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How do you show stakeholders insights from different data sources?

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As a data scientist, you often have to work with different data sources and types, such as structured, unstructured, or streaming data. How do you show stakeholders insights from these diverse data sets? In this article, you will learn some tips and best practices to communicate your findings effectively and persuasively.

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1 Understand your audience

Before you start creating any charts, graphs, or dashboards, you need to understand who your audience is and what they care about. What are their goals, challenges, and expectations? What level of detail and technicality do they need? How do they prefer to consume information? By answering these questions, you can tailor your message and format to suit their needs and preferences.

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Carly Taylor, M.Sc.

Engineering | Gaming | Machine Learning
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As a leader in data science and machine learning, I value effective storytelling and learning from sales to overcome objections. Anticipate stakeholders' concerns and address them transparently with evidence. Embrace sales strategies to back up insights and discuss data limitations. This approach fosters trust and collaborative decision-making for data-driven success.

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2 Choose the right data sources and types

Depending on your audience and the question you are trying to answer, you may need to use different data sources and types. For example, if you want to show historical trends or patterns, you may use structured data from databases or spreadsheets. If you want to show sentiment analysis or text mining, you may use unstructured data from social media or reviews. If you want to show real-time updates or alerts, you may use streaming data from sensors or web logs. You should choose the data sources and types that are relevant, reliable, and accessible for your analysis.

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Neema Madayi Veetil

LinkedIn Top Voice 2024 - BI Analyst | Analytics | Tableau, Power BI, SQL & Python | Driving Business Growth with Data Storytelling | Podcast Host ??
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In large organizations, often times there are situations where several data tables are present for performing data tasks. Make sure to reach to the right data source. One tip would be to reach out to the data engineering team/data owners who create the data tables. Validate if the different data sources used in the available are right. And finally, document the data used. Although, the stakeholders don't understand, the documentation will help the other teams if the analysis get shared across different teams.

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3 Integrate and transform your data

Once you have selected your data sources and types, you need to integrate and transform them into a consistent and usable format. This may involve cleaning, filtering, merging, or aggregating your data to remove noise, errors, or duplicates. You may also need to apply some data engineering techniques, such as ETL (extract, transform, load) or ELT (extract, load, transform), to move your data from different sources to a common destination, such as a data warehouse or a data lake. You should ensure that your data is accurate, complete, and ready for analysis.

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4 Analyze and visualize your data

The next step is to analyze and visualize your data to generate insights and answer your question. You may use various tools and methods, such as descriptive, predictive, or prescriptive analytics, to explore, model, or optimize your data. You may also use different types of charts, graphs, or dashboards, such as bar charts, pie charts, or heat maps, to display your data in a clear and engaging way. You should choose the tools and methods that are appropriate, effective, and easy to understand for your audience.

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5 Tell a story with your data

The final step is to tell a story with your data that captures the attention and interest of your stakeholders. You should not just present the facts and figures, but also explain the context, meaning, and implications of your data. You should use a narrative structure, such as a problem-solution-benefit or a before-after-impact, to highlight the value and relevance of your insights. You should also use a compelling title, a catchy hook, and a strong call to action to motivate your audience to take action.

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Amanda Fetch, MSc

AI Innovation Advisor | Leader | Mentor | Board Director | MIT X PRO AI Leadership and AI Product Learning Facilitator| PhD Tech/AI Candidate Space Situational Awareness??Harvard Business Analytics Program
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Data storytelling is the key to making data-driven insights impactful and actionable. When presenting data, it's essential to go beyond just raw numbers and graphs and provide the broader context and implications. A well-structured narrative helps stakeholders understand the significance of the findings and how they relate to real-world challenges and opportunities. By using storytelling techniques like problem-solution-benefit or before-after-impact, you can guide your audience through a compelling journey that highlights the value of your insights. Effective data storytelling bridges the gap between data analysis and practical applications, ensuring that the true potential of the data is harnessed to drive positive change and growth.

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6 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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How do you show stakeholders insights from different data sources?

1

2

3

4

5

6

1 Understand your audience

2 Choose the right data sources and types

3 Integrate and transform your data

4 Analyze and visualize your data

5 Tell a story with your data

6 Here’s what else to consider

Data Science

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How do you show stakeholders insights from different data sources?

1

2

3

4

5

6

1 Understand your audience

2 Choose the right data sources and types

3 Integrate and transform your data

4 Analyze and visualize your data

5 Tell a story with your data

6 Here’s what else to consider

Data Science

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