Async review
Instruction and application
In ProgressRecap core topics:
Unit 1:
Effective communication in machine learning
Unit 2:
Stakeholder engagement and management
Unit 1: Effective communication in machine learning
In Unit 1, you explored:
Adapting communication styles:
How to adjust tone, language and framing when communicating with technical vs non-technical stakeholders to ensure clarity and relevance.
Core communication principles:
Transparency, consistency and empathy as the foundations for trust and collaboration in ML project teams.
Clarity and framing:
Techniques for simplifying complex technical concepts without losing accuracy, helping bridge gaps between data teams and business leaders.
Structured communication:
Using frameworks such as the ‘who, what, when, how’ approach to plan and deliver targeted, purposeful updates throughout the ML life cycle.
Common challenges:
Managing misalignment, inconsistent updates and competing priorities through clear, audience-specific communication.
Unit 2: Stakeholder engagement and management
In Unit 2, you explored:
Stakeholder identification and analysis:
How to map project stakeholders by their level of influence and interest, ensuring that attention is focused where it drives the most impact.
The stakeholder matrix and communication planning:
How to use the influence-interest matrix and a structured communication plan to deliver the right message, at the right time, through the right channel for each stakeholder group.
Engagement strategies:
Practical approaches to balance competing priorities, build trust and maintain alignment across executive sponsors, technical teams and end users.
Handover documentation:
How to create approval-oriented reports that clearly frame the problem, solution, business benefits and next steps — making stakeholder approval easier and faster.
Securing and confirming approval:
The importance of formal documentation, confirmation emails and post-approval communication to ensure that decisions are clear, traceable and acted upon.
Communicating with impact
- Adapt your tone, detail and framing to match technical and non-technical stakeholders.
With technical audiences
, focus on accuracy, data transparency and process clarity.
With non-technical audiences
, emphasise outcomes, relevance and business value.
- Avoid jargon when it creates confusion — aim for clarity, not complexity.
Mapping stakeholder influence and interest
Use a stakeholder matrix to visualise influence and interest levels:
High influence, high interest:
Manage closely.
High influence, low interest:
Keep satisfied.
Low influence, high interest:
Keep informed.
Low influence, low interest:
Monitor periodically.
Tip
Prioritise your engagement strategy based on influence and interest, not justseniority or visibility.
Planning communication and managing priorities
A strong communication plan outlines:
Who:
Key stakeholders and message owners.
What:
Critical messages or updates.
When:
Timing and frequency of communication.
How:
Preferred channels and formats.
Balancing stakeholder priorities
- Use structured, empathetic communication to manage conflicting priorities.
- Maintain transparency and balance between technical and business perspectives.
Action item: Poll — how do you engage your stakeholders?
Start with a quick stakeholder-focused poll. This will help you gauge how you think about identifying, prioritising and engaging different stakeholders in ML projects.
There are no right or wrong answers — just choose the option that best reflects your approach or experience.