Why did Datadista decide to focus on this project?
Datadista’s goal was to develop a system that would help organise and recycle its multimedia content more efficiently. Over the years, the group had accumulated a large amount of material from various investigations: videos, photographs, graphics, maps, etc., but it lacked an organisational method to catalogue and access the content. The PluPro project allowed Datadista the opportunity to create a functional prototype tailored to editorial needs and workflow enhancement.
How did applying design-thinking principles help?
Datadista did not follow a traditional design methodology, instead applying some key principles. For example, iterative prototyping allowed Datadista to adjust both the database model and the metadata taxonomy based on real-usage cases. Collaborative sessions between team members and input from an expert mentor aided in aligning technical development with practical editorial needs. Although user testing is planned for the next phase, the expectation of feedback has already influenced many of Datadista’s decisions.
Did Datadista’s approach change engagement with its audience in any way?
The tool is internal, so its impact on the audience is indirect. By helping Datadista efficiently recover and reuse archived content, the repository supports faster and more relevant publishing cycles across Datadista’s social media, newsletter, and other editorial outputs.
What challenges did Datadista encounter and how were they addressed?
Datadista faced several challenges throughout the process: defining a flexible taxonomy to catalogue multimedia material stored across different environments and hard drives; adjusting the database schema as needs evolved; and changing the core technology midway through development. Additionally, the group discovered that some tools and software did not produce the expected results, which led to a refinement of its production guidelines in which iteration and testing were key.
How was the project received by Datadista’s audience?
The tool Datadista developed is not public, but its effects are visible in the reusing of content becoming more effective. Internally, workflow has been streamlined and externally, it contributes to a consistent and timely use of material in social media and in new productions.
What insights or lessons did Datadista gain from this project?
Datadista learned the importance of leaving room for iteration both in the taxonomy and in the choice of technology. The company also confirmed that open alternatives to proprietary tools can meet its needs while offering long-term sustainability and independence. Effectively grounding the tool in real editorial workflows was key to its adoption.
What are Datadista’s next steps?
Although Datadista may explore further development in the future, at this stage the tool is not conceived as a commercial product. Next steps include further automating workflows, integrating new AI-based scripts for classification and summarisation, and user testing. Datadista also plans to explore new integrations and progressively add more multimedia content from its archive.
What advice would Datadista give to other journalists or organisations considering a similar path?
Datadista recommends starting by identifying internal problems such as workflows and building simple prototypes to solve them. Using open-source tools when possible and involving both editorial and technical teams from the beginning make for a smoother process. And most importantly, leave room for change and test your assumptions early with real content.
Credit: Transitions, Journalismfund Europe partners for implementing the Microgrants for Small Newsrooms programme, produced this case study.
