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Data analysis is one of the most important steps in the user experience design process. Sometimes this step can lead us to a confusing black hole of data. That will have no functionality and will be useless. In this section, we are going to get acquainted with the correct way of analyzing the data. Obtained from the research in 5 steps.

Step 1: Collect and organize data

In the process of collecting and organizing your data, use a method that is logical and manageable. For example, if your data is in the form of video clips, audio files, or paper notes. They should all be converted to digital formats. At this point, you need to think about how you can organize the data files based on the type of work you do and your research. Creating a separate folder for each person you interview can be useful and rewarding. After arranging your digital data space, it’s time to prepare and organize your physical environment and surroundings. Get an empty table, a blank wall, or a whiteboard, and make sure all your research is available on them.

Step 2: Refer to the research objectives

Once everything is in order, you are ready to start the analysis. But before that, you have to get back to your research goals. Why did you do this research? What was the purpose of this research? Did you want to develop empathy with your target users? Or did you want to know if your product meets the user’s needs or not? Reviewing the initial objectives of the research will be your guide and the process of analyzing the data. Will help you to select useful information to achieve the goal.

Step 3: Search and explore the data to find the findings

In the third step, you are going to look at all the research data. And results to get related patterns and stories. Remember that you are looking for anything that can help you achieve the main goal of the research. To better understand your research, encode your data in the first step (data collection and organization). This is a very useful way to structure data, especially qualitative data such as data obtained from interviews with users. . Let’s take a closer look at this method:

Data encoding

Suppose you are reading texts related to a user interview, whenever the user says something interesting and significant, highlight it and give the corresponding code. The code must be a description of what the user has said. Suppose you want to design a food ordering application and need to know what kind of food ordering services the interviewee has used in the past? How often does he use these services? And how does he feel about them in general? He assumes that he is not a fan of food apps, he has an account, but he uses it maybe only once a month, and he believes that such apps are not useful for him.

Therefore, concerning this person’s words, data these codes can be useful: #food ordering program #negative emotions

Now you can easily tag the user’s statements (if you use hard copies you can add codes at the end). Once you have encrypted all your data and information, what is in front of you is a relatively erratic set of codes that you have to group the same code into different categories. Going through this step, you have some interesting topics that you can use to interpret the data. For example, the “Food Order Program”, “Online Order” or “Order Order” codes can all be grouped in the “Food Order Services” group and are a subset of it. Remember that dividing data into “different categories” is a repetitive process. Be prepared to constantly move between categories to make possible changes to categories and data. I recommend writing down the codes and categories on small sheets to make this move easier. This is where that empty high wall comes in handy.

Writing dependency and closeness:

Another useful technique for grouping and understanding your data is to identify the dependencies and proximity of the data. After encoding your data, you write them on small sheets of paper, then select one of them as a starting point and stick it on the wall, and then start pasting the related sheets around it. . Treat any irrelevant tabs you see as a starting point. In this way, collections are created, each of which has a unique theme. Once the collections have been created, take several tabs and write a name for each collection to indicate what type of writing is in it and why they are in a group. Then paste it on top of the group.

Step 4: Recognize user insights data

In the previous steps, you spent time exploring and organizing your data. The fourth step is to fully integrate your data. Combining can be a process for structuring findings and insights and concepts based on the facts of your analysis. Before elaborating on the meaning of this step, it is better to look at the difference between “findings” and “insights”.

In the field of User research, the findings and insights we are talking about maybe interchangeable, but they are not a concept. “Findings” are facts and statements from your data that simply state what is happening but do not provide an answer as to why this happened or what solutions are available. But “insights” introduce us to all kinds of human behavior. They come from the heart of the “findings” and help us figure out how to solve a specific and unique problem. For example, if our research finding is that “the user is willing to use multiple language learning applications”, we can find that “currently no application that meets all of the user’s needs in one go It does not exist and that is why users need several applications to learn the language. “This is the insight that comes from the research findings.

Step 5: Share the findings and concepts (for team projects)

The last step in the data analysis process is to share your findings and understanding with other team members so that they can take action based on the same document you prepared in the previous step. You also need to talk to the project stakeholders about them. All team members need to be fully aware of what points and findings you have reached during the research data analysis process. When your findings are properly shared, they will have a positive impact on the performance of other team members, and in most cases, those findings can become a description of your problems; Problems that users are struggling with, and that you intend to address.

They can create questions in your mind that answering them will allow you to design better. For example, according to the example of the fourth step, we found that no application meets all the needs of the user, and therefore users need several applications to learn the language. This could spark a question in the mind of a design team: “How can we keep users satisfied with learning a new language in one place only?” Remember to always ask a good question so that a good answer will find you. Together with the team, you can see each of the findings as a user problem, and ultimately, the results of their review can be your design guide and show you what to focus on.