WEEK 12 - BUSINESS INTELLIGENCE AND ARTICIAL INTELLIGENCE

Question 1

how does business intelligence and business analytics support decison-making?

Answer 1

• BI and analytics transform data into actionable insights
• data warehouses and data marts store and organize large volumes of data for analysis
• analytical platforms and tools such as Hadoop and OLAP (online analytical processing) enhance data processing capabilities
• data mining discovers patterns and relationships in large datasets, aiding predictive analytics

Question 2

what is the impact of improved decision making on business value?

Answer 2

• improved decision-making enhances business value at all levels, from routine to strategic decisions
• example: daily inventory management decisions by an inventory manager can accumulate significant annual value for a business

Question 3

what is predictive analytics?

Answer 3

• uses big data from various sources (social media, transactions, sensors) to predict future trends and behaviors)
• examples: predicting customer responses to marketing campaigns, identifying at-risk customers, forecasting demand

Question 4

examples of predictive analytics

Answer 4

1. aerospace: predict impact of maintenance on aircraft reliability and fuel efficiency
2. automotive: integrate component sturdiness data into manufacturing plans
3. energy: forecast price and demand ratios
4. manufacturing: predict machine failure ratios
5. retail: track customer behavior to optimize sales strategies
6. law enforcement: use crime trend data to allocate resources effectively

Question 5

types of AI techniques

Answer 5

1. expert systems: capture human expertise in a limited domain and use rules to solve specific problems
2. machine learning (ML): algorithms learn patterns from data without explicit programming
   - supervised learning: trained with labeled data (eg. identifying objects in images)
   - unsupervised learning: identifies patterns in data without labeled examples
3. neural networks: systems that simulate the human brain to recognize patterns and make deicions
4. genetic algorithms: optimize solutions by simulating natural selection processes
5. natural language processing (NLP): enables machines to understand and interact using human language
6. intelligent agents: perform tasks like finding information or routing calls

Question 6

limitations of AI and machine learning (ML)

Answer 6

• AI systems lack understanding of ethics and context
• ML systems can be biased based on the data they are trained on
• AI cannot fully explain its decision-making process
• large data sets are necessary for effective ML, but nonsensical patterns can emerge
• AI systems are best used for specific, well-defined tasks rather than general intelligence