What's the best way to handle feedback and revisions on data analysis reports and dashboards?

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Data analysis reports and dashboards are essential tools for communicating insights and recommendations to stakeholders, clients, and colleagues. However, creating them is not a one-time task. You often need to incorporate feedback and revisions from different sources, which can be challenging and time-consuming. How can you handle this process effectively and efficiently? Here are some tips to help you.

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1 Understand the purpose

Before you start working on your report or dashboard, make sure you understand the purpose and the audience of your deliverable. What is the main question or problem you are trying to answer or solve? Who will use or benefit from your analysis? What are their expectations and preferences? How will they access and interact with your report or dashboard? These questions will help you define the scope, format, and style of your data analysis.

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Maurizio Pisciotta??

5x LinkedIn Top Voice | Head of Business Intelligence & Data Analytics @ Tella | Innovation Strategy
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I consistently follow this approach: What? - What is the problem or question we aim to address? So What? - What will our dashboard help the audience learn or understand? Now What? - How can we guide the audience in taking action or making decisions based on this information?

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2 Plan and document your workflow

To avoid confusion and duplication of work, it is important to plan and document your data analysis workflow. This includes the steps you take to collect, clean, explore, transform, model, visualize, and interpret your data. You should also use consistent and clear naming conventions, comments, and annotations for your code, files, variables, and charts. This will make it easier for you and others to follow, review, and modify your work.

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Maurizio Pisciotta??

5x LinkedIn Top Voice | Head of Business Intelligence & Data Analytics @ Tella | Innovation Strategy
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Implementing version control and collaboration tools such as Git and platforms like GitHub. They help us see what changes have been made, work together with others, and keep a record of your analysis history. When those tools are combined with a well-documented workflow, we'll cut down on confusion, avoid doing the same work twice, and make data analysis process run more smoothly and neatly.

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3 Seek feedback early and often

Feedback is a valuable source of learning and improvement for your data analysis skills. However, feedback can also be overwhelming and conflicting if you wait until the last minute or receive it from too many people. To avoid this, seek feedback early and often from your key stakeholders, clients, or colleagues. Ask them specific questions about your data, methods, assumptions, results, and recommendations. Use their input to refine your report or dashboard and address any gaps or errors.

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Eugenia Chinchilla

Senior Project Manager @ Conduent | Project Management and Business Analysis
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Seek feedback early and ofthen ensures that the information you're providing is relevant, useful, and easy for your audience to understand. It also makes your audience feel like they're a part of the project from the beginning, which boosts their interest. By using the feedback you receive to improve on what and how you present the data, you increase the chances of making analytics a seamless part of your audience's daily work life. This is a key factor in making your analytics project sustainable and successful. In the late stages of your analytical project, feedback continues to be important to ensure that the information is still relevant or if changes are needed. Also helps you to verify the impact of the use of data.

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4 Use version control and backup

One of the biggest challenges of handling feedback and revisions is keeping track of the changes you make to your data analysis report or dashboard. To avoid losing or overwriting your work, use version control and backup tools. Version control tools allow you to track, compare, and revert changes to your code and files. Backup tools allow you to save and restore copies of your work in case of accidents or disasters. These tools will help you manage your revisions and collaborate with others more smoothly.

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5 Communicate your changes

Once you have incorporated feedback and revisions to your data analysis report or dashboard, you need to communicate your changes to your stakeholders, clients, or colleagues. This will help them understand what you have done and why, and how it affects your findings and recommendations. You can use different methods to communicate your changes, such as highlighting them in your report or dashboard, creating a summary or a changelog, or presenting them in a meeting or a call.

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Maurizio Pisciotta??

5x LinkedIn Top Voice | Head of Business Intelligence & Data Analytics @ Tella | Innovation Strategy
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Moreover, it is helpful to set up a feedback loop mechanism. This encourage stakeholders, clients, or colleagues to provide input and comments on the revised report or dashboard.

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6 Learn from the experience

The final tip for handling feedback and revisions on your data analysis report or dashboard is to learn from the experience. Feedback and revisions are not only a way to improve your current work, but also a way to improve your future work. You can use them to identify your strengths and weaknesses, learn new techniques and best practices, and discover new insights and opportunities. You can also use them to build trust and rapport with your stakeholders, clients, and colleagues, and to enhance your data analysis collaboration and communication skills.

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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's the best way to handle feedback and revisions on data analysis reports and dashboards?

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1 Understand the purpose

2 Plan and document your workflow

3 Seek feedback early and often

4 Use version control and backup

5 Communicate your changes

6 Learn from the experience

7 Here’s what else to consider

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