- Data Talent Pulse
- Tackling the Stakeholder Communication Problem
Tackling the Stakeholder Communication Problem
Are data science initiatives truly driving business value? The answer, as it turns out, hinges on a fundamental yet often underestimated facet of work– communication with stakeholders.
Ensuring that data scientists are aligned with the company's strategic goals and providing solutions that genuinely benefit the organization can be a challenging endeavor.
We spoke to multiple Data Science leaders on how they are tackling this challenge, and expand on actionable ways to implement similar strategies below.
🌐 From the Web:
Improved Communication Needed with Stakeholders on Data Needs: A recent report describing how The National Center for Science and Engineering Statistics (US) ensures its data and analyses meet data quality standards including relevance, and evaluating the extent to which NCSES has processes to identify emerging needs.
How to Create a Stakeholder Strategy: A HBR article emphasizing the importance of adopting stakeholder-based approaches for businesses, as they can lead to greater success and resilience.
Improving Data Analytics in Higher Education Requires Collaboration: Why institutions like Rutgers University must break down data silos, to make data-informed decisions effectively, and include a wide range of stakeholders in decision-making processes.
Fostering Understanding Between Stakeholders and Data Scientists
Stakeholders may need to put in the effort to learn the more technical aspects of their data science teams work:
The disconnect between stakeholders and data scientists often stems from differences in their educational backgrounds and training. This inherent distinction can lead to a shift in their respective focuses and priorities.
To bridge this gap effectively, stakeholders need to proactively invest effort in gaining a better understanding of the technical aspects of their data science teams' work. This information imbalance, characterized by stakeholders not fully comprehending the intricacies of the code and processes, necessitates a concerted effort to simplify and communicate complex technical concepts in a more accessible and understandable language during meetings and discussions.
This approach can foster better collaboration and alignment between stakeholders and data scientists, ultimately leading to more effective decision-making and data-driven outcomes.
Optimizing Data Science Collaboration Through Requirement Analysis
Effective collaboration between data science teams and business users hinges on a meticulous and thorough requirement analysis. Set up a process to:
Initiate a call or meeting that brings together the data science team or scientist with the business users.
Engage with end users who will utilize the data insights to extract precise project requirements.
Ensure a clear understanding of user needs, whether it involves incorporating new data sources or adding field calculations to existing data.
Prioritize the requirement analysis phase as the cornerstone of project success.
Once requirements are meticulously defined, proceed to acquire and integrate the relevant data.
Establish alignment with the precise needs and objectives of the business users to streamline subsequent project steps.
The Art of Translating Data to Insights for Data Scientists
The mindset shift when communicating with stakeholders isn’t easy for data scientists:
Here are actionable ways to navigate this transition:
Bottom Line Up Front (BLUF): Lead with the key conclusions and insights to ensure stakeholders grasp the main points right away.
Simplify Complexity: Avoid overwhelming stakeholders with technical details; focus on delivering a clear, understandable message.
Highlight Relevance: Emphasize how the data insights align with the business's goals and objectives.
Visual Aids: Utilize visual representations like charts or graphs to illustrate key findings, making them more accessible.
Engage in Two-Way Communication: Encourage questions and discussions to ensure stakeholders fully comprehend and can apply the insights.
Practice Concise Communication: Practice summarizing complex analyses into straightforward, digestible statements.
By adopting these strategies, data scientists can bridge the communication gap with stakeholders effectively, delivering insights that drive informed decision-making.
A Middleman Can Facilitate Data Science Communication
Here's how to leverage data product leaders for better project outcomes:
Appoint Data Product Managers: Assign individuals who possess a strong understanding of data science and are well-versed in your company's strategic objectives to the role of data product managers.
Ensure Strategic Alignment: Data product managers should be well-informed about the company's goals and long-term vision to align data projects with these objectives.
Facilitate Communication: Foster open channels of communication between data scientists and data product managers to ensure that the projects are on track and contribute to the desired outcomes.
Project Oversight: Data product managers play a pivotal role in overseeing data projects from inception to completion, ensuring that they remain focused on delivering value to the organization.
Performance Evaluation: Regularly assess the impact of data projects against predefined company goals, with data product managers taking the lead in this evaluation.
✅ Tools & Resources:
LucidChart: A RACI matrix software, allowing you map out individual tasks, stakeholders, and each stakeholders level of responsibility for that task in one central location
Stakeholder Questionnaires: Use Stakeholder Questionnaires to identify and understand people with influence over your project.
Stakeholder communications plan: A plan will help you figure out how to provide stakeholders with the right information at the right time, and via the right channels.
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