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Writer's pictureLuciana Nieto
Oct 155 min read

📊 Picture Perfect: How Data Visualizations Tell the Real Story 🔍

We've all heard the saying "A picture is worth a thousand words"—probably one of the most overused expressions out there. But, like most popular phrases, it holds a lot of truth (there’s a reason it’s stuck around for so long). Humans have survived—and thrived—because of our ability to recognize patterns. If something moved when it shouldn’t have, we knew to get out of there fast. There weren’t any signs that said, “Beware of the lion,” but our visual skills gave us the advantage to react quickly.


Fast forward to today, and we still rely on visuals just as much. Traffic lights change colors instead of spelling out "stop" or "go." And when it comes to texting, a meme often says what we’re thinking better than words ever could.


So, when we talk about data, why do we still insist on explaining everything with tables, especially when dealing with complex information? Is it because we’re afraid of leaving something out? Worried someone will ask for the fourth decimal of some obscure data point? Or maybe we just don’t feel confident in choosing the right chart?


No need to stress. We’ll tackle all of that today. And, since visuals are the name of the game, we’ll wrap up with a cheat sheet of visual examples
 because here, we believe in showing, not just telling.



Let’s jump in.


Understanding Your Data and Its Distribution

Before diving into predictions or decisions, the first step is always understanding the data you’re working with. That’s where Exploratory Data Analysis (EDA) comes in, like we’ve been discussing in previous posts. This phase helps us uncover trends, relationships, and outliers (those data points that stand out and could skew conclusions if not handled properly).


This is where charts like histograms or box plots become essential tools. They help us see the overall structure of the data and spot anything unusual that needs to be addressed before moving forward.


Histograms

A histogram shows the distribution of a dataset by dividing the values into intervals and counting how many values fall into each one. Each bar represents a range of data, and its height shows the frequency (how many data points fall in that range). It’s great for visualizing the shape of the distribution, spotting skewness, and identifying whether the distribution is unimodal (one peak) or multimodal (more than one peak). For example, if you’re analyzing soil moisture levels over a season, a histogram will show you how that variable behaved—giving you a quick idea of whether there were more days with high or low moisture.


Box Plots

A box plot shows the quantiles of a dataset, with the box representing the interquartile range (from Q1 to Q3), the line inside the box indicating the median, and the "whiskers" representing the overall range of the data. Outliers are displayed as points outside the whiskers—these are your outliers. Box plots are great for quickly seeing the distribution of data, whether there are long tails, where the median lies, and how the data is spread out. For instance, you could use a box plot to analyze yield data from a field.


Relationships: Connecting the Dots

Once we understand the structure of our data, the next step is to explore how different variables interact.


Scatter Plots

A scatter plot shows the relationship between two variables, with each observation represented as a point on an X-Y plane. This is ideal for detecting correlations or patterns between variables. For example, you could plot the days since planting on the X-axis and the leaf area index on the Y-axis to see how they’re related.


Bubble Charts

These are handy when you want to compare three (or even four) variables at once. For instance, if you’re analyzing yield, plant density, and fertilization, you could use a bubble chart where the size of the bubble represents different fertilization levels, and the X and Y axes represent density and yield, respectively. Add a fourth variable—like comparing data across different years—and you can use different colors for each year.


Comparing Data Across Categories

How do you compare data between different categories or show the proportion one variable takes up? We’ve got several options (besides the good old pie chart).


Radar Charts

Radar charts, or spider charts, are perfect for comparing multiple variables at once. They’re especially useful when you want to evaluate different crop varieties or analyze performance across several parameters at the same time. For instance, you could visualize how different hybrids perform in terms of pest resistance, disease resistance, water use, and yield—all in one chart. This type of visualization allows you to quickly see which varieties are best suited for your environment.


Stacked Bar Charts

Stacked bar charts are great for comparing how values are distributed within each category and comparing those distributions across groups. For example, if you want to analyze nitrogen accumulation for two genotypes and see where that nitrogen came from (soil, biological fixation, or fertilizer), a stacked bar chart will clearly and neatly display this information.


Pie Charts

Pie charts are ideal for showing proportions of a whole. We can use them to visualize how different types of crops were distributed over a season or how acreage was divided between different activities.


Treemaps

A treemap displays hierarchical data by dividing the area into rectangles proportional to each category's values. It’s useful for comparing compositions within a whole. For example, you could analyze spending on inputs like fertilizers, herbicides, insecticides, and seeds to see what percentage of the total each represents.


Trends Over Time: Tracking Critical Changes

One of the most important things when analyzing agricultural data is understanding how variables change over time. A snapshot of the data is helpful, but being able to see how conditions shift during a season or even from year to year is invaluable.


Line Charts

Line charts are perfect for showing how a variable changes over time. They connect data points on a timeline, allowing you to visualize trends, spikes, or drops. For example, you could use this to track corn yield across multiple growing seasons. The chart will show whether yields improved, stayed steady, or declined over time.


Fan Charts

Fan charts help visualize not only the central trend of a variable over time but also the uncertainty or projected variability going forward. These are represented by a fan of lines, with each line or zone showing possible future outcomes with different levels of confidence. For instance, you could project the expected soybean yield under different climate scenarios—if the season stays dry or turns very wet, yields will follow different paths.




With the data laid out and the tools to visualize it clear, now comes the fun part. How many more opportunities could we unlock by choosing the right chart to present our data?

Today, we’ve covered the most commonly used charts, but there’s a variation for every taste. The key is always asking ourselves why we’re doing it, what we want to communicate, and who’s going to see it. The most important thing is to stay true to the data because the value lies in presenting reality as clearly as possible. We’ll dive into that more in our next article, where we’ll explore styles, colors, and best practices to avoid data visualization disasters. We want to make sure your visuals are not only effective but also beautiful and functional.


At Bison Data Labs, we’re ready to guide you through every step and help you turn numbers into strategic decisions that drive real impact. If you're looking to take your analysis to the next level, we’re the team for you.

As promised, here’s a cheat sheet with all the charts we’ve discussed and how to apply them to your everyday analysis.


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