Unit 1: Introduction to business analytics
Analytics for Competitive Advantage notes · PTU syllabus (MBA 963-26)
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Unit summary
Firms that compete on analytics make better, faster decisions than rivals. This unit covers the concept, evolution and scope of business analytics, big data characteristics and the role of IoT, analytics and competitive advantage, business analytics models across industries, customer analytics, and data-driven decision making.
After this unit you can
- Explain the concept, evolution and scope of business analytics
- Explain big data characteristics and the role of IoT
- Explain how analytics creates competitive advantage across industries
- Apply customer analytics and data-driven decision making
PTU syllabus topics
- Concept
- evolution and scope of business analytics
- big data characteristics and role of IoT
- analytics and competitive advantage
- business analytics models across industries
- customer analytics
- data-driven decision making
Volume
Velocity
Variety
Veracity
Value
Topic 1
Concept, evolution and scope of business analytics
Business analytics: the extensive use of data, statistical and quantitative analysis, explanatory and predictive models and fact-based management to drive decisions and actions (Davenport and Harris).
- 1Analytics 1.0 (1950s–2000)
Internal structured data, descriptive reports, decision support
- 2Analytics 2.0 (2000s)
Big data at internet firms, Hadoop, data scientists
- 3Analytics 3.0 (2010s)
Data-enriched offerings in all firms, embedded and real-time analytics
- 4Analytics 4.0 (2020s)
AI and automated, cognitive analytics; generative AI
- Scope: marketing, finance, operations, HR, supply chain, risk, strategy and product development.
Topic 2
Big data and the role of IoT
Volume
Terabytes to zettabytes
Velocity
Streaming, real-time data
Variety
Structured, semi-structured, unstructured
Veracity
Uncertainty and data quality
Value
Business benefit extracted
- IoT: connected sensors and devices (machines, vehicles, meters, wearables) generate continuous data for predictive maintenance, fleet tracking, smart energy, usage-based insurance and connected products.
Example
Smart electricity meters let distribution companies detect theft, forecast load and offer time-of-day tariffs.
Topic 3
Analytics and competitive advantage
- Analytical competitors (Davenport): analytics supports a distinctive capability; enterprise-wide approach; senior management commitment; large-scale analytical ambition.
- Analytical competitors
Analytics central to strategy
- Analytical companies
Enterprise-wide analytics, not yet differentiating
- Analytical aspirations
Executive support, building capabilities
- Localised analytics
Isolated departmental efforts
- Analytically impaired
Little data or skill
- Sources of advantage: better customer insight, optimised pricing and operations, faster product innovation, superior risk management, data network effects.
Topic 4
Business analytics models across industries
| Industry | Analytics applications |
|---|---|
| Retail and e-commerce | Recommendations, demand forecasting, price and markdown optimisation, store location |
| Banking and fintech | Credit scoring, fraud detection, churn prediction, alternative-data lending |
| Telecom | Churn, network optimisation, next-best offer |
| Manufacturing | Predictive maintenance, quality analytics, supply chain optimisation |
| Healthcare | Diagnosis support, hospital capacity, epidemic surveillance |
| Airlines and hospitality | Revenue management, dynamic pricing, crew scheduling |
| Sports and media | Player performance, content recommendations, audience analytics |
Topic 5
Customer analytics
- Uses: segmentation, customer lifetime value, acquisition and churn models, next-best action, basket analysis, sentiment and voice of customer, attribution.
- 1Collect data across touchpoints
- 2Build single customer view
- 3Segment and score
- 4Personalise offers and service
- 5Measure response and refine
Topic 6
Data-driven decision making
- Principles: frame the decision and question, use relevant data, test hypotheses through experiments (A/B tests), combine data with judgement, measure outcomes.
- Barriers: HiPPO (highest paid person's opinion), poor data quality, silos, lack of skills, distrust of models.
- Evidence: firms using data-driven decision making show higher productivity (Brynjolfsson et al.).
Key terms
- Business analytics
- Use of data and models to drive decisions
- Analytics 3.0
- Era of data-enriched offerings in all firms
- Veracity
- Uncertainty and quality of data
- Analytical competitor
- Firm whose strategy rests on analytics
- HiPPO
- Decisions driven by the highest paid person's opinion
Quick revision
- Definition; Analytics 1.0 to 4.0; scope.
- 5 Vs of big data; IoT applications.
- Davenport's five stages; sources of advantage.
- Industry applications; customer analytics cycle.
- Data-driven decision making principles and barriers.
Important exam questions
Practice questions written to the PTU exam pattern for this unit's syllabus: short answers (Section A style) and long answers (Sections B and C style).
Short-answer questions
- Q1.Define business analytics.
- Q2.What is Analytics 3.0?
- Q3.State the 5 Vs of big data.
- Q4.Name Davenport's stages of analytical competition.
- Q5.Give two uses of customer analytics.
- Q6.What is the HiPPO problem?
Long-answer questions
- Q1.Explain the concept, evolution and scope of business analytics.
- Q2.Discuss big data characteristics and the role of IoT.
- Q3.Explain how analytics creates competitive advantage across industries.
- Q4.Discuss customer analytics and data-driven decision making.
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