What is the best and most scientific way to organize all the qualtative data gathered during research? I think this is important because with easy to understand research and data, it will be difficult to create accurate personas and user scenarios. I started using affinity diagraming to organize data gathered during user research, but I was wondering if anyone else had any other ideas. I feel that another scientific approach would really he helpful.

For example, I may have pages and pages of word and excel docs filled with research data. What is the best way to organize this so it actually makes meaningful sense?

  • 1
    This is a good question, but you will need to provide some additional context on the type of data you have collected to get any specific answer. What is the data? How do you want to use it? How was it recorded (not on what medium - but at what frequency, etc.)?
    – rk.
    Commented Jun 27, 2013 at 16:06
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    Lets stay for example I am conducting user research for a credit card company and interviewing 8 users on their goals for using the creidit card website. Lets also say I did 5 contextual interviews to see exactly what users are doing while using the credit card website (i understand contextual interview for CC website is trickly, but lets just imagine). Now, I have pages and pages of data of user goals and activities, but how do I organize this info and make sese of it to stakeholders?
    – ih123
    Commented Jun 27, 2013 at 16:17
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    Are you recording their comments and responses or is it completely observed data?
    – rk.
    Commented Jun 27, 2013 at 16:19
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    in this case, lets say its completley observed.
    – ih123
    Commented Jun 27, 2013 at 16:21

5 Answers 5


This is a huge question and cannot be answered in a simple answer but here are some recommendations based upon personal experience and what has worked best for me

  • Stakeholders generally dont have time to go through sheaf's and sheaf's of data. Hence talk to them to find out what is the most important detail they want (taking your example in the comments, assuming its details about how much money do people spend on credit cards),
  • Once you have that information, work with them to find out what other details are they most important to them (like which credit cards are most popular, how do people pay their bills, what is the age group and so on)
  • Organize this list in order or priority
  • Take the data from your findings and put it into a presentation (Yes, powerpoint if done well can communicate information really well) or as an infographic since people assimilate information better if presented visually. I would recommmend the layout of the powerpoint presentation as this

    1. Introduction
    2. Problem statement
    3. Quick summary of the users interviewed ( 2 lines about each user)
    4. Key findings
    5. Common aspects which applied to all the users or most of them (I find that most business stakeholders find this very important as they like to know generally the crowd likes)
    6. A graphical or visual representation of your data (this is where an infographic is helpful)
    7. Any surprise\edge cases and its impact.e.g: Lets assume one of your expected target audience does not buy credit cards due to religious reasons but their buying potential is huge..so what can you do to reach out to them.
    8. Suggestions
    9. Summary

Here are some links which can help you analyze your user data

Analyzing data from field studies

Finding Gold in Your User Research Results

Analysis, Plus Synthesis: Turning Data into Insights

I also recommend looking at this excellent article from UX booth which proposes this method if you required to present a paper or document on your findings

  1. Brief description about the goal, methodology and participant demographics
  2. Notable findings
  3. High and medium-level issues (in a bulleted list) together with participant quotes (recommended) or even video snippets.
  4. Task Success Ratings and Metrics Studied (if applicable)
  5. Recommendations and next steps (if applicable)
  • the powerpoint layout is a great one.
    – ih123
    Commented Jun 27, 2013 at 16:46

Well, the answer to your question will be much broader than imagination because it depends on the type of data and its business relevancy. You can try by making a directory and using descriptive folders for detailed organization, or can save this in XLS or depending upon the nature & industry of the data you may opt some software as there are several software, which are avilable for data storgae, organization and managment.


Great question! Our team went through something similar recently. The affinity diagrams don't scale well. As the number of interviews went up, the affinity diagrams just became incomprehensible.

We recently on-boarded to a platform that provides the ability to do tagging and text analysis. We are using this platform as our central repository now. Tagging combines the benefit of qualitative (highlighted text) research with quantitative methods (tag frequency) and keeps insights organized with scale.


I'd say you can organize research data on different levels:

Organizing data within a research project A increasingly popular way to do that is to create tags to categorize similar information. The image below shows a sample text from an interview about travel preferences. The colors indicate highlighted information and you can see the tag on the right. The tag usually has a name that represents the respective category and/or a color.

Tagging research data

Organizing data across research projects The goal of that would be to store or archive data in an organized manner in order to find it again quickly when needed. Tools that allow to store research data are referred to as research repositories. You could use general purpose tools like Google Drive or Dropbox and there are tools built specifically for that like Condens.

Full disclosure: I'm one of the co-founders of Condens


This is actually a growing area of UX design that is probably out of the scope for the type of questions generally asked and answered on UXSE, but here are some useful links that point to various articles and groups in ResearchOps:

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