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Unit 4 of 4 · MBA Sem 4

Unit 4: Execution, governance and trends

Analytics for Competitive Advantage notes · PTU syllabus (MBA 963-26)

3 min read7 topics10 exam questions
On this page
  1. Unit summary
  2. Developing execution plans
  3. Performance measurement frameworks
  4. Data governance
  5. Privacy and ethical challenges in analytics and AI
  6. Cybersecurity and data risk management
  7. Building a data-driven culture
  8. Future trends in AI and big data
  9. Key terms
  10. Quick revision
  11. Important questions

Unit summary

Analytics programmes must be executed, governed and trusted. This unit covers developing execution plans, performance measurement frameworks, data governance, privacy and ethical challenges in analytics and AI, cybersecurity and data risk management, building a data-driven culture, and future trends in AI and big data.

After this unit you can

  • Develop analytics execution plans and performance measures
  • Establish data governance
  • Manage privacy, ethics and cybersecurity risks
  • Build a data-driven culture and anticipate future trends

PTU syllabus topics

  • Developing execution plans
  • performance measurement frameworks
  • data governance
  • privacy and ethical challenges in analytics and AI
  • cybersecurity and data risk management
  • building a data-driven culture
  • future trends in AI and big data
ClassificationData governance essentials
Responsible analytics

  • Data quality

    Accurate and complete

  • Privacy

    Consent, DPDP Act

  • Security

    Access control, encryption

  • Ethics

    Fairness, bias checks

  • Culture

    Decisions driven by data

1

Topic 1

Developing execution plans

ProcessAnalytics execution plan
  1. 1

    Vision and objectives

  2. 2

    Prioritised use-case roadmap

  3. 3

    Data and technology architecture

  4. 4

    Team and operating model

  5. 5

    Change management and training

  6. 6

    Pilot, scale and industrialise (MLOps)

  7. 7

    Measure and iterate

2

Topic 2

Performance measurement frameworks

  • Metrics: business impact (revenue, cost, risk), adoption (users, decisions supported), model performance (accuracy, drift), delivery (time to value), data quality.
  • Frameworks: balanced scorecard for analytics, OKRs, value tracking against business cases.
3

Topic 3

Data governance

  • Data governance: policies, roles and processes ensuring data is accurate, secure, consistent and used properly.
ClassificationData governance elements
Data governance

  • Roles

    Data owners, stewards, governance council, chief data officer

  • Policies

    Quality, access, retention, classification

  • Metadata and catalogues

    Definitions, lineage

  • Master data management

    Single version of customers, products

  • Compliance

    Privacy law, sector regulation

4

Topic 4

Privacy and ethical challenges in analytics and AI

  • Privacy: consent, purpose limitation, data minimisation, rights of individuals — Digital Personal Data Protection Act 2023.
  • Ethics: fairness and non-discrimination, transparency and explainability, accountability, human oversight, avoiding manipulation, responsible use of generative AI (hallucinations, copyright).
  • Frameworks: NITI Aayog's Responsible AI principles, OECD AI principles, EU AI Act (risk-based regulation).
5

Topic 5

Cybersecurity and data risk management

  • Risks: breaches, ransomware, insider misuse, model attacks (data poisoning), third-party risk.
  • Controls: access management, encryption, monitoring, incident response (CERT-In six-hour reporting), backups, vendor assessments, data masking and anonymisation for analytics.
6

Topic 6

Building a data-driven culture

  • Leaders ask for evidence and model data use; decisions reference metrics; experimentation is encouraged; data is accessible (self-service BI); data literacy training; recognition for insight-driven results; tolerance for learning from failed tests.
7

Topic 7

Future trends in AI and big data

  • Generative AI and AI agents embedded in work; real-time and edge analytics; synthetic data; augmented analytics (natural-language queries); data marketplaces and data sharing frameworks; privacy-enhancing technologies; responsible AI regulation; quantum computing on the horizon.

Key terms

MLOps
Practices for deploying and maintaining ML models
Data governance
Policies and roles for managing data
Master data management
Maintaining a single source of key data
Explainability
Ability to understand why a model made a decision
Augmented analytics
AI-assisted data preparation and insight generation

Quick revision

  • Execution plan steps; MLOps.
  • Impact, adoption, model, delivery metrics.
  • Governance roles, policies, metadata, MDM, compliance.
  • Privacy (DPDP Act), ethics, responsible AI frameworks; cybersecurity controls.
  • Data-driven culture; future trends.

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

  1. Q1.What is MLOps?
  2. Q2.Name four analytics performance metrics.
  3. Q3.What is data governance?
  4. Q4.State three ethical principles for AI.
  5. Q5.Name two cybersecurity controls for analytics data.
  6. Q6.State two future trends in analytics.

Long-answer questions

  1. Q1.Explain how an analytics execution plan is developed and measured.
  2. Q2.Discuss data governance and its elements.
  3. Q3.Discuss privacy, ethical and cybersecurity challenges in analytics and AI.
  4. Q4.Explain how to build a data-driven culture and discuss future trends.

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