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What are the best practices for communicating data science findings to stakeholders?

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In the realm of Business Intelligence (BI), effectively communicating data science findings to stakeholders is crucial. Your findings can influence decision-making, shape strategies, and drive business growth. However, the complexity of data science can make this a challenging task. The key is to bridge the gap between the technical aspects of your work and the practical needs of your audience. By following best practices in communication, you can ensure that your insights are not only heard but also understood and acted upon. Whether you're presenting to executives, sharing insights with colleagues, or explaining outcomes to clients, the way you communicate your data science findings can make a significant difference.

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Eric Cordeiro-Spinetti, PhD

Scientist and Communicator
Namrata Dutta

Research Analyst| Data Analyst| Published Reseacher| Project Management
Evelyn Eze CSc

Accounts | Manager | Impact | SDGs Advocate | Humanitarian

1 Know Your Audience

Understanding your audience is pivotal in tailoring your communication. Stakeholders vary in their technical knowledge and interest in data science intricacies. Some might be more interested in the high-level implications of your findings, while others may require a deep dive into the data. By assessing their level of expertise and what they care most about, you can adjust the complexity of your language, focus on the most relevant insights, and preemptively address potential questions. This approach ensures that your message resonates with your audience and that your findings are actionable.

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Namrata Dutta

Research Analyst| Data Analyst| Published Reseacher| Project Management
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When sharing data science findings with others, it's important to speak their language and tell a story that makes sense. Start by understanding who you're talking to and what matters most to them. Keep it simple and use pictures to show what the data means. Focus on the big ideas, not all the tiny details. Be honest about what the data can and can't do, and encourage questions so everyone understands. Finally, offer suggestions for what people can do with the information, and keep talking to help them even after the conversation is over.

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Evelyn Eze CSc

Accounts | Manager | Impact | SDGs Advocate | Humanitarian
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Identify who your stakeholders are and what their backgrounds, interests, and levels of technical expertise are. Tailor your communication to match their needs. Be clear about the purpose of your communication. Are you informing, persuading, or driving action?

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2 Simplify Complexity

Data science is inherently complex, but your communication shouldn't be. Translating complex analytical results into simple, clear messages is an art. Avoid jargon and use analogies or metaphors to make abstract concepts more tangible. Visual aids like charts and graphs can be powerful tools to convey your findings at a glance. Remember, the goal is to make your audience understand the significance of the data without overwhelming them with the technical process behind it.

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Eric Cordeiro-Spinetti, PhD

Scientist and Communicator
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Smart them up instead of dumbing them down! Stakeholders are intelligent people who may not be fluent in scientific language. So, what common ground can you use to build complex concepts together? Recently, I used the analogy of growing from a baby to an adult to explain cell multiplication — everyone was a baby once. This relatable process helped me illustrate how cancer arises when cell growth gets out of control. Finally, I explained how our technology could revolutionize blood cancer treatment. You can certainly add layers of complexity, like building a 10-story wedding cake, ready to be served with champagne.

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3 Storytelling with Data

Storytelling is a powerful tool in communication, and data science is no exception. By weaving your findings into a narrative, you make the data relatable and memorable. Start with the problem or question that initiated the analysis, lead through the investigative journey, and culminate with the insights. This not only engages your audience but also provides context that helps them grasp the importance of your work. A good story can turn abstract numbers into compelling evidence for action.

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Evelyn Eze CSc

Accounts | Manager | Impact | SDGs Advocate | Humanitarian
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Present your findings in a logical sequence that tells a story. Start with the problem or question, followed by the analysis process, and end with the conclusions and recommendations. Provide context for your data. Explain why the findings are important and how they relate to the business objectives.

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4 Interactive Engagement

Encourage interaction to make your presentation more engaging. This could involve asking questions to gauge understanding, inviting feedback, or even incorporating interactive elements like live data demonstrations if appropriate. By fostering a two-way conversation, you ensure that your audience is following along and that they feel involved in the process. Interactive engagement can also help you identify areas that may need further clarification.

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5 Actionable Insights

Ensure that your communication emphasizes actionable insights. Stakeholders are most interested in how data science findings can affect decisions and operations. Highlight key takeaways and suggest concrete steps or recommendations based on your analysis. By providing a clear path from data to action, you help stakeholders understand the value of your work and how it can be applied to achieve business objectives.

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6 Follow-up Support

Offer follow-up support to address any lingering questions or concerns after your presentation. This could mean providing additional documentation, offering one-on-one meetings, or making yourself available for further discussions. Being accessible and responsive not only builds trust with your stakeholders but also ensures that the insights you've provided are fully understood and can be effectively integrated into business strategies.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the best practices for communicating data science findings to stakeholders?

1

2

3

4

5

6

7

1 Know Your Audience

2 Simplify Complexity

3 Storytelling with Data

4 Interactive Engagement

5 Actionable Insights

6 Follow-up Support

7 Here’s what else to consider

Business Intelligence

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What are the best practices for communicating data science findings to stakeholders?

1

2

3

4

5

6

7

1 Know Your Audience

2 Simplify Complexity

3 Storytelling with Data

4 Interactive Engagement

5 Actionable Insights

6 Follow-up Support

7 Here’s what else to consider

Business Intelligence

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