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How do you explain the value of your data science project to stakeholders?

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Data science projects can be complex, challenging, and rewarding. But how do you communicate their value to your stakeholders, such as clients, managers, or investors? How do you convince them that your project is worth their time, money, and attention? In this article, you will learn some tips and strategies to explain the value of your data science project to stakeholders in a clear, concise, and compelling way.

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Nils Weskamp

Data Science | Drug Discovery | Medicinal Chemistry @ Boehringer Ingelheim

1 Understand your audience

Before you present your data science project to your stakeholders, you need to understand who they are, what they care about, and what they expect from you. Different stakeholders may have different goals, perspectives, and backgrounds. For example, a client may be interested in how your project will solve their business problem, a manager may be interested in how your project will align with their strategy, and an investor may be interested in how your project will generate revenue or reduce costs. You need to tailor your message to your audience and focus on the benefits that matter to them.

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Nils Weskamp

Data Science | Drug Discovery | Medicinal Chemistry @ Boehringer Ingelheim
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Data science projects often require significant investments: it may be necessary to acquire or clean large data sets, build an infrastructure or to hire experts. Stakeholders want to understand why they should approve such investments and what they will get in return. For example, a data science project might help to improve an existing process or enable a completely new kind of product or service. Tailoring your project and your message to the expectations of the stakeholders is key. If you are not already familiar with their perspective, this may require doing interviews, running workshops, or just asking many questions. Otherwise, you might just end up proposing a project that nobody understands or supports.

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Shivraj Sharma

Technical Trainer| Corporate Trainer|Python|SQL|Tableau|Power BI|ML
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Effective communication is the linchpin of data science project success. To secure stakeholder buy-in, follow these steps: Set Clear Goals: Define measurable objectives linked to organizational outcomes, like increased revenue or cost reduction. Speak Their Language: Avoid technical jargon and use visuals to simplify complex data, making it relatable to stakeholders. Showcase Impact: Provide evidence of success through pilot tests or case studies, highlighting the project's real-world significance. Transparent Risk Management: Acknowledge uncertainties and detail your plans to mitigate risks. By implementing these strategies, you bridge the gap between data science and business goals, ensuring stakeholder support for your project's success.

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2 Define your value proposition

A value proposition is a statement that summarizes how your data science project will create value for your stakeholders. It should answer three questions: what problem are you solving, how are you solving it, and why are you different from others. A value proposition should be specific, measurable, and relevant. For example, instead of saying "We use machine learning to optimize marketing campaigns", you could say "We use machine learning to increase conversion rates by 15% and reduce customer churn by 10%". A value proposition should also be concise and catchy. You should be able to explain it in one sentence or less.

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Nils Weskamp

Data Science | Drug Discovery | Medicinal Chemistry @ Boehringer Ingelheim
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The value proposition is a cornerstone of your data science project. It should not only tell your stakeholders why they should be excited about your project; it also serves as a north star to tell the project team towards what it is working. Therefore, it must be clear, accepted, and realistic. It pays off to invest enough time into defining your value proposition as this will be difficult to change afterwards - and determines your success.

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Rajat Mehta

AI Engineer | MSc in AI
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Think about the form in which the value will be delivered. It could be a model which is served as REST API, a dashboard which shows the KPIs, and so on. Ensure that these goals are decided in the early phases of development.

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3 Use storytelling techniques

Storytelling is an effective strategy for engaging stakeholders and making them remember your data science project. A successful story should have a clear structure, a captivating plot, and a memorable outcome. To achieve this, you can incorporate storytelling techniques such as setting the scene with background information about your project, building tension by describing the challenges and obstacles you faced, showing the resolution of how you overcame these issues, and highlighting the impact of how your project delivered value to your stakeholders.

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Shivraj Sharma

Technical Trainer| Corporate Trainer|Python|SQL|Tableau|Power BI|ML
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Storytelling is the way to describing and presenting your views and data in pictorial form. When we use visuals it is more easy for stakeholders to understand that , rather then data tables and figures. Quick decisions can be taken as quick understanding and real time observations is possible by them. Always focus with audience type and the domain you are working. Suppose if your domain is Healthcare and another is Event Management then the types of visuals for both of them will be very different. Storytelling always makes impact of Stakeholders, it attracts their focus and clear views of their business insights.

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4 Use visual aids

Visual aids can be an effective and appealing way to explain your data science project to stakeholders. This could include charts, graphs, tables, diagrams, maps, dashboards, or animations. Visual aids can help you demonstrate the data in a clear and meaningful manner, such as displaying trends, patterns, and outliers. They also provide a way to illustrate the steps and methods used in the project, such as the data pipeline, model architecture, or validation process. Additionally, visual aids can be used to highlight the results of your project by showing the performance, accuracy, or impact.

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5 Use simple language

Explaining your data science project to your stakeholders can be a challenge, so you should use simple language that your audience can understand and relate to. Make sure to define any acronyms or abbreviations that you use, as well as explain any concepts or terms that are specific to your project or domain. Providing examples or analogies can help your audience comprehend the idea, while avoiding unnecessary details and complexity can prevent confusion or boredom.

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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 explain the value of your data science project to stakeholders?

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6

1 Understand your audience

2 Define your value proposition

3 Use storytelling techniques

4 Use visual aids

5 Use simple language

6 Here’s what else to consider

Technological Innovation

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How do you explain the value of your data science project to stakeholders?

1

2

3

4

5

6

1 Understand your audience

2 Define your value proposition

3 Use storytelling techniques

4 Use visual aids

5 Use simple language

6 Here’s what else to consider

Technological Innovation

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