Hello everyone,
I’m currently working on a large-scale agriculture and plantation project (several tens of thousands of hectares). We will collect various types of data (related to staff, suppliers, and project activities). We are looking to set up an automated and robust data collection and analysis system using KoboToolbox.
Our main goals are:
- To have a database that updates automatically with every form submission
- To automate data analysis (even if setting it up is a bit complex)
- To provide managers with a simple, practical, real-time visualization of project progress (e.g., basic graphs showing the number of hectares plowed per day)
- To automatically generate daily or weekly summary reports
I find Kobo to be an excellent data collection tool, but it seems not well-suited for analysis (you can create forms that summarize other forms, but I find this quite limited), and also not ideal for flexible data storage and flexible visualisation.
My current idea is to use Python to:
- Call the Kobo API to fetch new data
- Automatically transfer raw data to a shared storage (Google Drive, as used by the client) so that it can be accessed and updated by multiple people
- Analyze the data: check for duplicates, errors, totals, sums, and perform other simple numeric analysis
- Set up a data visualization platform: for example, display the total hectares plowed per day — using Google Looker Studio for instance
- Automatically generate simple summary reports
- Use a task scheduler to run the Python script daily or weekly
This approach would allow us to use a minimal number of tools, with maximum automation.
My questions:
- Can you confirm that Kobo is not really designed for simple data analysis?
- What do you think of the approach described (Python + Drive + Looker Studio)? Have you used any alternative methods for handling data analysis in large-scale projects?
- Are there any advanced training resources for KoboToolbox (especially for API usage or external integrations)?
Thank you very much for any feedback, suggestions, or shared experiences!
Hi @nadsan2025,
Even though kobo is not fully suitable what you are trying to achieve, it is a great tool to support what your you are trying to achieve. You don’t need to work with python, drive or any other tool. I would suggest you to use Power BI, which you can directly import your data from Kobo to Power BI, Power Query and DAX support for your data analysis, by using the Power BI services you can set up schuladed refresh which can update your data and visual 8 times a day, or daily or weekly. Great and interactive filtering and data anlysis, row-based data security if needed. You can set it and forget it. and Kobo is a great tool for this set up, i have been using this kind of system for years
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Hello @osmanburcu, thank you for your response!
I have a few questions regarding your use of Power BI, if you don’t mind.
First of all, are you able to carry out your analyses with the free version, or is the paid version necessary?
Also, do you work with a large database? Is that manageable with Power BI?
Regarding the analysis, what type of data do you work with, if that’s not confidential?
Is the analysis in-depth, or is it more about presenting the results?
Many thanks !
Hi @nadsan2025,
First - As long as you have a outlook or work mail, you can use the free version, it would be more than enough for you, premium not highly needed for you.
Second - Power BI designed to work with large data bases such as millions of rows, i don’t believe it will have any problem with your dataset.
Third, you can do any type of analysis such time series analysis or correlation etc. The DAX is a data analytic language so i don’t believe you will have any problem, if you do, you can use python or R in the Power BI. and you can also visualize this python and R script results as well
This sounds like a solid approach. Kobo is great for data collection, but for large-scale analysis and visualization, combining it with Python and tools like Google Drive and Looker Studio makes a lot of sense. I’ve used a similar setup, and scheduling scripts with something like Airflow or even simple cron jobs works well. For API usage, Kobo’s documentation is a good start, and GitHub has a few helpful community scripts too. Would love to hear how your pipeline evolves!
