned.

Database Design

• Designing a database involves:
  • Understanding the goal for the database.
  • Identifying the needed data to achieve the goal.
  • Understanding how the data is related to each other.
• Identify tables and fields to organize data.
  • The main table requires a primary key, which is unique to each record and doesn't change.

Primary Key & Foreign Key

• A primary key is a unique identifier for each record.
  • Example: Student Number
• A foreign key in one table links to the primary key data in the original table.
• Normalization involves:
  • Reducing duplicate data.
  • Ensuring data integrity.

Database Design: Data Types

• Text: Non-numeric data up to 256 characters. Example: student name
• Number: Numeric data. Example: Student number
• Boolean (Yes/No): 0 for No/False; 1 for Yes/True. Example: Part-time or Full-time student
• Date/Time: Numeric data that can be interpreted as a number or a time. Example: Day student started the program
• Currency: Monetary data Example: Tuition amount charged
• Paragraph Text: Stores text longer than 256 characters.
• Object: Data that cannot be typed, such as a picture or music file. Example: Student picture

Database Management Systems (DBMS)

• Software used for creating, storing, organizing, and accessing data from a database.
• SQL (Structured Query Language) is used to analyze and manipulate relational data.
• Separates the logical view (how users see the data) from the physical view (how the data is structured).
• Examples: Microsoft Access, DB2, Oracle Database, Microsoft SQL Server.

How a Relational DBMS Works

• Select: Creates a subset of records meeting specific criteria.
• Join: Combines tables to present more information than individual tables.
• Project: Creates a subset containing only selected columns, allowing users to create new tables with desired information.

The Challenge of Big Data

• Big data is massive, unstructured, and semi-structured data sets that conventional data processing tools cannot handle.
• Big data is characterized by:
  • Volume: The sheer size of the data.
  • Variety: Different forms of data.
  • Veracity: Trustworthiness of the data.
  • Velocity: Frequency of incoming data.
• Big datasets offer more patterns and insights than small datasets.
• Requires new technologies and tools.

Non-Relational Databases

• "NoSQL" (Not Only SQL) databases.
• Scale better than relational databases, often on multiple machines or data centers.
• Handle large data sets that are not easily organized into tables.
• Use flexible data models and do not require extensive structuring.
• Can manage unstructured data, such as social media and graphics.
• Examples: Amazon's Simple DB, MetLife's MongoDB.

Cloud Databases and Distributed Databases

• Cloud relational database engines are offered by cloud computing services.
  • They are priced based on usage.
  • Attractive to small and medium-sized businesses.
  • Example: Amazon Relational Database Service (offers MySQL, Microsoft SQL Server, Oracle Database engines).
• Distributed databases:
  • Stored in multiple physical locations.
  • Example: Google's Spanner cloud service.

Blockchain

• A distributed database of transactions.
• Operates on a network without a central authority.
• Maintains a growing list of records called blocks.
• Once recorded, blocks cannot be changed, enhancing security.
• Reduces transaction processing costs.
• Used for cryptocurrencies (Bitcoin) and NFTs.

Data Warehouse Benefits

• Encourages organizations to understand their data.
• Provides a centralized view of data to identify inconsistencies.
• Higher quality data leads to better business decisions.
• Allows analysis of data over multiple time periods.
• Tools combine data for more insights into business operations.

Data Mining

• An automated process of analyzing data to find unknown trends, patterns, and associations.
• Used to make better business decisions.
• Business intelligence: Uses mined data and analyzes it for information to gain a competitive advantage.
• Business analytics: Uses internal company data to improve business processes and practices.

Knowledge Management (KM)

• Companies and individuals accumulate knowledge, which is not consistently written down or saved.
• KM formalizes the capture, indexing, and storing of company knowledge.
• It helps to create a system to organize and share knowledge throughout the company.

This quiz covers the fundamental concepts of data, information, and knowledge. It explores the differences between these terms and the importance of databases in managing data. Test your understanding of how data can be transformed into actionable knowledge for decision-making.