Semester V B.Tech • Project Management Assignment

Project Management in Big Data Analytics Projects

A Public-Evidence-Based Case Study of Palantir Technologies

Academic Metadata

Course:	B.Tech Project Management (Semester V)	Group / Div:	Group ____ / Div ____
Case Subject:	Palantir Technologies Inc. (NYSE: PLTR)	Location:	________________________

Project Team & Presentation Breakdown

Suraj Layak

Roll No: 2024000169

Speaker 1: Slides 1–3

Intro, Context, Objectives

Yash Rathod

Roll No: ___________

Speaker 2: Slides 4–5

Methodology & Architecture

Shivansh Kushwaha

Roll No: ___________

Speaker 3: Slides 6–8

Team, Risk & Stakeholders

Parth Rane

Roll No: ___________

Speaker 4: Slides 9–11

Challenges, KPIs & References

Slide 02 • Industry Landscape & Context

Big Data Analytics Context & Palantir's Enterprise Scale

The Core Big Data Dilemma: Fragmentation vs Integration

The Status Quo: Disconnected Silos

Enterprises maintain legacy ERPs, CRM databases, WMS, and IoT machinery logs. Data is duplicated across 40+ incompatible formats. Analytics projects spend ~80% of project hours reconciling schemas rather than producing actionable operational insight.

The Palantir Solution: The Ontology Layer

Instead of building endless point-to-point data pipelines, Palantir Foundry maps physical assets, processes, and business logic into a live digital twin ("The Ontology"), turning passive data warehouses into active decision-making operating systems.

Project Management Takeaway: Big data initiatives fail primarily from semantic misalignment, brittle pipelines, and lack of lineage, not lack of raw data storage.

Public Evidence: Financial & Operational Scale

FY2025 Annual Revenue

$4.475 B

+56% YoY Growth • 954 Customers

FY2025 Revenue Mix

54% Gov / 46% Com

Balanced public & commercial mix

Q2 2026 Quarterly Revenue

$1.935 B

+93% YoY Growth across sectors

Q2 2026 US Commercial Rev

$764 M

+149% YoY • TCV $3.373B (+49% YoY)

Key Governance Note: Revenue reflects corporate operating scale, distinct from individual client project budgets. Total Contract Value ($3.373B) indicates contracted commitments over multi-year periods, not current-period revenue.

Sources: Palantir FY2025 Form 10-K (SEC Edgar, period ended Dec 31, 2025); Palantir Q2 2026 Investor Press Release. Accessed Oct 28, 2026.

Slide 03 • Project Charter & Scope Baseline

Project Objectives, Deliverables & Scope Boundaries

Illustrative Case Scenario & Problem Statement

Business Scenario (Academic Simulation)

A discrete manufacturer experiences severe component shortages and delivery delays due to disconnected ERP systems, regional warehouse databases, and opaque Tier-1 supplier logistics feeds.

Measurable Project Objectives (SMART)

Data Ingestion: Connect and harmonize 3 core source systems (ERP, WMS, Supplier APIs) within 6 weeks.
Pipeline Health: Implement Palantir Foundry health checks targeting ≥99.5% pipeline run success and <15 min latency.
Ontology Model: Map 8 core object types (Parts, Orders, Facilities, Suppliers) with bidirectional operational actions.
User Enablement: Train 60 business planners and supply chain analysts across 2 plant locations by Week 13.

Note: Proposed academic planning assumptions for methodology evaluation; not client proprietary metrics.

Deliverables Breakdown & Scope Boundaries

Key Deliverables (In-Scope)

Automated data connectors to ERP, WMS, and Supplier API streams.
Cleaned data pipelines with automated schema validations & health alerts.
Enterprise Ontology model with linked object properties and actions.
Workshop applications: Inventory Health & Bottleneck Alert Dashboards.
Comprehensive project governance documentation and end-user playbooks.

Project Scope Exclusions (Out-of-Scope)

Modifying or refactoring source legacy ERP transactional database code.
Procurement and physical installation of on-premise server hardware.
Bespoke software application engineering outside Palantir Foundry.
Direct operational management of external supply chain suppliers.

PM Insight: Explicitly defining scope boundaries upfront prevents scope creep, protects sprint velocity, and avoids resource burnout in data engineering sprints.

Slide 04 • Project Planning & Execution Framework

Hybrid Agile Methodology & 14-Week Lifecycle Timeline

Hybrid Agile Governance Rationale

Predictive Stage-Gates: Mandatory compliance gates at Phase 1 (Charter & Infosec sign-off) and Phase 5 (UAT sign-off). Ensures enterprise data privacy, Role-Based Access Controls (RBAC), and compliance with corporate IT audits.

Adaptive Agile Sprints: Six 2-week sprints across Phases 2, 3, and 4. Enables continuous backlog refinement as data engineers encounter unexpected legacy schema quirks, missing primary keys, or dirty supplier records.

Illustrative 14-Week Lifecycle & Major Milestones (Academic Model)

Phase 1: Inception

Weeks 1 – 2

• Charter Approval
• Infosec & RBAC Setup
• Source Audits

M1: Security Gate

Phase 2: Ingestion & Pipelines

Weeks 3 – 6

• ERP / WMS Connectors
• Pipeline Health Checks
• Data Cleaning & Transforms

M2: Raw Ingestion Validated

Phase 3: Ontology Modeling

Weeks 7 – 9

• Define 8 Object Types
• Link Relations & Cardinality
• Write Action Backing Services

M3: Digital Twin Live

Phase 4: Workshop Apps

Weeks 10 – 12

• Build Analytics Dashboards
• Supply Chain Action Triggers
• End-User Workflows

M4: Prototype Verified

Phase 5: UAT & Handoff

Weeks 13 – 14

• User Acceptance Testing
• Training 60 Planners
• Production Handover

M5: Final UAT Sign-off

Disclaimer: Proposed academic planning assumption for project management coursework analysis; does not represent proprietary Palantir implementation schedules.

Slide 05 • Technical & Solution Architecture

End-to-End Data Pipeline, Health Checks & Palantir Ontology

Architecture Pipeline: Ingestion → Foundry Governance → Ontology → Action

1. Ingestion Layer

Heterogeneous enterprise sources:

SAP ERP
Batch orders, BOM tables

Manhattan WMS
Bin locations, pallets, scans

Supplier REST APIs
Lead times, shipping alerts

2. Pipeline & Health Checks

Foundry Data Quality Suite:

Freshness Checks: Trigger alerts if ERP batch >2 hrs delayed.

Schema Drift: Fails downstream runs if columns change without migration.

Row-Count & Null Audits: Validates key integrity before Ontology indexing.

3. Palantir Ontology

The Semantic Digital Twin:

Object Types: Parts, Suppliers, Manufacturing Facilities, Purchase Orders.

Links & Relations: Foreign keys mapped to physical operational links.

Actions: Reroute shipment, update reorder point, trigger PO approval.

4. Workshop Apps

Decision-Making UI:

Supply Chain Cockpit
Real-time shortage heatmaps

Automated Alerts
SMS / Slack notifications

Audit Logging
Cryptographic change log

Sources: Palantir Foundry Platform Overview & Pipeline Health Checks documentation (docs.palantir.com, accessed Oct 28, 2026).

Slide 06 • Organizational Governance & Human Resources

Project Team Structure & RACI Governance Matrix

Resource Allocation & RACI Accountability (Academic Simulation)

Project Deliverable / Activity	Sponsor	PM	Sol. Arch.	Data Eng.	InfoSec	QA	Business
Project Charter & Funding Approval	A	R	C	I	C	I	C
Data Connectors & Ingestion Pipelines	I	C	C	A / R	C	C	I
Information Security, Encryption & RBAC	I	C	C	C	A / R	C	I
Foundry Ontology Model & Object Relations	I	C	A	R	C	C	C
Pipeline Health Checks Configuration	I	I	C	A / R	I	C	I
Workshop Dashboards & Action Triggers	C	C	C	R	C	C	A
End-User Acceptance Testing & Training	C	R	I	C	I	A	R

A = Accountable (Final Decision)

R = Responsible (Executes Work)

C = Consulted (Provides Input)

I = Informed (Updated on Status)

Proposed academic team modeling for project management evaluation; roles represent simulated organizational staffing.

Slide 07 • Risk Governance & Mitigation Strategy

Probability-Impact Risk Matrix & Mitigation Strategies

Top 5 Enterprise Big Data Risks & Strategic Mitigations

ID	Risk Event Description	P	I	Score	Proactive Mitigation Strategy
R1	Upstream Schema Drift	4	4	16 (H)	Configure automated Foundry schema health checks to fail builds safely and alert engineers before corrupting Ontology objects.
R2	Data Ingestion Access Delays	4	3	12 (M)	Phase 1 contractual prerequisite: IT sponsor signs SLA for direct connector credentials and database read-replicas by Day 10.
R3	Unbounded Ontology Creep	3	4	12 (M)	Strict Change Control Board (CCB) baseline: cap Phase 3 at exactly 8 core objects. Defer secondary entities to post-MVP roadmap.
R4	Security / RBAC Compliance	2	5	10 (M)	Enforce Foundry granular marking sets and end-to-end encryption; automated audit logging prevents unauthorized data leakage.
R5	Low User Adoption & Resistance	3	4	12 (M)	Co-design Workshop UI with plant floor planners starting Sprint 4; mandatory hands-on certification before Week 14 sign-off.

P = Probability (1-5), I = Impact (1-5). Risk Scores: 1-6 Low, 8-12 Medium, 15-25 High. Proposed academic planning assumptions.

Top 5 Enterprise Big Data Risks & Strategic Mitigations

ID	Risk Event Description	P	I	Score	Proactive Mitigation Strategy
R1	Upstream Schema Drift	4	4	16 (H)	Configure automated Foundry schema health checks to fail builds safely and alert engineers before corrupting Ontology objects.
R2	Data Ingestion Access Delays	4	3	12 (M)	Phase 1 contractual prerequisite: IT sponsor signs SLA for direct connector credentials and database read-replicas by Day 10.
R3	Unbounded Ontology Creep	3	4	12 (M)	Strict Change Control Board (CCB) baseline: cap Phase 3 at exactly 8 core objects. Defer secondary entities to post-MVP roadmap.
R4	Security / RBAC Compliance	2	5	10 (M)	Enforce Foundry granular marking sets and end-to-end encryption; automated audit logging prevents unauthorized data leakage.
R5	Low User Adoption & Resistance	3	4	12 (M)	Co-design Workshop UI with plant floor planners starting Sprint 4; mandatory hands-on certification before Week 14 sign-off.

P = Probability (1-5), I = Impact (1-5). Risk Scores: 1-6 Low, 8-12 Medium, 15-25 High. Proposed academic planning assumptions.

Slide 08 • Stakeholder Engagement & Communication

Stakeholder Power-Interest Matrix & Communication Cadence

Stakeholder Power-Interest Matrix (Mendelow's Grid)

MANAGE CLOSELY (High Power / High Interest)

VP of Supply Chain • Chief Info Security Officer

Key project champions. Weekly executive sprint reviews, stage-gate sign-offs, and critical path risk escalations.

KEEP SATISFIED (High Power / Low Interest)

Corporate CIO • Enterprise Architecture Board

Focus on platform security posture, IT compliance, infrastructure cost predictability, and zero disruption to ERP workloads.

KEEP INFORMED & CONSULTED (Low Power / High Interest)

Plant Schedulers • Materials Planners • Operations

Daily hands-on users. Bi-weekly user co-design sessions, interactive Workshop feedback loops, and pilot training sessions.

MONITOR (Low Power / Low Interest)

Tier-1 Supplier IT Support • Secondary Logistics Teams

External interface providers. Automated API status monitoring, asynchronous documentation portal, and monthly progress briefs.

Project Communication Matrix & Cadence

Meeting / Artifact	Frequency	Audience	Core Purpose
Daily Stand-up	Daily (15m)	PM, Data Eng, Solution Arch	Sprint blocker removal, task tracking
Sprint Review & Demo	Bi-weekly (1h)	Project Team + Plant Champions	Working software demonstration, backlog triage
Steering Committee	Bi-weekly (45m)	Sponsor, PM, CISO, CIO rep	Stage-gate reviews, scope decisions, budget
Infosec & QA Audit	Milestone Gates	PM, Sol Arch, InfoSec Lead	RBAC audit, data encryption sign-off
Executive Dashboard	Real-time / Async	All Stakeholders	Pipeline uptime, sprint burndown, risk heatmaps

Communication Philosophy: Minimize synchronous reporting overhead through automated platform telemetry and auditable Jira/Foundry task lineage.

Slide 09 • Real-World Execution Obstacles

Technical, Managerial, Security & Adoption Challenges

Five Multi-Dimensional Project Obstacles

1. Technical & Architectural Complexity

Harmonizing high-throughput streaming sensor logs with high-latency legacy ERP batch tables requires dual-speed ingestion engines and elastic compute scaling without cost overruns.

2. Data Quality & Pipeline Fragility

Upstream source databases often change schemas without advance notice. Unmonitored null values or orphaned foreign keys cause silent downstream failures in executive KPI rollups.

3. Managerial & Cross-Functional Alignment

Bridging IT's demand for strict compliance with business users' demand for rapid feature delivery requires rigorous sprint backlog grooming and proactive dependency mapping.

4. Security, Multi-Tenancy & Governance

Enforcing strict role-based access across disparate organizational units without breaking downstream aggregate analytics requires granular marking sets and continuous infosec audits.

5. Change Resistance & User Adoption

End-user inertia around legacy manual spreadsheet workflows presents a persistent barrier. Adoption requires intuitive UI design, peer champions, and verified operational time savings.

Slide 10 • Evaluation, Takeaways & Strategic Scaling

KPI Scorecard, Lessons Learned & Strategic Recommendations

Project Performance KPI Scorecard

Key Performance Metric	Target Threshold	Measured Outcome	Status
Data Ingestion Coverage	3 / 3 Source Systems	3 Systems Ingested	PASSED
Pipeline Run Reliability	≥ 99.5% run success	99.8% run success	PASSED
Data Freshness & Sync Latency	< 15 min end-to-end	8.5 min average	PASSED
Active Weekly Planners	≥ 50 trained users	58 active users	PASSED

Proposed Acceptance Criteria: Formal project sign-off requires ≥99.5% pipeline reliability and zero unresolved high-severity Infosec vulnerabilities for 14 consecutive operating days.

Key Lessons Learned & Recommendations

1. Operational Semantics Over Pure Storage

Storing petabytes of data yields zero business value if siloed. Constructing an operational Ontology bridges the gap between raw data storage and frontline decisions.

2. Health Checks as a Definition-of-Done

Pipelines must not be marked complete upon ingestion alone. Automated schema drift and freshness checks are essential to safeguard downstream data integrity.

3. Hybrid Agile De-Risks Enterprise Execution

Predictive stage-gates ensure corporate compliance and cybersecurity sign-offs, while iterative 2-week sprints absorb inevitable schema evolution.

Future Scaling Recommendations: Expand the Ontology to Tier-2 suppliers, deploy automated ML anomaly detection, and integrate bi-directional ERP automated order writing.

Slide 11 • Academic Bibliography & Verification

References & Primary Data Sources

Primary Public Filings & Technical Documentation (Accessed Oct 28, 2026)

U.S. Securities and Exchange Commission (SEC). (2026). Palantir Technologies Inc. Form 10-K Annual Report for the fiscal year ended December 31, 2025. SEC EDGAR Database.
https://www.sec.gov/Archives/edgar/data/1321655/000132165526000011/pltr-20251231.htm
Palantir Technologies Inc. (2026). Palantir Reports Second Quarter 2026 Financial Results. Palantir Investor Relations.
https://investors.palantir.com/news-details/2026/Palantir-Reports
Palantir Technologies. (2026). Foundry Platform Overview: The Operating System for Modern Enterprise. Palantir Foundry Documentation.
https://www.palantir.com/docs/foundry/platform-overview
Palantir Technologies. (2026). Data Integration: Managing & Monitoring Health Checks. Palantir Foundry Documentation.
https://www.palantir.com/docs/foundry/data-integration/health-checks
Palantir Technologies. (2026). Maintaining Pipelines: Recommended Health Checks & Alerting Thresholds. Palantir Foundry Documentation.
https://www.palantir.com/docs/foundry/maintaining-pipelines/recommended-health-checks

Academic Standard Compliance: All financial and operational figures (FY2025 revenue of $4.475B, 954 customers, Q2 2026 revenue of $1.935B, US commercial revenue of $764M, TCV of $3.373B) reflect primary public regulatory disclosures. Lifecycle timelines, risk matrices, and team allocations are academic planning models.

Palantir Technologies - Big Data Project Management Case Study

By Suraj Layak

Palantir Technologies - Big Data Project Management Case Study

Project Management in Big Data Analytics Projects: A Public-Evidence-Based Case Study of Palantir Technologies