Apps software don’t directly provide you with food, water, shelter, affection or sex – the only thing they do is disseminate information, which can lead to all sorts of muscle needs. version above.
Therefore, graphic design, UX, software, reality always involve selecting and organizing information, over time. Information is everything, you need to know what it is, what it means? As well as how to show it honestly, comes with specific features, thereby becoming a really useful asset for the company or society.
Designers are not scientists, mathematicians, economists, or even rigorous researchers – perhaps that sounds fine. But in my opinion, most of them may have never-seen Excel spreadsheets since their last high school accounting class, and this is a huge problem that is plaguing the industry.
In most stores or companies I’ve seen, alongside dozens of products I’ve made or advised, designers often have to visualize, or simply assume, a chart or bar chart. that they think users should read. As someone with a lot of experience in both decomposition, computation and visual representation – I can claim that the lack of basic statistical knowledge would be detrimental to the design process. your products, especially in the information age. This fear or lack of understanding is especially evident in the multitude of digital products that have dashboards with no feature attached. Do you have a software product? Let’s get it a dashboard now!
Dashboard can be understood as a digital control panel (digital control), or a digital interface used to collect and synthesize data of the entire system. Dashboard not only provides in-depth data on the production and business process, but also gives an overview of the productivity of each department, trends, activities, KPIs (Key Performance Indicator – also known as the performance evaluation indicator).
Understanding the data you are designing will allow you to present them in a way that is consistent with end-user functionality. It will force you (the designer) to become the analyst. See it as a form to learn and immerse yourself in the element of ethnography (Ethnography).
What is Ethnography?
Ethnography is a research activity aimed at understanding cultural behaviors and customs in natural and everyday situations by participating in activities with candidates for research. This method is used by anthropologists to study cultural groups. However, some people who do research work also often use them to understand the living habits and consumption of the respondents. The data is collected mainly through observations, informal conversations or formal interviews.
Instead of just picking out a few high-level metrics and simulating them on bar charts with nice colors or fonts, force yourself (or your designer) to read the data first and determine if they are possible. show what. What’s important for your casual user? Is the sample size enough? How does this depiction give someone a specific power to fix the problem involved – to take next steps? What will this look like with different sets and prototypes? All of these questions are important when choosing a particular visualization technique. Again, designers need to be responsible for selecting and organizing information – indeed. If you don’t grasp many of the methods for numerical representation, your solution will become more rudimentary.
How to start working with data
As prototype tools like Sketch, XD, and Figma are used more and more popular – designers tend to move away from tools like Adobe Illustrator. All of the aforementioned new generation tools are very simple to learn, but they often lack Illustrator’s rough functionality as a powerful math rendering engine. Especially the ability to create visual thematic maps (choropleth) while layering and scaling data. When it comes to information design, Illustrator excels at all.
What is Choropleth map?
Choropleth Map is a type of thematic map in which areas are shaded or scaled to a statistical variable representing a summary summary of a geographical feature in each area, such as population density or income per capita. This is one of the data visualization tools useful in many situations especially for the researcher.
Many years ago, I worked for an urban research fund that was heavily focused on data visualization. So I worked with a lot of raw CSV files – importing, analyzing the data and trying to figure out how to make them more honest and efficient. Then, I was forced to learn Excel and suddenly started enjoying the whole research process.
I get excited when I enter data, render the chart so I can see all it has to show. Learning how to decompose, read, and visualize data is a unique skill that will advance anyone’s career as a functional designer. It also taught me the importance of rigor in research, besides the scientific method, which is extremely valuable to me as an active in software where the ego can advance. and well-developed social prejudices. In other words, the proper research methods have highlighted the rudeness of UX.
The beauty of Excel is that it works amazingly well with Illustrator. You can create charts in Excel, change fonts to AI-compatible fonts, paste them into Illustrator, then detach loads of clipping masks. This leaves you with fully editable vectors, which you can easily manipulate as you like.
This technique is also well-suited for classifying data to produce rich visuals that are perfect for analysts. Big data always requires great visualization, and Illustrator has the power to provide you with a canvas for that. Remember that ‘the designer of information’ (UX / UI / product / graphic) is responsible for the accuracy of the data so that people can make decisions more efficiently. These are the people who make billion-dollar decisions based on how they grasp the problem, so the person who organizes them has a great sense of responsibility. Furthermore, working with data forces designers to become analysts as well as users.
Analytical experience is a process that comes together – looking at different variables to understand relationships, patterns, and outliers. Analysts are people who are constantly moving their eyes. Design rendering tools such as Adobe Illustrator (using blend and eyedropper tools) allow you to create stunning visual thematic images where one can “layered” the data, helping different graders. It is easier for people to gain insight into these relationships.
Illustrator is also great for analyzing long trend lines as users can set up their charts along with a sizable canvas, edit visual elements, easily annotate as well as submit. Graphs for ‘teams’ to review. It’s also great for physical printing and for placing large visual images on walls for analysis or annotation. Furthermore, it is the designer’s duty and responsibility to allow analysts to compare figures easily without overcomplaining the canvas. Visual complexity leads to cognitive chaos.
Realistic data will teach you how to do your research properly and closely
UX fields in general are often overblown by processes driven by hearing – speaking, ego, and group thinking – not hard numbers and data. We often use qualitative feedback to justify design decisions when in fact, in most cases the sample size is too small to tell which perception is “true”. The future of design will be focused on experimentation and evidence, not ego-based qualitative research. Although the ego is essential to formulate a hypothesis (or null hypothesis if you are doing it right), assuming or following an idea is improper unless there is at least 1 -2 proof of its authenticity. Furthermore, exposing you (or your designer) to real data in real situations will certainly provide insight into strategy, processes and products. Dear designers, learn how to use Excel and feel free to use it.
Design is the manipulation of stimuli and information. As products become more complex, sound information design separates the successful ones from the weak ones. Not only does the layout, style, color or texture need to work, but the representation of the data itself needs to be suitable for deepening the analysis. If you have created complex charts before, you may understand how difficult it is to harmonize the different variables that a chart includes. It takes time, patience as well as a willingness to query to the end.
With the advancement of artificial intelligence and machine learning, humans may think we may end up without analysts. Computers and the system will make all of these decisions for us. This is a serious mistake made by strong advocates of artificial intelligence. Humans will and forever want to become the race of autonomous decision-making. Even as today’s AI starts delivering super-accurate decisions about buying behavior, we will still need to validate these recommendations, as free will is one of the highest values and virtues. most human, regardless of whether you think it exists or not. Until the very end, the human race is still decisive.
Translator: Nam Vu
Author: Jeff Davidson
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