Just completed the basics of visualization from DataCamp, and some of the lessons I learned will be useful.
Here’s the structure of basic visualization.
1. Understand what your audience needs
Understanding the audience means that you don’t need to take every data that you think matters for the page’s visuals. You just need to make your data :
The image is a representation of how the data focuses on the same scope of understanding.
it has the same filter applied for all visualization:
- year: 2021
- product category: Tank Tops
The focus on finding the insight is in the same scope or area. It focuses on looking for the best product from a certain matrix:
- order quantity
- sales amount
- average product price
- cost of goods sold
Some insights we can easily take from the visualization:
Insight from the table visual:
The product with the highest order quantity and sales amount in 2021 and in the category Tank Tops was MT12-M-blue.
Insight from scatter plot chart:
The dimension of the chart is date, by seeing the visual we can easily notice wich date is 2021 for tank top product category is the highest cost of good sold and sales emaount.
If there any day were having high sales amount with low COGS it will be high beneficial insight for the company.
Insight from the bubble chart:
The size of the bubble is the order quantity. By seeing the visual, we can easily conclude that the product in the 2021 category of I Tank Top had a good average of product price, high sales amount, and a large size of order quantity. In this case, Primo Endurance Tank met all the matrix.
2. Getting an Emotional Response
After you understand who the users are, and give more relevant data so the important part if give them the detail information.
in my experience, getting detail in in insight it doesn’t mean you break It down to really low level. You have to choose among all of the available data, wich one is tickeling you by the insight it bring and potential business improvement.
After we’re getting product information from previous process, then one of most relevant and low hanging place to be evaluated was the distribution.
- Which one of the stores has the most performance?
- Which one of the stores did not perform?
- Does the performance of the product vary in each store?
It will pull the attention of stakeholder related relevance room to improve and validating the data with comparison in same dimension.
This deepdive analysis detail dimension colour and and gender. This analysis simply can clarify and evaluate at the same time before we moving to the insight.
3. Reduce noise in visuals
We as humans have some sort of bias, especially limitations of the human brain to digest information.
What we want to highlight, we don’t want the users lost by focusing to the wrong important aspect and getting lost by a too much information that actually not really matter the the conclusion.
Every data in this part was considered using the same metric, as it is seen that the visualization was focused on revenue, COGS, and profit, then it was broken down to the month and each product.
To make sure the data was related and had less noise, all the visuals represent 2021.
The same principle was applied at this visual page, by making sure the data was connected to one another.
4. Less visualization with more clarity
By we understand
- Who our users are,
- delivering what really matters to them,
- help with suggestions and conclusion,
- Now we can add the highlight to the visualization.
The visualization we use can be optimized by using style or conditions.
This allows us to highlight the specific information we want users to focus on, which will make it easier in terms of communication with stakeholders.