26. Data Analytics - Share Data Through the Art of Visualization - Week 3

Learning storytelling with data. Stories make people care.

For a story to be successful, focus on who is listening.

Stay on track with your key message.

Ask self: What's the single most important thing I want my audience to learn from my analysis?


Definitions:

Data visualization // the representation and presentation of data to help with understanding

Dashboard // tool that organizes information from multiple datasets into one central location for tracking, analysis, and simple visualization

Dashboard filter // a tool for showing only the data that meets a specific criteria while hiding the rest

Data storytelling // communicating the meaning of a dataset with visuals and a narrative that are customized for each particular audience

Engagement // capturing and holding someone's interest and attention

Spotlighting // scanning through data to quickly identify the most important insights

Filtering // showing only the data that meets a specific criteria while hiding the rest

Theme // a preset of colors, formatting, and other elements of visualization that is consistent throughout the visualizations


Creating a narrative slideshow:

        - Pick a theme, add text, visuals, the big reveal at the end, paste/link/embed data

        - Create screenshots in Tableau by exporting as image, PDF, Powerpoint, etc.

        - Create presentation

        - Limit a slide with 25 words or less

        - To avoid editing original data, copy/paste, or embed data.


Narrative you share needs:

        - Characters // people affected by your story

        - Setting // describes whats going, how often, what task are involved, and the background info

        - Plot // the conflict, the tension, the complication that your analysis is trying to solve

        - Big Reveal // the resolution. show the characters how the data shown can solve the problems

        - Aha Moment // when you share your recommendation and explain why itll help success


Editing Dashboards:

        - Changing boxes to Floating allows them to hover above other boxes.


Filters:

        - Help remove outliers from a dataset visualization so that the focus can be put on the main story.


Live vs Static Insights:

        Live:

                - Pros: dynamic, scalable, up-to-date, allows immediate action, alleviate time on processes

                - Cons: takes engineering resources to maintain live and scalable, can't control narrative of 

                            data, can cause lack of trust if data isn't handled properly.

        Static:

                - Pros: tightly control point in time narrative of data and insight, allows complex analysis

                - Cons: insight's value degrades as long as data remain in static state


3 Data Storytelling Steps:

        1. Engage your audience // make the story meaningful to the audience, make them engaged

        2. Create compelling visuals // show the story of your data

        3. Tell the story in an interesting narrative // connect data to project's objective and explain it

            clearly


Effective Data Stories:

        - Setting the context // does visualization clarify the data? does it help set the context?

        - Analyzing the variables // does visualization help clarify the data variables? meet 5 second rule?

        - Drawing conclusions // does visualization help make a point? clarify data?


Understand the audiences POV:

        - what role does this audience play?

        - what is their stake in the project?

        - what do they hope to get from the data insights I deliver?


Additional Resources:

https://www.nugit.co/what-is-data-storytelling/

https://www.analyticsvidhya.com/blog/2020/05/art-storytelling-analytics-data-science/

https://www.gartner.com/smarterwithgartner/use-data-and-analytics-to-tell-a-story/

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