Management Information Systems Notes

Data in Organizations

  • Transactional Data (internal): Data from day-to-day operations (customer, financial, product data).
  • Non-transactional Data (external): Data from external sources (social media, internet, sensors, purchased data).

Big Data

  • Massive unstructured/structured data sets from various sources.
  • Volumes too large for typical DBMS.
  • High volume, velocity, variety.
  • Big data analysis analyzes all data, while transactional data analysis analyzes small subsets.

File Organization Terms

  • Database: Group of related files.
  • File (table): Group of records of the same type.
  • Record: Group of related fields.
  • Field: Group of characters.
  • Entity: Person, place, or thing.
  • Attribute: Characteristic describing an entity.

Data Hierarchy

  • Bit (0 or 1) -> Byte (character) -> Field -> Record -> File -> Database.

Database Management Systems (DBMS)

  • Relational Database: Collections of related tables.
  • DBMS: Interfaces between applications and physical data files.
  • Solves problems of traditional file environment.
  • Data dictionary: Definitions of data elements.

Relational DBMS

  • Data represented as two-dimensional tables.
  • Entity-Relationship Data Model: track entities (order, customer, supplier) and attributes (OrderNumber, CustomerNumber).
  • Identifier: Uniquely identifies one entity instance (CustomerNumber).

Designing Relational Databases

  • Conceptual (logical) design: Abstract model.
  • Normalization: Minimizes redundant data, builds relationships using primary and foreign keys.

Keys

  • Key field: Uniquely identifies each record.
  • Primary key: Field in table used for key fields.
  • Foreign key: Primary key used in another table for look-up.

Structured Query Language (SQL)

  • Data manipulation language to add, change, delete, retrieve data.
  • SELECT: Creates a subset of data.
  • JOIN: Combines tables using keys.
  • PROJECT: Creates a subset of columns in a table.

Non-Relational Databases (NoSQL)

  • Flexible data model, no specific structure required.
  • Data stored across distributed machines (nodes).
  • Easier to scale, handles large volumes of unstructured data.
  • Fault-tolerant: Data replicated across machines.
  • Used through cloud services (DBaaS) or private clouds.

Business Intelligence (BI)

  • Transformation of data into actionable knowledge.

Business Intelligence Infrastructure

  • Data warehouse: Stores current and historical data.
  • Data marts: Subset of data warehouse for specific functions.
  • NOSQL database (Hadoop): Enables distributed parallel processing of big data.
  • Analytic platforms: High-speed platforms for large datasets using relational and non-relational tools.

BI Users

  • Casual users: Organizational members who retrieve data (MIS for routine reports).
  • Power users/business analysts: Use DSS for sophisticated analysis, custom reports, OLAP, data mining.

Online Analytical Processing (OLAP)

  • Multidimensional data analysis.

Data Mining

  • Finds hidden patterns and relationships in datasets.

Prediction

  • Uses variables to predict unknown values.
  • Regression: Predicts a value based on other variables.

Text Mining

  • Extracts key elements from unstructured data.
  • Sentiment analysis: Uses positive and negative word lists.
  • Topic analysis: Identifies main ideas in the text.

Association Rules

  • Determines products people purchase together.

Clustering (Segmentation)

  • Groups objects into clusters based on similarity.