How would you navigate misunderstandings between data scientists and business stakeholders in ML projects?

I think there is a process of managing expectations on the business side meanwhile for data scientists it is very important to have clarity on available data its limitations and possible issues. I think if both these issues are clarified in the beginning better outcomes can be reached

follow these steps to define goals: 1. Establish clear communication channels 2. Define project objectives together 3. Use plain language 4. Identify key performance indicators (KPIs) 5. Align goals with business objectives 6. Document assumptions and expectation 7. Regularly review and refine goals 8. Foster a collaborative problem-solving approach 9. Provide context and education 10. Lead by example and encourage empathy

-Align on Business Outcomes: Begin by collaboratively defining the project’s goals. Ensure that both data scientists and stakeholders understand the desired business outcomes and how ML can contribute to achieving them. -SMART Criteria: Use the SMART framework (Specific, Measurable, Achievable, Relevant, Time-bound) to articulate goals clearly. This helps in setting expectations and measuring success.

To navigate misunderstandings between data scientists and business stakeholders in ML projects, foster clear and continuous communication. Start by ensuring both sides have a shared understanding of goals and expectations. Use simple, non-technical language to explain complex concepts, and encourage regular check-ins to align progress with business needs. Promote collaboration by involving stakeholders in the decision-making process and validating assumptions frequently. Building trust through transparency and setting realistic milestones can bridge gaps and enhance project success.

By speaking plainly, you can: - Prevent misunderstandings and miscommunication - Ensure stakeholders understand ML concepts and solutions - Build trust and credibility with stakeholders - Drive successful ML project outcomes - Enhance collaboration and communication between data scientists and stakeholders

Data scientists often use technical language that may not be easily understood by business stakeholders. Act as a translator by breaking down complex concepts into simple, non-technical terms. Use analogies and visual aids like charts and diagrams to make technical details more accessible and comprehensible.

Frist , Speak their language 1. Cut to the chase: Get straight to the point, avoiding technical jargon. 2. Talk turkey: Use plain language to explain complex ML concepts. 3. Break it down: Simplify technical terms into easy-to-understand bits. Second, Meet them halfway 1. Find common ground: Identify shared goals and interests. 2. Be on the same page: Ensure understanding and alignment. 3. Read from the same script: Use clear, consistent language. Finally, technicalities to close yours 1. Keep it simple, stupid (KISS): Use straightforward language. 2. Avoid alphabet soup: Limit technical abbreviations.

It's important to evangelize to business stakeholders about the differences between engineering work and machine learning. While engineering tasks are typically straightforward, machine learning is inherently exploratory and iterative. Including business stakeholders in the iteration loop is key to aligning their expectations with the dynamic nature of ML projects.

By providing regular updates, you can: - Prevent misunderstandings and miscommunication - Ensure stakeholders are informed and engaged - Build trust and credibility with stakeholders - Drive successful ML project outcomes - Enhance collaboration and communication between data scientists and stakeholders

-Scheduled Check-ins: Implement regular meetings or updates to discuss progress, challenges, and next steps. This keeps everyone aligned and informed. -Progress Reports: Share concise reports that highlight key metrics, insights, and any changes in project direction. Visual aids like dashboards can enhance clarity.

More than the tool itself, the usage of the tool means that the communication happens 'in writing'. This means that the possibility of misunderstanding at least reduces. Even if a client does see the update on the tool I find that a good norm is to share weekly updates and direction to the client so there is general awareness where we are going. I do not tend to use 'technical jargon' and we adapt to the client terminology. If it is a crucial task I tend to repeat it a few times in different words. Why do I repeat? because misunderstanding is usually because the language is 'ambiguous' and if you repeat with different words may help.

-Project Management Software: Utilize tools like Trello, Asana, or Jira to track tasks, timelines, and responsibilities. This promotes transparency and accountability. -Shared Documentation: Use platforms like Google Docs or Confluence for collaborative documentation. This ensures that all stakeholders have access to the latest information and can contribute their insights.

-Workshops and Training: Organize sessions where data scientists can share their knowledge about ML concepts, while stakeholders can educate the team about business processes and needs. -Cross-Functional Teams: Encourage collaboration between data scientists and business stakeholders on smaller projects or pilot initiatives. This builds mutual understanding and respect.

Facilitate Open Dialogue: Encourage regular, transparent communication between both parties. Clarify Objectives: Ensure that both data scientists and stakeholders have a clear understanding of project goals. Establish Common Ground: Identify and emphasize shared interests and objectives. Demonstrate Reliability: Consistently deliver on promises to build credibility and trust. Foster Mutual Respect: Promote appreciation for each other’s expertise and contributions

-Open Dialogue: Encourage an environment where questions and concerns can be raised freely. This openness fosters trust and collaboration. -Celebrate Successes Together: Acknowledge and celebrate milestones and successes as a team. This reinforces a shared commitment to the project and strengthens relationships.