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ITM 451

Business Data Analytics

TU BITM · Semester 8 · BITM curriculum and programme regulation

Requirement
required
Credits
3
Study Guide
6 units available

Chapter notes

Authoritative study guide across 6 syllabus units

Open a unit to read its study notes.

Unit 1: Introduction to Business Intelligence· 5 LHs· ~6 marks

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Part 1: Core Concepts (2 Marks)

  • Business Data Analytics & Intelligence (BDA/BI): An umbrella term combining architectures, tools, databases, analytical methodologies, and applications to enable business executives to make better, data-driven decisions.
  • Decision Support System (DSS): An interactive computer-based information system that utilizes data models and specialized databases to assist management in solving semi-structured and unstructured decision problems.
  • Descriptive vs Predictive vs Prescriptive Analytics:
    • Descriptive: “What happened?” (Standard reporting, dashboards).
    • Predictive: “What will happen?” (Statistical modeling, machine learning forecasting).
    • Prescriptive: “What should we do?” (Optimization algorithms, simulation, automated decision rules).

Part 2: Structural Frameworks (10 Marks)

Business Data Analytics High-Level Architecture

  1. | Operational Data Sources: ERP, CRM, Web Logs, IoT Sensors |
  2. ETL Data Extraction & Cleaning
    • | Enterprise Data Warehouse (EDW) & Data Marts (Star Schemas) |
  3. Analytical Processing
    • | BI Presentation Layer: Dashboards, OLAP Cubes, KPIs, Alerts |

Part 3: Distinction Tables

Basis of Comparison Operational Systems (OLTP) Business Data Analytics (BI/OLAP)
Primary Focus Day-to-day transaction processing. Strategic decision analysis and trend forecasting.
Data Scope Current real-time granular data. Multi-year historical summarized data.
Database Design Normalized (3NF) to maximize write speed. Denormalized (Star/Snowflake) to maximize read query speed.

Part 4: Worked Problems & Exam Traps

Formulating Key Performance Indicators (KPIs)

A KPI must satisfy the SMART criteria: Specific, Measurable, Achievable, Relevant, and Time-bound. Example for Nepalese FinTech: “Achieve a 15%15\% increase in monthly active QR payment users within Q3 with average transaction failure rate below 0.2%0.2\%.”

BI tools do not replace operational transaction databases; they sit downstream from them, fed via scheduled asynchronous ETL pipelines.