Data visualization presents information in an easy-to-understand way for different audiences. It can help uncover patterns, connections and other a-ha insights hidden within your numbers.
Specific types of Data Visualization include Gantt charts and waterfall graphs specifically used for project management; bubble or scatter plots; and infographics that combine visuals with data points.
1. Data Analysis
Data visualization enables business people to turn the information that fuels their KPIs into accessible and visually compelling business insights for all stakeholders. It also provides a mechanism for sharing this data with the public or specific audiences without technical knowledge in a way that is universally understandable and readily consumable.
Visualizing data allows you to identify trends and patterns that aren't apparent when analyzing raw data. It is important to establish a clear purpose and the main question you want to answer from your analysis before creating any visualizations. This will guide you in selecting the correct chart type. For example, scatter plots are good for comparing two variables while line graphs are best for displaying time series data.
The use of text on visualizations is also important as it can either clarify or detract from the intended message. Text can be in the form of labels, a brief summary paragraph, titles or legends.
2. Visualization
Data visualization is the process of translating data into a visual context. This makes it easier for the human brain to observe patterns, trends and outliers in data sets.
For example, a heat map is used to communicate data points based on location in a geographical area (such as how different states voted in a political election). Dot distribution maps are another form of geospatial data visualization.
Similarly, correlation matrices are often used to help discover patterns among various data points. Data Analytics The color scale on the matrix helps illustrate how closely correlated two variables are.
When creating a data visualization, consider the goal and audience for it. This will impact the type of visualization that's best for the specific data set. For example, a scatter plot might be well-suited for relationship-based data while a line graph is ideal for time series data. Avoid clustering elements that don't truly relate to each other. This can confuse viewers and cause them to draw the wrong conclusions from the visualization.
3. Storytelling
The process of creating a story that uses data visualization is called "data storytelling". Data storytelling allows you to communicate your insights in a way that's memorable, persuasive and engaging for your audience. It can also help to close the knowledge gap between what you know and what your audience or organization needs to understand.
There are a number of visuals that can be used to tell a data story, such as infographics, bar charts, pie charts, tables and heat maps. The key is to choose the right visuals for your purpose and make sure that they are designed appropriately. For example, using a pie chart to compare more than three elements can cause the visual to become too cluttered and difficult to grasp.
It's also important to avoid any visual tricks that could bias the way that your data is interpreted. This includes things like blowing up certain data segments to make them appear more significant or starting a graph axis at a different value than zero.
4. Communication
Good communication is vital to any career, and data visualization is a powerful tool for conveying complex information in an easy-to-understand format. It can help bridge the gap between quantitative analysis and decision-making, allowing you to share your findings and conclusions with colleagues and external stakeholders.
Before creating a visualization, it's important to understand your audience. This can help narrow the focus of your data exploration and determine what type of chart or graph to use. For example, a hierarchical visualization might be appropriate for displaying the relationships between different datasets, while a network diagram might better illustrate how qualitative data points are connected.
It's also critical to avoid visual "tricks" that could mislead or bias how data is interpreted. This includes things like bloating data segments to make them appear more significant or starting your graph axis at a non-zero value. Remember that, as the French writer Antoine de Saint-Exupery said, "Perfection is achieved not when there's nothing left to add, but when there's nothing more to take away." (link to workshop slides and additional readings)
