Unlocking Insights with Sankey Charts: A Visual Guide to Flow and Connectivity Sankey charts have become increasingly popular in recent years as a tool for visualizing and understanding complex flow data. These charts use a series of interconnected, colored bands or “arrows” to represent the flow of data or resources between different nodes or categories. In this guide, we will explore the basics of Sankey charts and how they can be used to uncover insights in data that might otherwise be difficult to discern.
Understanding Sankey Charts Sankey charts are named after Matthew Henry Phineas Riall “Phineas” Sankey, a British engineer who used a similar visualization method in the 1870s to demonstrate the efficiency of steam engines. Since then, Sankey diagrams have been used in a variety of fields, from energy and environmental studies to business and finance. What makes Sankey charts so powerful is their ability to illustrate not just the total flow of data, but also the relative flows between different sources and destinations.
Constructing a Sankey Diagram When constructing a Sankey diagram, the first step is to define the nodes, which represent the starting points or endpoints of the flow. Nodes are usually depicted as rectangles or circles and are labeled to indicate the relevant category or type of data being considered. Once the nodes are in place, the relationships between nodes are mapped out as interconnected arcs that resemble the bands of a braid. The width of the arcs is proportional to the quantity of data flowing between the nodes, making it immediately clear which pathways carry the most or least amount of data.
Coloring the Bands One of the key features of a Sankey diagram is the use of color-coding to visually distinguish between different flows of data. Each node or category can have its own distinct color, while the arcs connecting nodes can be colored to match the source node or the destination node. In this way, it is easy to trace a specific flow of data from start to finish, and see how it is divided and combines with other flows along the way.
Interpreting a Sankey Chart Once an initial Sankey diagram has been created, the real fun begins. By scrutinizing the thickness, colors, and paths of the arcs, it is often possible to spot patterns that might not be immediately obvious from raw data. Here are a few ways to interpret the information presented in a Sankey chart:
1. Identifying dominant flows: Look for the wide arcs carrying a high volume of data, which indicate the most significant sources or destinations in the data set. These can provide insights into the core processes or transactions that underpin the entire system.
2. Tracking trends over time: If data spans multiple periods, you can overlay Sankey charts to see how flow patterns have changed over time. This is particularly valuable in understanding how shifts in demand, supply, or policy have influenced relationships or connections between nodes.
3. Analyzing efficiency: In some contexts, such as energy consumption or supply chains, Sankey diagrams can reveal where bottlenecks exist or how resources are being wasted. By identifying clusters of narrow arcs surrounded by wide arcs, you can pinpoint inefficiencies or losses that require attention.
4. Highlighting interdependencies: Sankey charts are also useful for illustrating how a large part of the system relies heavily on a small number of flows or nodes. If a main node is cut off or has its flow severely reduced, significant disruption or cascading effects can occur, making it crucial to address dependencies and redundancy in your system to ensure resilience against potential disruptions.
5. Improving decision-making: By demonstrating the scale and nature of the flow, Sankey diagrams can help decision-makers identify opportunities for improvement, restructuring, or cost optimization. This is particularly valuable when making strategic choices in competitive markets or allocating limited resources.
Creating Effective Sankey Diagrams To create successful Sankey charts, it is important to plan carefully and follow some best practices. Here are a few tips to keep in mind:
1. Prioritize clarity: In order to make sense of the data, focus on keeping the chart as simple and visually straightforward as possible. Avoid overwhelming the viewer with too many colors, layers, or nodes. It is usually better to start with a minimalist approach and add complexity only if needed.
2. Choose effective visualization tools: Use graphing software or online tools like Gephi, Tableau, or Plotly to generate Sankey charts. These platforms provide flexible templates, preset color palettes, and customization options that allow you to easily create, edit, and export your diagrams for sharing or reporting.
3. Optimize the layout: Arrange the nodes and flow arcs so that the chart is easy to read and visually appealing. For instance, ensure that the arcs do not intersect with each other or become cluttered, and use sufficient white space to separate the bands and make individual flows more distinguishable.
4. Use legends effectively: To help the viewer understand what colors or categories correspond to different parts of the chart, include an interactive legend. You can create a dropdown menu or a clickable index that defines each color or grouping, or use hover-over text or tooltips for a dynamic user experience.
5. Emphasize key metrics: For added context, consider overlaying other relevant data on the chart, such as percentages, values, or timestamps. Including these metrics in the form of text labels or annotations can help users quickly grasp the relative significance and meaning of each flow within the diagram.
By following these guidelines and utilizing the insights revealed through Sankey charts, you can make your data more accessible, engaging, and actionable. With practice and creativity, Sankey diagrams can become valuable tools for understanding and managing complex systems in a variety of disciplines, ultimately leading to better decision-making and optimization of resources within your organizations or communities.
