Introduction
Data visualization is an art, not a science. While there are many different ways that you can analyze and visualize data, the goal is to make your audience understand it by using a combination of visuals, colors and text. The most important part of data visualization is to communicate your message. If people don’t understand what your data means or how they should use it then they won’t be able to use it effectively!
How much data is being created?
We are in the midst of a data explosion. Data is being created at an exponential rate, and it’s happening across industries and sectors. Individuals, organizations and governments are creating data by the terabyte every day; sensors in machines are generating petabytes of information; social media generates exabytes (1 EB = 1 billion TB) of content every month.
The following visualization shows how much data has been created since 1990:
What are the types of data?
Data is a broad term that means different things to different people. For example, if someone says “I have a lot of data,” it could mean that they have a large amount of information or it could mean they have an extensive database full of data.
To be more specific, we can use the following terms:
- Raw Data – This is unprocessed information that has not been converted into another format such as text or numbers. It may come in various formats such as audio recordings or images (photos). The main purpose behind collecting raw data is so you can use it later on for analysis purposes (such as analyzing customer behavior). When you collect this type of information from customers in your business environment then there’s no need for any special equipment because most smartphones today already come equipped with cameras attached onto them so anyone can take pictures whenever needed!
Which data elements are most important?
Before you can visualize data, it’s important to understand the context of the information you’re working with. What is the purpose of your data? What do you want to achieve with it? How will this information be used by others in your organization or outside of it? Is this particular dataset applicable to a specific audience–and if so, who are those people and what do they need from their visualizations of this data set?
Once you’ve answered these questions and identified which elements are most important for others in order to make sense of their own conclusions based on what they see in front of them (or not), then we can move forward into how exactly we go about turning numbers into images that tell stories.
How can you present your data in a way that’s easily understood and can be communicated to others?
The answer to this question is that you need to use a variety of visual elements.
You should also use a variety of scales, encoding and interaction.
Finally, consider the context in which you’re presenting your data.
Don’t make assumptions about your audience.
Don’t make assumptions about your audience.
When you’re creating a visualization, it’s easy to assume that everyone knows what you are talking about. You may feel like all of the technical details and industry jargon will be lost on non-experts, so you don’t include them in the visualization. This can actually hurt your ability to communicate effectively with other people who may not share all of your knowledge base! If someone else doesn’t understand something in the graphic, they might think something is wrong with their own understanding instead of realizing that maybe there was some information missing from the graphic itself.
It’s also important not only for yourself but also for others working with data visualizations if possible (or even just using them) – because we often forget how different our skillsets really are until we try collaborating together on something new like this!
Data visualization is an art, not a science.
Data visualization is an art, not a science. There’s no right or wrong way to visualize data–the best way to learn is by practicing.
Visualizations can be used to communicate and share information effectively with your audience.
Conclusion
Data visualization is an art, not a science. You will never be able to predict how someone will interpret your data, so it’s important to keep an open mind when presenting it. If you want people to understand what they are seeing in your visualization, then make sure that any assumptions made by the audience are correct and avoid making them if possible!
