Information Systems and Business Analytics
Information systems convert data into decisions. Know the system types and their users: transaction processing systems capture routine transactions; management information systems summarize them; decision support systems support semi-structured analysis; executive information systems serve senior leaders. Distinguish data, information, and knowledge, and know the characteristics of valuable information (accurate, timely, relevant, complete, cost-effective).
The analytics hierarchy runs descriptive (what happened), diagnostic (why), predictive (what will happen), and prescriptive (what should be done). Statistics essentials: mean, median, mode; standard deviation as dispersion; correlation versus causation; sampling error and bias; confidence intervals and hypothesis testing with significance levels and Type I and Type II errors. Data quality issues include duplication, incompleteness, and inconsistent formats; master data management addresses them.
Security and governance: confidentiality, integrity, availability; authentication versus authorization; phishing and social engineering as human-centered threats. Enterprise systems include ERP (integrated core processes), CRM (customer-facing), and SCM. Cloud models are IaaS, PaaS, and SaaS.
Traps: labeling a dashboard of past sales as predictive; confusing correlation with causation; and mixing Type I (false positive) with Type II (false negative) errors.
Worked example: a retailer uses past purchase data to estimate next quarter's demand, then sets stock levels. Forecasting is predictive; choosing the order quantity is prescriptive. Rule of thumb: classify by the question asked, not by the tool used.