How can you incorporate human feedback into data visualization for prediction?

From my experience, I have used Power Apps that we can embed into Power BI reports which help end users enter the required details that I as a developer only need in manual form(Because that data comes from a human understandings). This Power App was connected to a SQL table in the backend, and this SQL table was imported and used in the Power BI report. So if the end user enters such information, it will be automatically included in the next scheduled refresh of Power BI. I hope this helps!

1. Ask the right questions 2. Start simple 3. Get feedback 4. Refine based on feedback Example: You make a bar chart showing website traffic to predict peak hours. Viewers say it's confusing with too many colors. You simplify it with clearer labels and fewer colors. They then suggest adding info on user demographics to improve the prediction. You adjust the chart and prediction accordingly. By iterating with feedback, you create visualizations that are not only clear but also incorporate valuable human understanding for better predictions! #happy_reading

Understand the purpose of your prediction (e.g., sales forecasting, customer behavior analysis) and who will be using the visualizations (e.g., analysts, decision-makers).

Incorporating human feedback into data visualization for forecasting is like blending technical finesse with real-world intuition. By using interactive tools, we can gather feedback on predictions as they unfold, refining our models in real-time. This collaboration between data whizzes and industry experts adds depth to our analyses, making them more trustworthy and applicable in diverse scenarios. Plus, it's like uncovering hidden gems – those little details that algorithms might miss but humans catch onto, giving our predictions that extra edge.

Select visualizations that are clear, concise, and suitable for the data and audience. For example, use line charts for trends over time, scatter plots for correlations, and bar charts for comparisons.

Collect feedback: Use surveys, interviews, or observation to gather feedback from users interacting with your visualizations. Analyze feedback: Look for patterns and trends in the feedback to identify areas for improvement in your visualizations.

Iterate based on feedback: Make changes to your visualizations based on the feedback received. This could include adjusting the layout, color scheme, or data representation to make it more intuitive and informative. Test with users: Conduct usability testing with real users to validate the effectiveness of your visualizations and gather additional feedback.

Improving visualisations is an iterative and interactive approach where you need regular human feedback to identify what the business needs to successfully address the KPIs and how easy to digest, comprehensive and relevant the information is that you're providing!

Context matters: Consider the context in which the visualizations will be used (e.g., mobile devices, presentations) and tailor them accordingly. Accessibility: Ensure that your visualizations are accessible to all users, including those with disabilities, by following accessibility guidelines. Data privacy: Adhere to data privacy regulations and best practices when collecting and using human feedback.