How can you apply data visualization in any profession?

Data Visualization can be applied to any profession that produces relevant data. Firstly, understand the business domain around such profession. Then, research on key metrics that drives businessness or the activities to help churn out valuable insights from available data. Finally, produce detailed visualisation reports of the dataset available.

Data visualization is important because it helps people understand information more easily. By turning data into pictures, graphs, or charts, it becomes clearer and simpler to see patterns, trends, and relationships. This can be useful in many areas like business, healthcare, and education. With data visualization, people can make better decisions, spot problems faster, and communicate ideas more effectively.

This is a broad topic. Let me take it from a narrower scope. Data visualization isn't suitable for every profession or every data-related task. Don't visualize data just for the sake of visualization. In some instances, an article or a narrated video might convey the story more effectively. How can you determine when and what type of data visualization is needed? —> ChatGPT can help. ChatGPT's Advanced Data Analysis plays a significant role. Any paid user can utilize it to develop a holistic approach to data visualization and witness its application. Last but not least, just be aware that ChatGPT is not for analysis of sensitive or private data. There are alternatives and workarounds.

A key skill in any profession is the ability to effectively communicate concepts to various audiences. Data visualization has the power to almost instantly convey key data concepts to help support your story. It can provide valuable context and help your audience remember the message you are trying to convey.

Research concludes that are brain is able to digest more than 36.000 visual messages on average per hour, in comparison with only 15.000 words we can read on average per hour. It simply means we are more keen to comprehend visual results faster than textual. Think of it as driving in a car, when you would need to read whether the car in front of you wants to turn left or right, instead of having the visual communication of a blinker (even though some drivers don’t know what this is ??) The essence of data viz is to facilitate understanding and where the visualizer is in charge of providing the right take away to the reader. Using data visualizations speeds up the process of the take away, which people crave in this fast moving world.

A few tips to keep in mind to make your visualizations less complex and challenging! ?? ??Data is garbage in garbage out: If you want to avoid a misunderstood data visualization, do proper checks for your data. Ask yourself, are these the right metrics to bring insights on this topic? ??Choose the right charts: line charts for trends and bars for categorical data. Pie charts should not have more than 5 for a breakdown e.t.c ??Avoid information overload: Don’t try to show everything the dataset has. Display what’s important! What matters the most to the business? Focus on conveying the most important insights. Simplicity is key here. Hope this helps!

Data visualization comes with a set of challenges. - The interpretation of complex data. Striking a balance between making data understandable and avoiding oversimplification can be tricky. - Data overload is a common issue, as organizations collect vast amounts of data, and selecting the most relevant information for visualization is critical. - Choosing the right type of visualization, such as charts or graphs, is another challenge, as it greatly influences how data is perceived. - Data accuracy is fundamental; ensuring data quality and precision is vital to prevent misleading visuals. - Adapting visualizations to different audience levels of data literacy can also be demanding, as not everyone interprets data in the same way.

The data quality aspect stands out as almost 60-70% time is consumed on dealing with missing or inconsistent data. The other important aspect is the mindset. Often we do data analysis with an agenda already established in our mind and we are using the data visualisations to support that.

There are two main challenges. The first one is not to mislead the reader by providing an inaccurate graphical representation of data. Think about the axis of the bar chart not starting at zero, or using a diverging colour scale when the measure is not really diverging. We want to show things, the point is to show them correctly. The second one is keeping the focus on data. I am tempted to add more graphical elements like icons, images, and labels to make the visual more eye-catching and "easier to read", but every time I need to think - aren't those additions just distracting - making it more difficult to understand the underlying message in the data? Striking the balance between accuracy, readability and visual appeal is a real challenge!

Even while data visualization is incredibly beneficial, there are still difficulties involved. First of all, it can be difficult to choose the right kind of representation to accurately depict a given set of data because misalignment can mask important information. It can take time to manage and clean data for visualization because there may be problems with data quality that need to be fixed. Another problem with visualizations is striking a balance between complexity and simplicity so that complicated information is presented in an intelligible way without being oversimplified. In terms of inclusive design, it might be difficult to guarantee that visualizations are readable by all audiences, including people with disabilities.

The term "Less is more" goes a long way to help you design effective visuals that can help you convey your results to your varied audience. Three key things I would urge you to pay attention to when designing data visuals are: 1. Emphasize the essential data: Focus on the core data points, and concentrate on delivering clearer insights. 2. Understand your varied audience: Recognizing your different audience groups will help you determine the type of visuals you will use to effectively communicate your results. 3. Often overlooked but crucial is the accessibility of your data visuals. It is important to use color schemes and fonts that ensure easy comprehension for all viewers, promoting inclusivity.

The effectiveness of any design process (including data visualization) depends on the end users we're designing it for, whether that's a colleague or the general public in news media. Tableau has this fantastic white paper about how to design visuals effectively based on how humans perceive concepts visually. At the top of this list is order, like ranking the categories in a bar chart from largest to smallest. Second, we want to think about color, like how we can use them to separate key categories or make callouts. Third, we want to think about size, like how we can use the length of bars to communicate magnitude. Fourth, we want to think about shape, like differentiating data points with different markers or using shapes as labels.

I would ask 3 questions to assess the effectiveness: 1. If you showed the visualization to a random person on the street, would they understand its content? 2. Can your audience grasp the main message from the visualization in a single glance or within 10 seconds? 3. When displayed on a mobile screen, are the key elements of the visualization or dashboard clearly visible? If the answer to all these questions is "yes," then congratulations, your data visualization is effective regardless of your design skills!

Instead of showing all the information in one graph, try to make multiple graphs and show them on a dashboard. Also put the graphs on a dashboard in a top down order meaning the summary graphs should be shown at the top followed by the detail graphs.

?Accessibility myst be catered for all report cosumers. Choose your themes and colours carefully. ?Mobile device friendly, can the report be consumed "on the go" ?Does it answer the applicable business questions- does someone in another department know what the report is about (thus not too technical and/or easy to use) - a factor which will drive report adoption as well. ?Did you follow recommended Data Visualization best practises i.e. "Kiss method" or "Less is more"

There's a lot of flexibility in data visualization too and we can certainly use more than one tool for the job at hand. I'll ask first whether this is an ad hoc request or a frequently updated one. Excel still remains one of my favorite tools for ad hoc requests! BI tools and languages that let us build interactive and dynamic dashboards that automatically refresh are better for requests that require frequent updating, both for the visuals and the underlying data behind them. While BI tools limit how others can change the model, Excel doesn't have those safeguards in place. We certainly don't often need to give the end user the entire Excel file or code to produce the visual. Instead, we can save the graphic directly to embed in slides.

??? (label for human-generated content) There are thousands of data visualization tools, but I still prefer spreadsheets such as Excel or Google Sheets for three reasons: 1. Anyone can open the file. 2. They're perfect for handling small data sets. 3. They offer rich functionality. Yes, many people, including me in my early career, have complained about spreadsheets. However, with AI in Google Workspace and Microsoft 365, I believe most pain points will soon be a thing of the past! The only thing you should be prepared to deal with are new pain points brought by AI, e.g. hallucinations.

Common data visualization tools offer diverse features for creating compelling visuals. Begin by selecting the right tool like Tableau, Microsoft Power BI, or Python libraries. Import your dataset and explore its variables. Choose appropriate visual types such as bar charts or scatter plots. Customize visuals by adjusting colors and labels. Add interactivity if needed. Share your visuals and iterate based on feedback, utilizing learning resources for mastery.

I think knowing basic tools such as advanced MS Excel is enough to start visualization. The first step is to have data in a structured format, which can be used to create dashboards and derive the required business insights. Once, basic data is cleaned, transformed and finally modelled for visualization, we can expand to advanced tools. In complex projects, a single Excel sheet may have 20 different tables which may be interdependent and need to be visualized appropriately. Once, you have used basic tools, you can explore other common tools as per needs. Power BI and Tableau are becoming staples in the industry because of their ease of use and wide variety of visuals.

There are several advantages to learning and improving your data visualization abilities in a methodical manner. Begin by familiarizing yourself with the fundamentals of data visualization by reading books, taking online classes, or following tutorials. Working with data on a regular basis and experimenting with various visualization tools like Excel, Tableau, or Python libraries like Matplotlib or Seaborn are important since practice makes perfect. To obtain ideas and criticism, consult professionals and practical examples for inspiration. You may also want to participate in data visualization forums and groups. Peer assessments and constructive criticism can help you improve.

There best way of learning it is practice and feedback. Data visualization is a lot like visual design - you just need to train your eye to "see" which visuals work best, and which ones are the most appealing. Also mastering the tools you use (during practice!) helps a lot because then you spend less time thinking about how to tweak some parameters - more on how the visual should look like. But without feedback this will just be harder to achieve. Post your creations online, participate in challenges and see what attracts people and makes them understand the topic! Reading books, and blogs helps a lot, but only when you try in practice what you have read.

Look at the work of Edward Tufte. He is the master if data visualization. Take one of his courses - it changed my professional life.

By doing some tools courses and practising. For example, I was using Power BI for my Data visualization project already. However, when I did the course I could apply different tools to create design which was meaningful psychologically and better presentable. With practice, my speed for creating new data visualization dashboards from scratch has increased. With new updates to the tools, I can use different plugins and AI analytics to create visuals.

Data visualization benefits various professions by making complex data understandable and actionable. In business, it aids in analyzing market trends and financial performance. Healthcare professionals use it for patient statistics and disease patterns. Educators apply it to monitor student performance. Scientists, engineers, and IT experts visualize experimental data and system analytics. Government and public policy makers use it for demographic and policy analysis. In media, it helps in illustrating news trends and statistical information.