Unit 6.2: Data Visualization (Charts, Graphs, Scatter Plots)

Introduction to Data Visualization

In the modern business landscape, organizations collect vast amounts of data every day. However, raw data in the form of spreadsheets or databases is often difficult to understand. Data Visualization is the practice of translating this raw data into a visual context, such as a map or graph, to make it easier for the human brain to comprehend and pull insights from.

For business professionals, this is not just about creating pretty charts; it’s a critical skill for making informed, data-driven decisions. By visualizing data, you can easily spot trends, identify outliers, compare different variables, and communicate complex findings to stakeholders in a clear and compelling way. It bridges the gap between technical data analysis and practical business strategy.

Detailed Content: Understanding Visualization Types

Choosing the right type of visualization is crucial for conveying the correct message. Different charts are designed to answer different types of questions. Here are some of the most common types used in business.

Bar Charts

A bar chart (or bar graph) is a chart that presents categorical data with rectangular bars, where the lengths of the bars are proportional to the values they represent. They are one of the most common and easily interpretable chart types.

  • What it’s for: Comparing discrete categories of data.
  • When to use it:
    • Comparing values across different groups (e.g., sales performance by region).
    • Tracking changes over a small number of time periods (e.g., quarterly revenue).
  • Types:
    • Vertical Bar Chart: Standard format, good for most comparisons.
    • Horizontal Bar Chart: Useful when category names are long.
    • Stacked Bar Chart: Shows how a larger category is divided into smaller sub-categories.

Example: A bar chart showing the number of new customers acquired by different marketing channels (Social Media, Email, SEO, etc.) in a quarter.

Line Graphs

A line graph displays information as a series of data points called ‘markers’ connected by straight line segments. It is most effective at showing the change in a variable over a continuous range, usually time.

  • What it’s for: Showing trends and changes over time.
  • When to use it:
    • Tracking a metric’s performance over a continuous period (days, months, years).
    • Comparing trends for multiple series on the same graph.
  • Example: A line graph tracking a company’s monthly website traffic over the past two years.

Pie Charts

A pie chart is a circular statistical graphic that is divided into slices to illustrate numerical proportion. Each slice’s arc length (and consequently its central angle and area) is proportional to the quantity it represents.

  • What it’s for: Showing the composition or parts of a whole.
  • When to use it:
    • When you want to show how different components make up 100% of a total.
    • Best used with a small number of categories (ideally less than 6) to avoid clutter.
  • Example: A pie chart showing the market share percentage of different smartphone brands in Nepal.

Scatter Plots

A scatter plot is a graph that uses dots to represent the values obtained for two different variables plotted along the horizontal and vertical axes. It is used to observe and show relationships or correlations between variables.

  • What it’s for: Identifying the relationship between two numerical variables.
  • When to use it:
    • To determine if one variable has an effect on another (e.g., positive correlation, negative correlation, or no correlation).
    • Identifying outliers in a dataset.
  • Example: A scatter plot showing the relationship between an employee’s years of experience (X-axis) and their monthly salary (Y-axis).

Business Applications Across Functions

Data visualization is not limited to one department; it provides value across the entire organization.

  • Finance & Accounting:
    • Expense Tracking: Using bar charts to compare departmental spending against budgets.
    • Revenue Analysis: Creating line graphs to track monthly recurring revenue (MRR) and identify seasonal trends.
    • Profitability Reporting: Using pie charts to show the contribution of different product lines to the total profit.
  • Marketing:
    • Campaign Performance: Dashboards with line graphs to monitor website traffic, conversion rates, and leads generated from a new campaign over time.
    • Customer Segmentation: Using bar charts to visualize customer demographics (age, location).
    • A/B Testing: Comparing the performance of two different ad versions with a simple bar chart.
  • Human Resources (HR):
    • Employee Performance: Using histograms to see the distribution of employee performance review scores.
    • Turnover Analysis: Creating stacked bar charts to show employee turnover rates by department and reason for leaving (e.g., resignation, retirement).
    • Recruitment Funnel: Visualizing the number of candidates at each stage of the hiring process (Applied, Interviewed, Hired).
  • Operations & Supply Chain:
    • Inventory Management: Using bar charts to monitor stock levels of different items in a warehouse.
    • Production Efficiency: Line graphs to track manufacturing output per hour or per day to identify production bottlenecks.
    • Supplier Performance: Visualizing supplier delivery times and quality ratings to make better procurement decisions.

Real-World Examples from Nepal

1. FinTech: eSewa/Khalti - Analyzing Transaction Data

  • Scenario: The management team at a digital wallet company like eSewa or Khalti needs to understand which services are most popular among its users to plan future marketing campaigns and product development.
  • Visualization in Action:
    • They use a pie chart or a bar chart to display the breakdown of transaction volume by service category (e.g., Mobile Top-up, Utility Bills, QR Payments, Bank Transfer).
    • A line graph is used on their internal dashboards to track the daily active users (DAU) and total transaction value over the past month or quarter.
  • Business Impact: This allows them to instantly see that, for example, utility bill payments peak at the end of the month. They can then run targeted promotions for other services during that high-traffic period to encourage wider use of the app.

2. Banking: NIC Asia Bank - Branch Performance Monitoring

  • Scenario: A leading commercial bank like NIC Asia Bank, known for its extensive branch network, needs to evaluate the performance of its branches across the country.
  • Visualization in Action:
    • Regional managers use a bar chart to compare key performance indicators (KPIs) like new account openings, loan disbursements, and deposit collections across all branches in their region for a specific quarter.
    • A scatter plot could be used to analyze the relationship between the number of staff at a branch and its total profit, helping to optimize staffing levels.
  • Business Impact: By visualizing this data, management can quickly identify top-performing branches to replicate their strategies, and underperforming branches that may need additional support, training, or resources.

3. E-commerce: Daraz Nepal - Sales and Customer Behavior Analysis

  • Scenario: An e-commerce giant like Daraz Nepal wants to understand sales trends during its major “11.11” sale event.
  • Visualization in Action:
    • The marketing and sales teams use a line graph to track sales revenue hour-by-hour during the 24-hour sale period to see peak shopping times.
    • They use a bar chart to visualize the top-selling product categories (e.g., Electronics, Fashion, Groceries) during the event.
  • Business Impact: This information is crucial for future planning. They can ensure server capacity is highest during peak hours, and they know which product categories to promote most heavily in the next big sale event, leading to increased revenue and better customer experience.

Key Takeaways

  • Data visualization transforms raw data into easily understandable visual formats like charts and graphs.
  • It is a powerful tool for identifying trends, patterns, and outliers, which is essential for data-driven decision-making.
  • The choice of chart is critical: use bar charts for comparison, line graphs for trends over time, pie charts for composition, and scatter plots for relationships.
  • Visualization provides actionable insights for every business function, including Finance, HR, Marketing, and Operations.
  • Tools for visualization range from accessible spreadsheet software like Microsoft Excel to specialized Business Intelligence (BI) platforms like Tableau and Power BI.

Review Questions

  1. Explain in your own words why a manager would prefer to see a line graph of monthly sales instead of a table with the same data.
  2. You are an Operations Manager for a manufacturing company. You want to show your team the percentage breakdown of production defects by cause (e.g., Machine Error, Human Error, Material Flaw). Which type of chart would be most suitable and why?
  3. Describe a business scenario where an HR manager in a Nepali software company might use a scatter plot to make a decision.
  4. What is the primary difference in purpose between a bar chart and a line graph?