What are the best methods for collaborating on data visualization projects with stakeholders?

Co-create. Deliver iteratively and collaborate regularly across iterations. Stay flexible on deliveries for each iteration but stay aligned to requirements. Along the way, build data literacy with end users so that that can competently self-serve from the end result.

Collaboration with stakeholders on data visualization projects is a dynamic and pivotal component of delivering effective data-driven solutions. Based on my prior experience, I recommend the following: 1. Understand the audience and the ways in which the project will bring them value 2. Address the ever-evolving challenges by explicitly outlining your deliverables, adapting to changing requirements, and emphasizing functionality of the tool. 3. Foster a culture of continuous feedback collection from diverse teams involved in the project, ensuring a well-rounded perspective

Collaborating on data visualization projects with stakeholders is essential for effective communication and insights. Here are concise methods for success: Understand Needs: Meet stakeholders to grasp their objectives and target audience's data literacy. Set Clear Goals: Define objectives and KPIs to ensure alignment. Use Tools: Utilize collaboration and visualization software. Involve Early: Engage stakeholders from the start and hold workshops. Prototype: Develop visual prototypes for quick feedback. Share Progress: Regularly update and refine based on feedback. Document Feedback: Maintain records of stakeholder input. User Testing: Test with representative users for usability. Data Governance: Ensure data security and accuracy.

KISS = Keep It So Simple or Keep it Simple Solution, this is a power, save time & money & resources. KISS should be a strategy E2E (Detail design, implementation, development, meetings, presentation, training, go live, ...). When solution far from being KISS then should do one or more of the following: 1. Consult . 2. Small research. 3. Learn

1) Think about the Why- Why does this project exists? is there a specific goal or action you wish the audience to take? Is there a specific feeling you want to invoke? Is that goal or feeling universal or does it vary by type of audience? 2) Think about Who- Who should be involved: Who are the key stakeholders and their primary role? 3) Think about What- What this tool/ visual will help you do/see differently? Really ask yourself are you just sharing the data blankly or are you trying to tell a story (e.g. This product really does sell better in the South during the summer or are you trying to convince someone to open a new country market) Keep it simple but structured to get the best results.

Choosing right tool is a critical aspect that can significantly impact the project's success 1. Assess the data sources & ensure that the chosen tools support seamless integration with these sources 2. Determine the type of visualizations needed (e.g., charts, graphs, maps, dashboards) as there are different tools excel in creating different types of visualizations 3. Ensure that the tool offers good performance for the data volume you will be dealing with & user interactions as expected 4. Assess the level of customization and flexibility required. Not all the tools provide similar customization features 5. For a real time interactive dashboard its very important to assess how data refreshes and updates will be managed

Using a data visualization tool is key for establishing data culture. Like using a costly pen doesn't make our handwriting better, even with a pencil one can write. Power bi , tableau , qlik or Google lookr, AWS quicksight are almost similar in features and scale. Decide to use the tool basis the data ecosystem, talent and total cost of ownership. .

"Choosing the right tools and platforms is crucial when collaborating on data visualization projects with stakeholders. It entails selecting software and technology that align with project goals, team proficiency, and budget. Opting for tools that facilitate data manipulation, visualization creation, and seamless collaboration streamlines the project, enhancing its efficiency and effectiveness.

Always marry the tool choice to the project's core objectives and your audience's skill set. If you're visualizing high-dimensional data for data-savvy analysts, consider using advanced platforms like R Shiny or D3.js. However, if the end-users are non-tech savvy stakeholders, something like Tableau or Power BI may be more suitable, offering drag-and-drop simplicity. At each phase, cross-check the tool's capability with the project's evolving needs; sometimes a pivot in tech stack is worth more than pushing through limitations.

Most of the time it will be PowerBI I feel or even Excel. Excel for those quick and dirty visualizations when you just want to get a peak at the data, and powerbi for when you want to build something that is a little more robust and will last longer. The advantage is that excel is a familiar space for most business employees and powerbi works well with the most common datasources or other microsoft products.

Data cleaning is undervalued as an analyst skill. So many visualizations make no sense because the data is garbage. Don’t use zeroes in categorical columns and ensure spellings are consistent.

-Version Control: Use version control systems for your visualization project. This ensures that everyone is working on the latest version and allows for easy tracking of changes. - Data Access and Sharing: Ensure stakeholders have access to the data used in the visualizations. Consider using data sharing platforms or dashboards for real-time access.

Our team found it incredibly helpful to work off a template report that was curated and fine-tuned through everyone's efforts. This template not only aligns perfectly with our business identity but can also serve multiple purposes across various functions of the business. It is vital that this template receives input from the entire team responsible for creating reports and dashboards for the business. Additionally, it should be approved by compliance and the creative team to prevent any potential future issues.

In data visualization collaborations with stakeholders, adhering to standards is vital. These standards ensure data accuracy and clear, appealing design. Accessibility is emphasized for diverse audiences, including individuals with disabilities. Consistency aids data interpretation, while ethical guidelines promote fairness and privacy. Interactivity and responsiveness enhance the overall user experience, collectively ensuring credibility, effective communication, and informed decision-making among stakeholders.

Our data engineering team worked tirelessly inside our EDW to develop a bronze to platinum medal system. As analysts we continued that excellent work by developing a playbook of best standards and practices around its presentation. Templates help to standardize all of our dashboards and reports while the medal system provides our end users with an increased level of confidence in the data. Through this we've developed a strong working relationship with our current stakeholders and both new stakeholders as well as the entire organization know what to expect from its data team. Having those best practices and standards in place can be critical to building that stakeholder relationship.

Here are my 3 points from stakeholder collaboration perspective: Anticipating Stakeholder Needs: Understand the key parameters stakeholders prioritize. Adapt the dashboard's features to match expected stakeholder interactions, ensuring clarity and relevance. Feedback Integration: Include a "Feedback Corner" in the visualization for stakeholders to share experiences. Highlight and address tasks they find most challenging or lengthy. Emphasis on User Experience (UX) Monitoring: Utilize monitoring tools within the tool. Analyze stakeholder interactions with AI for insights, aiming for ongoing refinement and alignment with user preferences.

Create a collaborative, iterative working relationship with your stakeholders and customers. Be sure to focus on the business value, and capture ROI toward decision-making insights.

Consider setting up regular progress meetings or checkpoints to maintain clear communication channels. These meetings can serve as opportunities to align goals, discuss emerging insights, and adapt the visualisation as needed. This proactive approach fosters a collaborative environment, where stakeholders feel valued and engaged, ultimately leading to more successful and impactful data visualization projects.

Imagine your data visualization project as a captivating story, and your stakeholders as the co-authors. Instead of just presenting them with the finished visualization, invite them to join you in crafting the narrative. Start with a blank canvas, and together, brainstorm the key plot points, characters (data points), and the overarching message you want to convey. Encourage stakeholders to bring their unique perspectives and experiences into the mix, much like authors infuse their stories with diverse characters and plot twists.

Incorporating stakeholders throughout the data visualization process is key. It means engaging them from the project's inception, gathering their input, and keeping them informed at every stage. This ensures that the final visualization aligns with their needs and expectations, fostering a sense of ownership and increasing the likelihood of successful outcomes.

Documentation is perhaps the most important and least valued step of any data project. Everyone will make time for the design and build of their report/dashboard, but almost no one wants to deal with documentation. And for a basic reason...documentation does not have immediate impact or ROI. It might seem to your boss that spending 2-3 days writing comprehensive documentation is a waste of resources, and want you to move on to another urgent deliverable. Truth is, documentation SAVES time, not the opposite. During my tenure at several companies, I spent more time answering questions about "why is this report showing this number" than on building dashboards and reports. And for a good reason, data has gotten more complex than ever.

Less can often be more when it comes to impactful data visualization. With my past experience, In the world of data visualization, it's easy to fall into the trap of overloading your visuals with too much information. While you may want to showcase every data point, it's important to prioritize clarity. Avoid cluttered graphs and charts that overwhelm your audience. Instead, focus on the key insights and messages that truly matter. Make your visuals a powerful tool for decision-making by keeping them clear and concise.

When we work on data visuals, it's essential to keep detailed notes about where our data comes from, how we've used it, and what we've found. This helps everyone involved understand the choices made and ensures clarity. It's like telling the backstory of our charts. When sharing our work, it's key to be clear and convincing, focusing on the main points. This makes sure our audience grasps the main message and knows its significance. After all, a well-documented and well-communicated project leads to better understanding and trust.

- Training and Education: Offer training or educational sessions to help stakeholders interpret the visualizations effectively. This is especially important if dealing with complex data. Implement the ultimate collective collaboration.

Effective collaboration in data visualization projects hinges on clear documentation and communication. Start by documenting your data sources, methods, assumptions, and findings. This provides transparency and helps stakeholders understand the rationale behind your visualizations. Tailor your communication to the audience, using accessible language and highlighting key insights that align with the project's purpose. Encourage feedback and provide actionable recommendations when relevant. Visual clarity and storytelling elements enhance understanding, ensuring that your data visualisations deliver valuable insights for informed decision-making.

Evaluating data visualizations involves diverse KPIs. For web and mobile visuals, track user engagement like page views, time spent, and click-through rates. Social media impact can be measured with shares, likes, and comments. Conversion rates are crucial for marketing visuals. User satisfaction, comprehension, and usability can be assessed through surveys and interviews. Ensure data accuracy and performance metrics like load times. Consider the costs of hosting and maintenance. Knowledge transfer and privacy compliance are also essential. The choice of KPIs depends on project goals and context, ensuring a holistic assessment of data visualizations.

- Conflict Resolution: Be prepared to handle conflicts or differing opinions among stakeholders. Maintain a clear process for resolving disagreements and making decisions. - Project Management Tools: Consider using project management tools or collaboration platforms (e.g., Trello, Asana, or Jira) to track tasks, deadlines, and project progress.

It can be demoralizing and near impossible to try and go from zero to hero and immediately discover the best data viz collaboration practices and how to implement them. For this reason, it can be useful to take on a more humble goal, one rooted in evaluation and improvement. Consider this one: our goal is to better collaborate with stakeholders on data visualization projects than we did last time. A goal like this serves as a mindset trick. Instead of trying to summit the dauntingly high mountain of best practices, the focus changes to evaluating the collaboration path you took last time and searching for creative, fun, obvious, low-cost-high-reward, partner-specific ways to improve.

Data analytics projects are never done. We all must accept this. Because underlying processes change, what is worth reporting or what is responsible reporting is constantly changing.

Evaluating and improving your work is an ongoing process. When using data to tell a story, you can expect that sometimes the story changes. Gathering feedback helps define any weaknesses and respond accordingly. By leading with transparency in your work, you improve your work; you help to create a continuous environment for constructive feedback. To help keep track of additional project requirements or issues, consider using tools like Notion or Jira to help keep a defined list of tasks, project additions, or deadlines. Learning never stops, so taking the time to embrace constructive feedback helps you grow professionally.

The key is frequent communication, setting expectations, and finding the right balance between stakeholder needs and accurate data depiction: Start with clear goals and success metrics aligned to business needs. Use mockups and prototypes to visualize options before investing in full build. Explain design choices and tradeoffs; make it a conversation, not a presentation. Document decisions and feedback in a centralized place for shared reference. Equip stakeholders to interpret data correctly by explaining visual encodings. Focus on business impact over aesthetics. Pretty but ineffective is pointless.

Dashboards: Make 'Em Findable, Shareable, and Collaborative You've got accurate numbers and sleek visuals—great! But if your dashboards are buried or hard to share, what's the point? Data thrives on collaboration and questions. Make your dashboards easy to find, share, and work on together. The more eyes and minds you have on your data, the better the insights you'll unearth.

Begin with the end in mind; who will benefit from the visualization, what question will it answer, and what purpose does it serve?

All of the above is true, as long as you keep the end -which can change slightly- in mind; why are we doing this and does it add value. The challenge is that what was envisioned as value at the start of the journey can change because of external factors. These might be as as simple as more detailed data or changing competitive environments. Keeping the balls in the air as a main goal is not always the most effective. Reshuffle can be an option

Ensure you add commentary on your dashboards and visualisations - make it clear to the wider business what the data is, what the analysis is all about, what the expectations of trends are (a chart going up over time is good, or a a bigger chunk of a pie chart is bad). Tie your charts into your business or department OKRs or KPIs, so the significance of them is known, and it allos others to see your objectives transparantly.