Contextual Drivers Layer (CDL)¶
Purpose¶
The Contextual Drivers Layer (CDL) defines the external and internal forces that shape how the SDLC system should operate in a given environment.
It recognizes that no project exists in a vacuum — every delivery model is influenced by commercial agreements, organizational structures, product maturity, technical landscape, and relationship context.
By identifying these drivers early, teams can adapt the SDLC configuration consciously, not reactively — selecting practices, rules, and decision patterns that best fit the project’s constraints and opportunities.
CDL is therefore the adaptive intelligence layer of the 3SF framework: it provides the situational awareness necessary to design, diagnose, and evolve any project context.
CDL in the 3SF Structure¶
| Layer | Purpose |
|---|---|
| SDLC Stages | Define the value flow (Discover → Evolve). |
| SDLC Practices | Ensure quality, automation, and governance throughout. |
| SDLC Stage Dimensions | Describe how each stage behaves in context. |
| Contextual Drivers Layer (CDL) | Explain why the system must behave that way — the conditions shaping delivery design. |
| Stable Rules Layer (SRL) | Define universal rules that keep the system coherent. |
| Rule Audit Checklist (RAC) | Measure adherence to those rules. |
| Contextual Rules Catalog (CRC) | Translate CDL + SRL into specific delivery playbooks. |
CDL thus acts as the input layer for delivery system configuration and as the diagnostic layer for understanding delivery friction.
CDL Categories¶
The CDL organizes all contextual drivers into six categories, each representing a domain of influence.
These drivers can be viewed as boundary conditions or design constraints that determine how maturity, practices, and decisions should be applied.
| Category | Definition | Examples of Drivers |
|---|---|---|
| 1. Commercial Context | Financial and contractual models defining risk, ownership, and incentives. | Fixed-Bid vs. Time & Material, shared risk, cost of delay, billing structure, vendor competition. |
| 2. Organizational Context | Structure, processes, and decision hierarchy of both Client and Vendor organizations. | Centralized vs. distributed governance, number of stakeholders, matrix structures, delivery dependencies. |
| 3. Product Context | Nature, maturity, and complexity of the product or service being delivered. | New build vs. modernization, MVP vs. enterprise system, technical debt level, release cadence expectations. |
| 4. Technical Context | Technology stack, platform constraints, and delivery environment. | Cloud migration vs. on-prem, CI/CD maturity, legacy integrations, DevOps readiness. |
| 5. Relationship Context | Trust, collaboration, and alignment level between Client and Vendor. | Transactional vs. strategic partnership, communication openness, feedback culture, autonomy. |
| 6. Environmental Context | External or systemic forces beyond direct control. | Regulatory requirements, data privacy laws, time zone overlap, market dynamics, cultural or language barriers. |
Each project operates within a unique combination of these drivers.
Understanding them early allows the SDLC configuration (stages, practices, and rules) to adapt for stability and effectiveness.
CDL as a Diagnostic Lens¶
Contextual Drivers can be analyzed from two perspectives:
- Influence: How a driver shapes or constrains SDLC decisions.
Example: A Fixed-Bid contract limits flexibility in Discovery and Shape — requiring stronger scope definition and risk buffers. - Signal: How a driver manifests symptoms of stress or misalignment.
Example: Persistent scope creep under a Fixed-Bid contract indicates mismatch between commercial and delivery context.
By mapping drivers to affected SDLC Stages and Practices, teams can uncover root causes of delivery dysfunction rather than symptoms.
CDL → SDLC Connection Map¶
| CDL Category | Primary SDLC Stages Impacted | Key Practices Affected | Typical Constraints or Levers |
|---|---|---|---|
| Commercial Context | Discover, Shape, Validate | Governance & Risk, Product Thinking | Budget model, risk allocation, scope flexibility. |
| Organizational Context | Shape, Build, Release | Collaboration, Architecture & Design | Decision speed, approvals, dependency management. |
| Product Context | Discover, Build, Run | Product Thinking, Engineering & Quality | Product maturity, architecture flexibility, tech debt. |
| Technical Context | Build, Validate, Release | DevOps & Delivery, Architecture & Design | Automation readiness, tooling, environment stability. |
| Relationship Context | Discover, Shape, Evolve | Feedback & Learning, Governance & Risk | Trust, autonomy, alignment maturity, transparency. |
| Environmental Context | Release, Run, Evolve | Governance & Risk, Feedback & Learning | Regulatory controls, operational risk, localization. |
This mapping becomes the foundation for contextual rule generation (CRC) and rule validation (RAC).
CDL Assessment¶
A Contextual Driver Assessment can be done during Discovery or periodically during long-running projects.
Its purpose is to identify which drivers are stable, volatile, or misaligned.
Example checklist (simplified):
| Category | Driver | Status | Impact | Action |
|---|---|---|---|---|
| Commercial | Fixed-bid contract | Active | High constraint on flexibility | Define strict Shape governance and risk buffers. |
| Relationship | Low client trust | High risk | Blocks autonomy | Introduce transparency rituals and co-created planning. |
| Technical | Legacy system dependency | Persistent | Medium | Schedule feasibility spikes and rollback plans. |
Drivers with high impact should trigger configuration adjustments (via SRL or CRC).
CDL in Practice¶
1. During Discovery:¶
- Identify contextual drivers and classify by category.
- Highlight those that constrain design or delivery models.
- Inform Shape decisions (architecture, governance, estimation).
2. During Delivery:¶
- Monitor changes in contextual drivers (e.g., contract renewal, new stakeholders, tool migrations).
- Adjust Stage Dimensions and Practices to maintain coherence.
3. During Retrospectives or Audits:¶
- Review contextual shifts to explain performance or relationship changes.
- Update RAC and CRC mappings to reflect new constraints or opportunities.
CDL and Relationship Maturity¶
Contextual Drivers determine how much maturity a project can realistically achieve.
For example:
- A highly regulated environment may limit autonomy but increase clarity — maturity can grow in governance, not speed.
- A flexible, trusted relationship may allow experimentation — maturity can grow in feedback and learning loops.
Mature organizations don’t fight constraints; they design within them.
The CDL enables this by making constraints explicit and manageable.
Principles Failure: Contextual Contradictions¶
At times, contextual forces directly conflict with system principles — for example, a regulatory environment that enforces heavy control contradicts the principle of Trust before Control, or a fixed-bid contract that prioritizes scope over learning undermines Outcome before Output.
3SF treats these contradictions not as violations but as trade-off boundaries: situations where local compliance may temporarily override ideal behavior.
When contradictions persist unacknowledged, they lead to silent erosion of trust — teams perform rituals without intent, and principles turn into slogans.
The resolution is transparency: make constraints visible, discuss which principles are constrained, and consciously rebalance the system once conditions change.
Context cannot always be changed — but how consciously we adapt within it defines maturity.
Summary¶
- The Contextual Drivers Layer (CDL) captures the forces that shape delivery — commercial, organizational, product, technical, relationship, and environmental.
- It acts as the input lens for tuning the SDLC system and explaining why projects behave as they do.
- CDL connects context (why) with system design (how) — forming the foundation for adaptive governance and contextual playbooks (CRC).
- Mastering CDL awareness allows leaders to make informed trade-offs and build delivery systems that thrive under real-world constraints.