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  • Writer's picturePooja Yadav

How To Create A Scatter Plot For Your Business Intelligence In Fintech?

In the world of finance, data is king. But how do you effectively analyze this data to gain valuable insights? Scatter plots are a powerful tool in business intelligence (BI) that can help you visualize relationships between two different variables in your fintech data.


This guide will walk you through the steps of creating a scatter plot to unlock the hidden patterns within your data and make informed business decisions.


What Is A Scatter Plot In Fintech?


A scatter plot in fintech is a visual tool used to represent the relationship between two variables. Each scatter plot focuses on two variables – loan amount credit score, and investment preferences. These variables are plotted on separate axes, with dots representing individual data points. By analyzing how these dots cluster fintech professionals can identify trends, potential risks, and even customer segments. This allows them to make data-driven decisions that fuel innovation and growth.


For instance, Consider a tree circumference scatter plot comparing Douglas Fir Height and White Pine Height. The x-axis shows the circumference, and the y-axis shows the height of the tree.


tree circumference vs height scatter plot image

 

When Should You Use A Scatter Plot In Fintech?


Scatter plots become true game-changers in fintech when dealing with massive datasets. Not only do they illuminate trends and groupings within the data, but they also excel at pinpointing outliers that demand closer scrutiny. Imagine you're analyzing customer investment behavior – risk tolerance on the x-axis and portfolio return on the y-axis. Each dot represents an investor. By observing how these dots cluster you can unearth valuable insights. Scatter plots excel at revealing these patterns.


Beyond basic clustering, scatter plots act as translators, revealing various relationships between your fintech data. They can expose positive correlations (where both variables increase together) or negative correlations (where one increases while the other decreases). Exponential growth, linear trends, or even a complete absence of correlation – scatter plots can decipher it all.


This power of visualization empowers fintech professionals to leverage business intelligence. By highlighting the natural tendencies of your data, scatter plots can answer crucial questions. Take the common query: Does lowering interest rates increase loan applications? By plotting interest rates against the number of applications received, you can visualize the relationship.


A scatter plot can reveal a positive correlation, prompting further investigation into potential overspending habits. In essence, scatter plots in fintech are powerful tools for transforming raw data into actionable insights, driving informed decision-making, and propelling innovation in the ever-evolving financial landscape.


 

Using A Scatter Plot In Different Business Scenarios


Scatter plots might seem like a simple chart with dots, but they're a powerful tool for businesses to unlock valuable insights. Imagine a detective board filled with clues - that's what a scatter plot can be for your data. Here's how different departments can leverage its power:


Using A Scatter Plot In Different Business Scenarios

1. Create user personas


Imagine understanding your customers like never before. Scatter plots can help you create user personas with laser focus. By plotting things like purchase value against age, you can see if there are clusters of similar customers. This reveals hidden segments you can target with specific marketing campaigns.


2. Monitor product performance


Have you ever wondered if your products are priced right? Scatter plots can shed light on that. Plot the number of units sold against the revenue generated by each product. This can expose pricing inconsistencies and reveal which product lines are pulling their weight (or not!).


3. Identify departments with high-spending


Keeping an eye on departmental spending is crucial. Scatter plots come to the rescue here. Plot out how much each department spent over a period. This helps identify high spenders and see if specific departments tend to cluster together in terms of their spending habits.


4. Graph sales trends


Sales forecasting can be tricky, but scatter plots can help. Take a coffee shop, for example. Plot iced coffee sales against the outside temperature. This can reveal a trend - a correlation between hotter days and higher iced coffee sales. This knowledge can help prepare for busier days and optimize inventory.


These are just a few ways scatter plots can be used. So, the next time you have business data, remember the power of the humble scatter plot. It might just reveal the hidden gems that can take your business to the next level.


 

What Are the Types Of Scatter Plots?


Scatter plots themselves aren't typically categorized into different types, but they can be enhanced to reveal various types of relationships between the data points. Here are some ways scatter plots can be used to visualize different data interactions:


  • Correlations: This is the most common use case. By observing the overall trend of the dots, you can see positive correlations (dots cluster upwards diagonally), negative correlations (dots cluster downwards diagonally), or no correlation (dots appear randomly scattered).


  • Linear Trends:  Sometimes, the data points will roughly follow a straight line. This indicates a linear relationship between the variables, and you might even add a trendline to depict the slope (positive or negative) visually.


  • Non-Linear Trends: Scatter plots excel at showing non-linear trends where the dots follow a curve or irregular pattern. This can help identify exponential growth, sudden drops, or more complex relationships in your data.


  • Clusters and Outliers:  By observing how the dots cluster you can identify groups with similar characteristics and outliers that might warrant further investigation.


  • Color-Coded or Multi-Series Scatter Plots: To analyze more than two variables at a time, you can use color-coding or multiple series within a single scatter plot. Each data point might have a different color representing a third variable (e.g., customer segment), or you might have multiple lines or shapes representing different groups within the data.

 

What Are the Advantages & Disadvantages of Scatter Plot?


Scatter plots are like visual detectives, uncovering relationships between data points. But like any tool, they have their strengths and weaknesses. Here's a breakdown of the good, the bad, and the scattered:


Advantages:


Advantages of Scatter Plot

  • Simplicity: Scatter plots are easy to understand, even for those without a data science background. A glance can reveal trends and patterns.


  • Identifying Relationships: The magic lies in uncovering correlations. Scatter plots can show if two variables move in tandem (positive correlation), oppose each other (negative correlation), or have no connection at all.


  • Identifying Outliers:  Stray data points can stand out in a scatter plot, helping you identify potential errors or unique cases that need further investigation.


Disadvantages:


Disadvantages of Scatter Plot

  • Limited to Two Variables: Scatter plots can only handle two variables at a time. If you have more data points, you might need to create multiple plots or use different visualization techniques.


  • Data Overload: With a large dataset, the dots in a scatter plot can become overwhelming, making it difficult to see any clear patterns. Consider using data sampling or filtering techniques for better visualization.


  • Correlation vs. Causation:  Just because two variables correlate doesn't mean one causes the other. Scatter plots can't tell you the "why" behind the relationship. You might need further analysis to understand the cause and effect.


Scatter plots are a powerful tool for data exploration and uncovering hidden trends. However, it's important to understand their limitations. When used correctly, they can be a game-changer for businesses, researchers, and anyone looking to make sense of their data.


 

What Is the Difference Between Plot & Scatter Plot?


A plot is a general term that refers to any graphical representation of data, such as a line plot, bar plot, or scatter plot. It is used to display information and patterns in data visually.


On the other hand, a scatter plot is a specific type of plot that displays individual data points as dots on a two-dimensional graph. Each dot represents a single observation or data point, with one variable plotted on the x-axis and another variable plotted on the y-axis. Scatter plots are particularly useful for showing the relationship between two variables and identifying patterns or trends within the data.



 

Conclusion


Scatter plots in fintech visually depict correlations, aiding analysis and decision-making by identifying trends, outliers, and patterns. They're crucial for informed strategies and performance optimization, particularly in financial analysis and risk management within the industry.


Unlock actionable insights! Learn to craft dynamic scatter plots for advanced business intelligence in fintech. Dive into data-driven decision-making today!

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