Unit 2: Analytics strategy and organization
Analytics for Competitive Advantage notes · PTU syllabus (MBA 963-26)
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Unit summary
Analytics creates value only when it is tied to strategy and built into the organisation. This unit covers linking analytics with business strategy, identifying use cases, the value of business analytics, organisational challenges and process integration, building data competencies, and communicating analytics insights.
After this unit you can
- Link analytics with business strategy
- Identify and prioritise analytics use cases
- Explain the value of analytics and organisational challenges
- Build data competencies and communicate insights
PTU syllabus topics
- Linking analytics with business strategy
- identifying use cases
- value of business analytics
- organizational challenges and process integration
- building data competencies
- communication of analytics insights
- 1
Business goals
- 2
Identify use cases
- 3
Prioritise by value and feasibility
- 4
Build data competencies
- 5
Embed in processes
- 6
Communicate insights
Topic 1
Linking analytics with business strategy
- 1
Business strategy and goals
- 2
Key decisions that drive value
- 3
Questions analytics must answer
- 4
Data, tools and talent required
- 5
Use-case roadmap
- 6
Governance and measurement
- Alignment: analytics priorities follow strategic priorities — cost leaders focus on operational analytics; differentiators on customer and product analytics.
Topic 2
Identifying use cases
- Sources: pain points, high-value decisions, repetitive decisions, customer journeys, data already available.
Quick wins
High value, high feasibility — do first
Strategic bets
High value, low feasibility — invest and plan
Low-hanging fruit
Low value, high feasibility — do if cheap
Deprioritise
Low value, low feasibility
- Business case: expected benefits (revenue, cost, risk), costs, timeline, success metrics.
Topic 3
Value of business analytics
- Direct value: revenue growth (pricing, cross-selling), cost reduction (inventory, maintenance), risk reduction (fraud, credit losses).
- Indirect value: faster decisions, better customer experience, innovation, organisational learning.
- Measuring ROI: compare outcomes against control groups or baselines; track adoption.
Topic 4
Organisational challenges and process integration
- Challenges: data silos, legacy systems, unclear ownership, skills shortage, resistance from managers, pilots that never scale ("pilot purgatory"), weak governance.
- Integration: embed models into business processes and systems (CRM, ERP, pricing engines), redesign workflows, train users, define who acts on insights.
Centralised
Central team serves the firm — consistent but distant
Decentralised
Analysts in each function — close to business, duplicated
Hub and spoke (centre of excellence)
Central standards and platforms with embedded analysts
Consulting model
Central team works on projects for units
Topic 5
Building data competencies
- Roles: data engineers, data scientists, analysts, analytics translators, ML engineers, data stewards.
- Capabilities: data platforms, governance, analytical methods, business understanding.
- Data literacy for all managers — reading data, questioning assumptions, interpreting results; training, communities of practice, hiring and partnerships.
Topic 6
Communicating analytics insights
- Lead with the business answer; use clear visuals; translate statistics into impact (₹, %, customers); state confidence and limitations; recommend actions; tailor to the audience; use storytelling frameworks (situation, complication, resolution).
Key terms
- Use case
- Specific business problem addressed by analytics
- Pilot purgatory
- Failure to scale analytics pilots
- Centre of excellence
- Central hub setting analytics standards
- Analytics translator
- Person linking business needs and data science
- Data literacy
- Ability to read, work with and communicate data
Quick revision
- Strategy-to-analytics flow; alignment.
- Use-case sources; value–feasibility matrix; business case.
- Direct and indirect value; measuring ROI.
- Challenges; process integration; centralised, decentralised, hub-and-spoke models.
- Roles and data literacy; communicating insights.
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.How should analytics be linked to strategy?
- Q2.How are analytics use cases prioritised?
- Q3.State two direct benefits of analytics.
- Q4.What is pilot purgatory?
- Q5.Explain the hub-and-spoke model.
- Q6.What is an analytics translator?
Long-answer questions
- Q1.Explain how analytics is linked with business strategy.
- Q2.Discuss how analytics use cases are identified and prioritised.
- Q3.Discuss organisational challenges and process integration in analytics.
- Q4.Explain building data competencies and communicating analytics insights.
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