"Data is like a treasure map, and visualizations are the X that marks the spot." $-$ anonymous

One of the problems with raw data and processed output is that it's very challenging to see trends, or patterns, or the dispersion of outliers that influence the validity and reliability of the data, along with the directionality and magnitude of the data. On one hand, you can tell by looking at outputs if a predictor variable has an influence and by what amount $($for instance, $1$ unit of positive change in weight on a vehicle can have a negative effect on miles per gallon of $1\mathrm{.}56$ units just by looking at the output predictors estimates and you can tell if it's statistically significant or not. However, to visualize a trend over time, and the actual dispersion of data points $($for instance which hours in a histogram show a higher frequency of people waiting at the DMV for a selected month so you have a better idea of when to go and have a shorter wait time, it's important to create graphs and charts to better show trends and patterns. Visualizations then are a way to tell a better data driven story, and tell a data driven story better through often$-$times compelling "wow" factor moments that are simple to understand, engaging and interactive, inclusive of multiple perspectives or effecting influential variables$/$factors$,$ and enable a focus on deep insights encouraging stakeholders to ask deeper questions leading to productive dialogue and insights.

For example, Toyota is well known for applying lean manufacturing principles including "just in time" $($JIT$)$ Kanban system for delivery of new parts. This includes using visual signals to indicate when those parts are actually needed. Every individual part in an assembly uses Kanban cards that go back to suppliers to help speed up the flow of materials delivery. This requires a highly coordinated supplier integration and collaboration effort between a highly refined network of suppliers closest to their manufacturing operations. It also requires keeping inventory levels extremely low to reduce overhead costs for storing unused excess inventory and an emphasis on Kaizen continuous improvement and Total Quality Management $($TQM$).$ These robust logistics required near$-$real time performance and supply chain dashboards on monitors showing inventory levels, supplier performance, locations and performance of delivery drivers, impact on cost reduction, defect rates, and performance of the manufacturing process. With this vital information continuously monitoring and displayed so leadership can quickly pivot as needed to remain highly competitive through increased production effectiveness and quality.

Question for you to answer now: How do data visualizations impact decision$-$making processes in organizations throughout other vertical industries, and what are some key factors that help determine the success or failure of those visualizations?