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Project Management: Methods, CI/CD, and AI

This guide teaches project management from novice to expert, first in general (software projects), then applied to data, BI, and datamarts.

Guide≈ 6 min readPartly checkedPartial checkThe main facts were checked by searching the cited sources. The items below still need checking before relying on them.To re-check
  • Décompte « 34 bonnes pratiques » de la page pitfalls-and-good-practices non recompté (57 pièges confirmés).
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In brief
  • This guide teaches project management from novice to expert, first in general (software projects), then applied to data, BI, and datamarts.
  • It covers frameworks (Scrum, predictive, agile and lean, data methods), their comparison, choosing and hybridizing, continuous delivery (CI/CD, including for data), the role of AI, and a summary of pitfalls and good practices.
  • Illustrative common thread, anonymous: a SQL Server datamart fed by a field sales force automation (SFA) application, with multilingual Power BI reports, versioned in TFVC, delivered as installation packages.
  • Each file increases in depth: 🟢 fundamentals, 🟡 practical, 🔴 expert. The /5 ratings are contextual expert assessments (see RATING).
On this page
  1. 1. Guide plan
  2. 2. Learning paths by level
    1. 🟢 Novice path (about 3 to 4 hours)
    2. 🟡 Practitioner path (about 6 to 8 hours)
    3. 🔴 Expert path (about 10 to 12 hours, over multiple sessions)
  3. 3. Map of the 13 files
  4. 4. Summary table
  5. 5. How to use this guide
  6. 6. Links with the rest of this section

Level: 🟢→🔴

1. Guide plan#

StepRead in this order
FoundationProject Management Fundamentals
ScrumScrum, the framework (Scrum Guide 2020) → Scrum in Practice → Scrum Applied to Data, BI, and the Datamart
Other methodsPredictive Methods (waterfall, V-model, spiral, RUP, PMBOK, PRINCE2, Merise, CMMI) · Agile and Lean Methods (Manifesto, Kanban, XP, Lean, Shape Up, DSDM, SAFe, DevOps…) · Methods Specific to Data Projects
ChooseComparison of Project Management Methods → Choosing Your Method and Hybridizing
DeliverCI/CD: Continuous Integration and Continuous Delivery → CI/CD for data: foundations, datamarts, pipelines, and reports
Cross-cuttingAI for Project Management (General Then Data) · Pitfalls and Good Practices in Project Management (Cross-Functional Synthesis)

The guide is read in five blocks: the common foundation, the Scrum family (framework, practice, data), the overview of other methods, decision support, and the delivery chain. The last two files are cross-cutting: they build on all the others.

2. Learning paths by level#

🟢 Novice path (about 3 to 4 hours)#

Goal: understand what a project is, how it is managed, and why data requires special handling.

  1. Project Management Fundamentals (🟢 then 🟡 sections): project, lifecycle, triangle, roles, value, risk.
  2. Scrum Framework (Scrum Guide 2020) (🟢 sections): team, events, artifacts.
  3. Scrum in Practice (backlog, user stories, INVEST): a team's day-to-day work.
  4. Scrum Applied to Data, BI, and Datamarts (🟢 sections): what an increment is in BI.
  5. Project Management Pitfalls and Best Practices (Cross-Cutting Summary): read families A, B, and D.

🟡 Practitioner path (about 6 to 8 hours)#

Goal: run a BI/datamart project with a suitable method, without dogma.

  1. Scrum Applied to Data, BI, and Datamarts in full: data stories, spikes, data Definition of Done, acceptance testing.
  2. Predictive Methods (Waterfall, V-Model, Spiral, RUP, PMBOK, PRINCE2, Merise, CMMI) and Agile and Lean Methods (Manifesto, Kanban, XP, Lean, Shape Up, DSDM, SAFe, DevOps…): know the alternatives.
  3. Methods Specific to Data Projects: CRISP-DM, Kimball, DataOps, migration, quality.
  4. Project Management Methods Comparison then Choosing and Hybridizing Methods.
  5. CI/CD: Continuous Integration and Delivery and CI/CD for Data: Databases, Datamarts, Pipelines, and Reports: pipeline, packages, idempotence, tests.
  6. AI for Project Management (General, Then Data): use cases and guardrails.
  7. Project Management Pitfalls and Best Practices (Cross-Cutting Summary) and its startup and review checklists.

🔴 Expert path (about 10 to 12 hours, over multiple sessions)#

Goal: train a team, arbitrate between methods, raise maturity.

  1. 🔴 sections of Project Management Fundamentals (Cynefin, cost of delay, laws and biases) and Scrum Framework (Scrum Guide 2020).
  2. Scrum in Practice: anti-patterns, scaling, contracts.
  3. Scrum Applied to Data, BI, and Datamarts: data debt, idempotence, migrations, DataOps.
  4. Project Management Methods Comparison, Choosing and Hybridizing Methods: weighting, tipping contexts, method change management.
  5. CI/CD for Data: Databases, Datamarts, Pipelines, and Reports: state-based vs migration-based, packages, numeric non-regression, rollback.
  6. AI for Project Management (General, Then Data): governance, gain measurement, agents.
  7. Move on to maturity matrices to measure the team, and to self-assessment.
💡 Expert tip

A learning path is not a program to complete: read what answers a current project question, and come back to the rest when it comes up.

3. Map of the 13 files#

# File What it brings in one sentence
01 Project Management Fundamentals The foundation: project, lifecycle, triangle, roles, value, risk, predictive vs adaptive, IT and data specificities.
02 Scrum Framework (Scrum Guide 2020) The Scrum Guide 2020 explained: empirical theory, team, events, artifacts, commitments, expert readings.
03 Scrum in Practice Backlog, user stories, estimation, velocity, retrospectives, anti-patterns, and thirty pitfalls.
04 Scrum Applied to Data, BI, and Datamarts Adapting Scrum to BI and datamarts: data stories, spikes, data DoD, acceptance testing on figures, data debt.
05 Predictive Methods (Waterfall, V-Model, Spiral, RUP, PMBOK, PRINCE2, Merise, CMMI) Waterfall, V-Model, Spiral, RUP, PMBOK, PRINCE2, Merise, CMMI, and when they remain the right choice.
06 Agile and Lean Methods (Manifesto, Kanban, XP, Lean, Shape Up, DSDM, SAFe, DevOps…) Manifesto, Kanban, XP, Lean, Shape Up, DSDM, SAFe, LeSS, DevOps as a culture.
07 Methods Specific to Data Projects CRISP-DM, TDSP, Kimball, DataOps, data products, DAMA, migration, data quality.
08 Project Management Methods Comparison Two /5 rated grids (general software, data/BI), weighting, and critical reading.
09 Choosing and Hybridizing Methods A decision tree, six dimensions, realistic hybrids, contracts, and method change management.
10 CI/CD: Continuous Integration and Delivery Continuous integration and delivery: pipeline, environments, deployment, feature flags, DORA.
11 CI/CD for Data: Databases, Datamarts, Pipelines, and Reports Versioning a database, idempotence, packages, data tests, anonymization, dbt, Fabric, PBIP, rollback.
12 AI for Project Management (General, Then Data) AI contribution activity by activity, gain/risk/maturity table, guardrails, prompts, limits.
13 Project Management Pitfalls and Best Practices (Cross-Cutting Summary) 57 pitfalls (symptom, cause, remedy), 34 good practices, startup, review, and data checklists.

4. Summary table#

Expert assessments, context: team of 4 to 12 people, BI/datamart project. Level: highest content level present. Density: amount of technical content (5 = very dense). Immediate usefulness: benefit for a practitioner starting tomorrow.

File Main audience Density Immediate usefulness Data side
01 Fundamentals Novice, all audiences 3/5 4/5 Dedicated section
02 Scrum framework Novice → practitioner 3/5 4/5 Limited
03 Scrum in practice Practitioner 4/5 5/5 Occasional specificities
04 Scrum and data Practitioner → expert 5/5 5/5 Core of the file
05 Predictive methods Practitioner 4/5 3/5 Dedicated section
06 Agile and lean Practitioner 4/5 3/5 By cross-reference
07 Data methods Practitioner → expert 5/5 4/5 Core of the file
08 Comparison Practitioner → expert 3/5 4/5 Dedicated grid
09 Choose and hybridize Expert, decision-maker 4/5 5/5 Dedicated dimension
10 CI/CD Practitioner 4/5 4/5 By cross-reference
11 CI/CD for data Practitioner → expert 5/5 5/5 Core of the file
12 AI and project management All audiences 3/5 4/5 Dedicated section
13 Pitfalls and good practices All audiences 3/5 5/5 Family F + checklist
✅ Good practice

If you were to read only three files: Scrum Applied to Data, BI, and Datamarts (the field-adapted method), CI/CD for Data: Databases, Datamarts, Pipelines, and Reports (safe delivery), and Project Management Pitfalls and Best Practices (Cross-Cutting Summary) (summary and checklists).

🤖 AI

The file AI for Project Management (General, Then Data) evaluates AI gain and risk activity by activity; each of the other files has its own “AI Contribution” section specific to its topic. The overall view is in the Cross-Cutting Overview — AI Contribution.

5. How to use this guide#

⚠️ Pitfall

Symptom: applying “the guide’s method” as-is. Cause: confusion between teaching a framework and prescribing it. Remedy: this guide describes trade-offs; the choice depends on your context (see Choosing and Hybridizing Methods).

6. Links with the rest of this section#

✻ Written by Claude Sonnet 5.5