Information Systems and Data-Driven Decision Making
Core Concepts
Information systems (IS) support business processes and decision-making. Key types: transaction processing systems (TPS), management information systems (MIS), decision support systems (DSS), executive information systems (EIS). Data-driven decision making involves collecting, analyzing, and interpreting data to inform decisions. Concepts: data quality (accuracy, completeness, timeliness), business intelligence (BI), analytics (descriptive, predictive, prescriptive), and key performance indicators (KPIs).
Traps in Questions
- Confusing data, information, and knowledge: data are raw facts; information is processed data; knowledge is contextual understanding.
- Misunderstanding DSS vs MIS: MIS provides routine reports; DSS supports non-routine decision-making with models.
- Assuming more data is always better: data quality matters more than quantity; bad data leads to bad decisions.
- Mixing up predictive (what will happen) and prescriptive (what should we do) analytics.
- Forgetting that KPIs must be aligned with strategic objectives.
Worked Example / Rule of Thumb
A retailer wants to reduce stockouts. Rule: Use descriptive analytics to identify past stockout patterns, predictive analytics to forecast demand, and prescriptive analytics to optimize inventory levels. Ensure data quality: accurate sales data, timely updates. A common trap is to jump to prescriptive without understanding the problem.
Information Systems in Business
Understand ERP (integrates core business processes), CRM (customer relationships), and SCM (supply chain). A trap is to assume ERP implementation is purely technical; it requires change management.
Data Governance
Policies for data security, privacy, and quality. A trap is to overlook compliance (e.g., GDPR) when using customer data.