How Open Data Editor improves our local government data practices

In Nepal’s local governments, data is everywhere – but it’s rarely in one place, clean, or easy to use. Public datasets are often scattered across departments, stuck in PDFs, spreadsheets, or legacy systems, and riddled with inconsistencies. These challenges don’t just create technical headaches; they delay service delivery, reduce data trust, and hold back transparency.

To address this, Open Knowledge Nepal (OKN) piloted the Open Data Editor (ODE) with support from the Open Knowledge Foundation. Our goal was to help local governments audit and clean their data more effectively and foster long-term ownership of data practices from within.

The Problem: Siloed, Messy, and Hard-to-Use Data

In our work with five local governments through the Integrated Data Management System (IDMS), we encountered common challenges:

• Datasets stored in varying formats (PDFs, legacy systems, surveys)
• Structural inconsistencies (missing headers, mismatched rows, duplicate columns)
• Content-level issues (blank fields, wrong data types, mixed Nepali-English values)
• Limited data literacy among staff, with small IT teams focused more on hardware than data

Manual error-checking could take weeks, often involving external support. And worse, low-quality data reduced trust in digital platforms entirely.

How We Used ODE to Strengthen Local Data Workflows

During our pilot with Tulsipur Sub-Metropolitan City, we used ODE to review and clean over 100 datasets on topics like population, education, health, infrastructure, finance, and more. These datasets were critical for planning, budgeting, and service delivery. The tool was integrated directly into the municipality’s existing IDMS workflow, where it provided immediate and visible benefits. Here’s how ODE transformed our approach:

Automated Data Audits

ODE allowed us to check datasets for structural and content-level errors, like:

• Missing headers and empty rows
• Inconsistent numbers of cells per row
• Wrong data types (e.g., text in numeric fields)
• Duplicate column names and blank required fields

Visual, Intuitive Editing

Municipal staff could see problems highlighted in the interface, making it easier for even first-time users to recognize and fix issues. With ODE, they could:

• Edit field names and data types
• Apply constraints (like “required,” “minimum value,” or “pattern”)
• Clean datasets without writing any code

Embedded in the Municipal Workflow

We didn’t just test ODE in isolation – we built it into the city’s data process:

• Automated checks during data uploads improved ongoing quality control
• Metadata standardization helped clarify ownership, structure, and meaning
• Capacity building allowed local IT staff to reduce dependence on external data fellows

Key Learnings Along the Way

As we pilot ODE, a few lessons stood out:

• Localize training resources early for easier adaptation
• Start small – begin with simpler datasets before scaling
• Introduce data before metadata to build understanding step by step
• Encourage self-discovery during training, too much information at once can overwhelm new users

Making ODE Better: Insights and Recommendations

As we used ODE in real-world environments, we also took notes on usability challenges and enhancement ideas. These insights were shared with the ODE team and are already helping shape future updates.

Here are our key recommendations:

• Bulk Error Fixes: Add the ability to fix all similar errors at once (e.g., filling blank cells in a column with default values). This would save time during large clean-ups.
• Performance Optimization: For large datasets (hundreds of columns), offer a preview-only mode to avoid browser crashes.
• Downloadable Error Logs: Let users export machine-readable error summaries in CSV or JSON – useful for documentation and accountability.
• Smart Suggestions: Automatically recommend fixes for minor issues like extra spaces, inconsistent date formats, or capitalization.
• Version Tracking: Add version history or changelogs so teams can review edits and undo mistakes if needed.

Collaborating with the Open Knowledge Foundation

This pilot wouldn’t have been possible without the guidance and open collaboration of the Open Knowledge Foundation (OKFN) team. From day one, this wasn’t just an ODE deployment and piloting – it was a co-learning journey. The OKFN team supported us through regular coaching calls, hands-on walkthroughs, and technical engagement, helping us understand the tool’s potential and push its boundaries.

We didn’t just report bugs or usability issues – we actively co-designed improvements based on frontline realities. Our team’s context-specific insights from Nepal, such as the importance of local language support, simpler metadata workflows, and guidance for first-time users, helped shape conversations.

This kind of global-local collaboration, between toolmakers and practitioners, is exactly what open source civic tech needs to thrive in diverse governance systems.

What We’ve Achieved So Far

Through this pilot, the Open Data Editor proved that data quality and usability can be dramatically improved without high-end infrastructure or complex programming.

Here are some of our key achievements:

• Accelerated Audit Time: We reduced the time required to review and clean datasets, thanks to ODE’s intuitive interface and real-time feedback.
• Audited Over 100 Datasets: The tool helped us review and improve datasets across sectors, critical data that directly impacts policymaking and public services.
• Empowered Local Staff: By providing localized training and a translated user guide, we enabled municipal officials to take ownership of data validation. This shift builds long-term capacity within government institutions.
• Localized Learning Materials: Translating the ODE documentation into Nepali broke a major barrier to adoption. It allowed the tool to be used meaningfully by staff across the municipality’s departments.

This wasn’t just a success in terms of outputs; it was a cultural shift. People who once feared spreadsheets began to explore, engage, and ask better questions of their data.

Looking Ahead: Scaling and Sustaining Better Data Practices

The successful pilot of ODE is just the beginning. We now see an exciting opportunity to embed better data practices across more local governments in Nepal, and potentially share this model with partners in other countries facing similar challenges.

Here’s what’s next:

• Scale to more municipalities: We plan to introduce ODE to other local governments already using the Integrated Data Management System (IDMS), especially those struggling with data consistency and reuse.
• Publish Open Resources: We will publicly release the Nepali-language user guide, training slide decks, and learning resources to support other municipalities and civic actors in adopting ODE.
• Foster Cross-Learning: We’re exploring partnerships with civic tech and open data communities across South Asia and the Global South to exchange practices, co-develop features, and promote interoperability.
• Advocate for Tool Adoption: We will continue to advocate for the adoption of low-barrier, open-source tools like ODE in digital governance policy, especially in contexts with limited digital capacity.

Our ultimate goal is to build a stronger culture of data ownership, one where local governments don’t just store or report data, but understand, use, and improve it as a foundation for better decision-making, accountability, and service delivery.

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About the Open Data Editor

The Open Data Editor (ODE) is Open Knowledge’s new open source desktop application for nonprofits, data journalists, activists, and public servants, aiming at helping them detect errors in their datasets. It’s a free, open-source tool designed for people working with tabular data (Excel, Google Sheets, CSV) who don’t know how to code or don’t have the programming skills to automatise the data exploration process.

Simple, lightweight, privacy-friendly, and built for real-world challenges like offline work and low-resource settings, ODE is part of Open Knowledge’s initiative The Tech We Want — our ambitious effort to reimagine how technology is built and used.

And there’s more! ODE comes with a free online course that can help you improve the quality of your datasets, therefore making your life/work easier.

↪ Take the course: Learn how to use ODE

All of Open Knowledge’s work with the Open Data Editor is made possible thanks to a charitable grant from the Patrick J. McGovern Foundation. Learn more about its funding programmes here.