Comprehensive Business Intelligence, Data Management, and AI Concepts for Students

What is business intelligence (BI)?

BI is the process of acquiring, analyzing, and publishing data to support decision-making.

What are BI systems?

Systems that provide tools, software, and processes to carry out BI.

Give an example of BI for informing, deciding, problem solving, and project management.

Informing: dashboards; Deciding: store locations; Problem solving: fraud detection; Project management: project tracking.

What are the three primary activities in the BI process?

Acquire data, perform analysis, publish results.

What is a data warehouse?

A central repository that collects, cleans, stores, and manages data from many sources.

What are the functions of a data warehouse?

Extract, transform, load (ETL) data; maintain metadata; ensure data quality.

What are the components of a data warehouse?

Data extraction/transformation, data storage, metadata, data management tools.

What is a data mart?

A smaller, specialized subset of a data warehouse for a specific department or function.

What is the difference between a data warehouse and a data lake?

Warehouse = structured/cleaned data; Lake = raw/unprocessed data.

What are common problems with data?

Dirty data, missing values, inconsistent data, wrong granularity, curse of dimensionality.

What are the three types of BI analysis?

Reporting (summarizes past), Data Mining (finds patterns), Big Data (handles massive data).

What are the five basic reporting operations?

Sorting, filtering, grouping, calculating, formatting.

What is data mining?

Using statistical/mathematical techniques to identify patterns and make predictions.

How does data mining differ from reporting?

Reporting = describes what happened; Data mining = predicts what might happen.

What is unsupervised data mining?

No prior model; system looks for patterns (e.g., clustering).

What is supervised data mining?

Uses prior data with known outcomes to predict future results (e.g., regression).

How can companies benefit from data discovery and visualization?

Reveals patterns, improves decision-making, communicates insights.

What are the three V's of Big Data?

Volume, Velocity, Variety.

What is MapReduce?

Map splits big data into subsets; Reduce combines results into one output.

What are the three primary alternatives for publishing BI?

Push publishing, pull publishing, automated publishing.

What is the difference between static and dynamic reports?

Static = snapshot (unchanging); Dynamic = updates automatically.

What is knowledge management (KM)?

Capturing, storing, and sharing organizational knowledge.

Why might employees resist sharing knowledge?

Fear of losing security/power, lack of trust, no incentives.

What is a content management system (CMS)?

Software that manages and publishes digital content.

What challenges exist in content management?

Version control, permissions, different formats, keeping content current.

What is artificial intelligence (AI)?

Systems that perform tasks requiring human-like intelligence.

What is automation?

Using technology to perform tasks with minimal human input.

What forces have driven advances in AI?

More computing power, big data, improved algorithms, investment.

What is deep learning?

A subset of machine learning using multi-layered neural networks.

What is strong AI?

Hypothetical AI with human-level general intelligence.

What is weak AI?

Narrow AI designed for specific tasks (e.g., Alexa).

What is superintelligence?

Future AI surpassing human intelligence.

What is machine learning (ML)?

AI systems that learn from data instead of explicit programming.

What is an algorithm?

A set of rules or instructions for solving a problem.

How does ML use training data?

ML builds models from past data to predict future outcomes.

What is natural language processing (NLP)?

AI that allows computers to understand and respond to human language.

How can AI and automation reduce costs and increase productivity?

Automates tasks, reduces errors, improves efficiency.

What are impacts of automated labor on productivity?

Higher efficiency, fewer human jobs, potential displacement.

What new jobs will AI and automation create?

AI development, monitoring, creative/problem-solving roles.

Why must workers adapt to AI and automation changes?

They need reskilling and lifelong learning to stay employable.