How do you navigate data visualization projects with conflicting stakeholders?

Data visualization projects can be challenging, especially when you have to deal with conflicting stakeholders who have different expectations, preferences, and opinions. How can you navigate these situations and deliver effective and engaging data visualizations that meet your goals and satisfy your clients or users? Here are some tips to help you manage data visualization projects with conflicting stakeholders.

1 Understand the problem

The first step is to understand the problem you are trying to solve with data visualization. What is the purpose, scope, and audience of your project? What are the key questions, insights, or messages you want to convey with your data? What are the data sources, formats, and quality you have to work with? By defining the problem clearly, you can align your data visualization objectives with your stakeholders' needs and expectations.

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Madhusudhan polineni

Principal Analyst & Program Manager | Tech, People & Professional Services Category Sourcing | Spend & Revenue & Equity,Commodity Analyst | $30M+ Cash Flow Project Deliverables | Dashboards Developer & AI Generalist
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As a key source for achieving the organization's goals, Spend Analytics provides a variety of advantages, including Data Visibility, Prioritization of Resources, Category Management, Savings Opportunities, Rationalizing the Supplier Base, and Risk Identification.

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Lior Herman

Head of Newtrax Mining Data Platform & Ai
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I think the term conflict stakeholders is wrong here. If you are doing visualization for an enterprise, for example, there are no conflicts, but different stakeholders expect the data to help their daily work. Assuming the data is a given fact like CRM, ERP, telemetry.... I would recommend looking into RLS (role level security), so different stakeholders see the need to know the basis of data targeted for their work.

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2 Communicate early and often

The second step is to communicate early and often with your stakeholders throughout the data visualization project. This means sharing your ideas, sketches, prototypes, feedback, and revisions with them and inviting their input and feedback. Communication can help you build trust, rapport, and understanding with your stakeholders, as well as identify and resolve any issues, gaps, or conflicts that may arise along the way.

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Tracy Butler

Data Analytics Professional | MSDA | Certified Statistical Business Analyst
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A data visualization project is like any other project. One thing you can do is sketch out your proposed visualization along with the description of how the values are calculated. Send them out to stakeholders and hold a workshop. This will help your stakeholders come up with ideas for changes or additions early in the process. It also allows for more open communication. Workshops should be interactive and time-bound with any changes tracked and communicated out to the wider team.

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3 Use best practices and standards

The third step is to use best practices and standards for data visualization design and development. This means following the principles of data visualization, such as choosing the right charts, colors, scales, labels, and annotations for your data and audience, avoiding clutter, distortion, and misleading representations, and ensuring clarity, accuracy, and relevance. By using best practices and standards, you can ensure that your data visualizations are consistent, credible, and effective.

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Andrzej Leszkiewicz

AI is a tool for a brain. It’s not the brain - developed during 3-4 billion years of evolution and trained by decades of growing, learning and practicing. DM me if you need one more brain.
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There is such a thing as standard best practices for visualization. See IBCS for reference. Beyond IBCS, there is an understanding that, for example, a bar chart is generally better than a pie chart in most cases. Sadly, these standards and best practices are not widely known or accepted. If stakeholders are asking for a bunch of rainbow-colored pie charts, then something is wrong. Show them better examples and educate them. They are probably great experts in sales, finance, etc., but it's your job as a data visualization professional to create reports that actually help the stakeholders reach their business goals. What stakeholders say they want is not necessarily what they really need. Help them.

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Lior Herman

Head of Newtrax Mining Data Platform & Ai
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I don't think there is such a thing as standard best practices visualization. In practice, it is very user preference: one line graphs and the other like tables. My guidelines for visualization are: 1. Tell the "story" from the first look. If you need to export one page to pdf, this page shows you all you need. 2. Try to make it actionable. Meaning the user knows what he should do next rather than throwing a lot of data with "try to find what you are looking for."

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4 Balance creativity and functionality

The fourth step is to balance creativity and functionality in your data visualization project. This means finding the right balance between aesthetics and usability, between innovation and convention, and between art and science. Creativity can help you capture attention, evoke emotions, and tell stories with your data, but functionality can help you deliver information, answer questions, and support decisions with your data. By balancing creativity and functionality, you can create data visualizations that are both engaging and informative.

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5 Manage expectations and trade-offs

The fifth step is to manage expectations and trade-offs in your data visualization project. This means being realistic, flexible, and adaptable to the constraints, limitations, and changes that may affect your project, such as time, budget, resources, data quality, or stakeholder feedback. It also means being able to negotiate, compromise, and prioritize the most important aspects of your data visualization project, such as the scope, features, quality, or delivery. By managing expectations and trade-offs, you can avoid frustration, disappointment, and conflict.

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6 Evaluate and iterate

The sixth step is to evaluate and iterate your data visualization project. This means testing, measuring, and improving your data visualizations based on the feedback, results, and outcomes of your project. Evaluation can help you assess the effectiveness, usability, and impact of your data visualizations, as well as identify any errors, issues, or areas for improvement. Iteration can help you refine, optimize, and enhance your data visualizations based on the evaluation findings. By evaluating and iterating, you can ensure that your data visualizations are relevant, useful, and valuable.

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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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How do you navigate data visualization projects with conflicting stakeholders?

1

2

3

4

5

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1 Understand the problem

2 Communicate early and often

3 Use best practices and standards

4 Balance creativity and functionality

5 Manage expectations and trade-offs

6 Evaluate and iterate

7 Here’s what else to consider

Data Visualization

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How do you navigate data visualization projects with conflicting stakeholders?

1

2

3

4

5

6

7

1 Understand the problem

2 Communicate early and often

3 Use best practices and standards

4 Balance creativity and functionality

5 Manage expectations and trade-offs

6 Evaluate and iterate

7 Here’s what else to consider

Data Visualization

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