Main Rules of Data Visualisation

Enhancing Decision-Making Through Effective Data Visualization


The ability to clearly communicate complex information is crucial. Data visualization is not just a tool for making data more accessible; it is a critical component of business strategy that influences decision-making and operational efficiency. This blog post outlines key practices that professionals can use to improve their data visualization techniques, thereby enhancing comprehension and facilitating better business decisions.


1. Prioritize Simplicity

In data visualization, simplicity is key. A common pitfall is overcomplicating a graphic with excessive elements that do not add informative value. To maximize clarity:

  • Use Clean Layouts: Avoid clutter by minimizing the use of heavy text, excessive colors, and intricate patterns.
  • Focus on Key Data: Highlight the most important information to draw attention to critical insights.


2. Select Appropriate Visuals

Choosing the right type of chart or graph is pivotal in conveying the correct message:

  • Bar Charts: Ideal for comparisons between quantities.
  • Line Graphs: Best for showing changes over time.
  • Pie Charts: Effective for displaying proportions and percentages.
  • Scatter Plots: Useful for depicting relationships and distributions. Each chart type has a specific function, and using the right one can significantly impact the viewer’s understanding.


3. Maintain a Professional Color Scheme

The colors used in your visualizations should enhance the viewer’s ability to understand the data:

  • Contrast for Emphasis: Use contrasting colors to highlight significant data points or trends.
  • Consistent Branding: Apply your organization’s color palette to ensure consistency and brand alignment.
  • Accessibility: Ensure that color choices are accessible to all viewers, including those with color vision deficiencies.


4. Incorporate Interactivity

In digital environments, interactive elements can transform static data into a dynamic exploration tool:

  • Drill-Down Capabilities: Allow users to click on elements within the visualization to explore further details.
  • Tooltip Information: Provide additional context when the user hovers over certain data points.
  • Adjustable Parameters: Enable viewers to change variables to see different scenarios or outcomes.


5. Narrate a Data Story

Effective data visualization tells a story:

  • Start with Context: Set the stage by explaining the background and the data source.
  • Build the Narrative: Guide viewers through the data in a logical sequence that builds towards a conclusion.
  • End with Actionable Insights: Summarize key takeaways and suggest actions based on the data.


Conclusion:


The art and science of data visualization lie in transforming raw data into a compelling, insightful narrative. By adhering to these best practices, professionals can enhance their ability to communicate complex data effectively, fostering a more informed decision-making process across their organizations.


June 10, 2025
Will we ever speak with animals? Long before, humans were only capable of delivering simple pieces of information to members of different tribes and cultures. The usage of gestures, symbols, and sounds were our main tools for intra-cultural communication. With more global interconnectedness, our communication across cultures became more advanced, and we began to be immersed in the languages of other nations. With education and learning of foreign languages, we became capable of delivering complex messages across regions. The most groundbreaking shift happened recently with the advancement of language models.  At the current stage, we are able to hold a conversation on any topic with a representative of a language we have never heard before, assuming mutual access to the technology. Can this achievement be reused to go beyond human-to-human communication? There are several projects that aim to achieve this. Project CETI is one of the most prominent. A team of more than 50 scientists has built a 20-kilometer by 20-kilometer underwater listening and recording studio off the coast of an Eastern Caribbean island. They have installed microphones on buoys. Robotic fish and aerial drones will follow the sperm whales, and tags fitted to their backs will record their movement, heartbeat, vocalisations, and depth. This setup is accumulating as much information as possible about the sounds, social lives, and behaviours of whales . Then, information is being decoded with the help of linguists and machine learning models. Some achievements have been made. The CETI team claims to be able to recognize whale clicks out of other noises and has established the presence of a whale alphabet and dialects. Before advanced machine learning models, it was a struggle to separate different sounds in a recording, creating the 'cocktail party problem'. As of now, project CETI has achieved more than 99% success rate in identifying individual sounds. Nevertheless, overall progress, while remarkable, is far away from an actual Google Translate between humans and whales. And there are serious reasons for this. First of all, a space of 20x20 km is arguably too small to pose as a meaningful capture of whale life. Whales tend to travel more than 20,000 km annually . In addition, on average, there are roughly only 10 whales per 1,000 km² of ocean space , even close to Dominica. Such limited observation area creates the so-called 'dentist office' issue. David Gruber, the founder of CETI, provides a perfect explanation: "If you only study English-speaking society and you're only recording in a dentist's office, you're going to think the words root canal and cavity are critically important to English-speaking culture, right?" Speaking of recent developments in language models, LLMs work based on semantic relationships between words (vectors). If we imagine that language is a map of words, and the distance between each word represents how close their meanings are, if we overlap these maps, we can translate from one language to another even without pre-existing understanding of each word. This strategy works very well if languages are within the same linguistic family. However, it is a very big assumption that this strategy will work for human and animal communication. Thirdly, there is an issue of interpretation of the collected animal sounds. Humans can't put themselves into the body of a bat or whale to experience the world in the same way. It might be noted that recorded sounds are about a fight for food; however, animals could be interacting regarding a totally different topic that goes beyond our capability. For example, communication could be due to Earth's magnetic field changes or something more exotic. And a lot of collected data is labeled based on the interpretation of human researchers, which is very likely to be wrong. An opportunity to understand animal communication is one of those areas that can change our world once more. At the current state, we are likely to be capable of alerting animals of some danger, but actual Google Translate for animal communication faces fundamental challenges that are not going to be overcome any time soon.
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