Data Visualisation : Flashcards

Data visualisation supports decision-making by helping people see patterns, trends, and relationships in the data. It uses charts, graphs, and dashboards to make information easier to understand quickly. For example, a sales dashboard in a company can show a bar graph of monthly sales. If sales are dropping in one region, managers can see this and take action. A line graph showing website traffic over time can help a marketing team decide when to launch a new campaign. Without data visualisation, the raw numbers would be hard to understand.

An effective dashboard has three important features:

1. Clear and simple layout – The dashboard should not be too crowded. It should show important information first, so users can find what they need quickly. For example, KPIs (Key Performance Indicators) should be at the top.

2. Real-time data updates – The dashboard should update as new data comes in. This helps decision-makers act fast. For example, if online orders increase suddenly, staff can prepare for faster deliveries.

3. Interactive elements – Users should be able to click on charts or apply filters to see different details. For example, they can filter sales by product or region. This helps managers explore the data without needing a data analyst.

Advantages:

• Easy to understand: Visuals like graphs and pie charts make complex data simple for everyone in the business.

• Real-time insights: These tools can connect to live data sources, so managers can see current performance.

• Better communication: Visuals help teams understand problems and make group decisions quickly.

Disadvantages:

• Needs training: Some users may not know how to use the tools or read the graphs properly.

• Cost: Tools like Power BI may need paid versions for full features.

• Data overload: Too many visuals or complicated dashboards can confuse users and hide important details.

Answer:
Poor data visualisation can negatively impact decision-making in enterprises in several ways:

• Confusing visuals: If graphs are too complex or not labelled properly, users might not understand the data. For example, using 3D pie charts or too many colours can mislead the viewer.

• Wrong conclusions: Misleading visuals can lead to wrong business decisions. For instance, if a graph hides a drop in customer satisfaction by using the wrong scale, managers may not fix the issue in time.

• Wasted time: If users cannot understand the dashboard easily, they spend more time asking questions or waiting for help from IT or data teams.

For example, a retail company using a dashboard with incorrect sales graphs might order too much stock of the wrong product. In conclusion, poor visualisation can cause mistakes, delay actions, and reduce trust in the system.