Open data—making government data freely available for public use—promises to increase transparency, enable innovation, and improve public services. But realizing these benefits requires strategic thinking, not just data publication.
This guide provides a framework for developing and implementing effective government open data strategies.
Understanding Open Data Value
Why Open Data Matters
Transparency and accountability: Citizens can see how government operates.
Economic value: Entrepreneurs and businesses build products and services on open data.
Civic innovation: Community organizations use data for problem-solving.
Government efficiency: Agencies use each other's data more easily.
Academic research: Researchers access data for study and analysis.
The Open Data Spectrum
Not all data is equally open:
Closed data: Internal only, restricted access.
Shared data: Available to partners under agreements.
Open data: Freely available with minimal restrictions.
Open data typically means: machine-readable, freely accessible, openly licensed for any use including commercial.
Strategy Development
Identifying High-Value Data
Not all data warrants publication priority:
Demand assessment: What data do users want?
Impact potential: What data would create most value?
Feasibility: What data can be released safely?
Strategic alignment: What supports government priorities?
Stakeholder Engagement
Understanding user needs:
Developer community: Technical needs and format preferences.
Civic organizations: Community application requirements.
Journalists and researchers: Analytical needs.
Business users: Commercial application potential.
Internal users: Cross-agency data needs.
Program Goals and Metrics
Defining success:
Publication metrics: Datasets published, formats, updates.
Usage metrics: Downloads, API calls, unique users.
Impact metrics: Applications built, research conducted.
Compliance metrics: Meeting mandates and requirements.
Data Governance
Data Quality
Open data must be usable:
Completeness: Minimize missing values.
Accuracy: Ensure correctness.
Timeliness: Keep data current.
Consistency: Standardize formats and values.
Documentation: Explain data meaning and limitations.
Privacy Protection
Publishing data responsibly:
PII assessment: Identify personally identifiable information.
De-identification: Remove or obscure identifying data.
Re-identification risk: Assess combination risks.
Legal review: Ensure compliance with privacy laws.
Licensing
Clear terms of use:
Open licenses: Creative Commons, public domain dedication.
Attribution requirements: Whether source citation needed.
Commercial use: Clarifying commercial use is permitted.
Minimal restrictions: Fewer restrictions increase use.
Technical Implementation
Data Portal
Platform for publishing and discovery:
Search and discovery: Users can find relevant data.
Preview and documentation: Users can understand data before download.
API access: Programmatic access for applications.
Feedback mechanisms: Users can report issues and request data.
Data Formats
Machine-readable formats:
Structured data: CSV, JSON, XML.
Geospatial data: GeoJSON, Shapefile, KML.
API standards: RESTful APIs, standard specifications.
Avoid: PDFs, scanned documents, proprietary formats for data.
Automation
Sustainable publication:
Automated publishing: Extract and publish from source systems.
Scheduled updates: Regular refresh cycles.
Quality monitoring: Automated validation checks.
Reduced manual effort: Sustainability through automation.
Program Operations
Governance Structure
Managing the program:
Executive sponsor: Senior leader championing initiative.
Data stewards: Agency-level ownership.
Technical team: Platform and tool management.
Community management: User engagement and support.
Continuous Improvement
Evolving the program:
User feedback: Incorporate suggestions.
Analytics: Track usage and adjust priorities.
Community engagement: Regular interaction with users.
Program evaluation: Periodic assessment and refinement.
Key Takeaways
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Strategy before technology: Clear goals and priorities come first.
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Quality matters: Unusable data is useless data.
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Automate for sustainability: Manual processes don't scale.
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Engage users: Understanding needs improves value.
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Protect privacy: Responsible publication builds trust.
Frequently Asked Questions
Which data should we publish first? High demand, high impact, low risk. Start with data already public elsewhere or clearly public record.
What platforms should we use? CKAN, Socrata, and custom solutions are common. Consider existing state/federal platforms before building.
How do we handle data quality issues? Document limitations, improve over time. Some data is better than none if limitations are clear.
What about data that's collected but not ready? Distinguish between publication and quality improvement. Publish what's ready, improve the rest.
How do we engage the user community? Community events, feedback channels, developer outreach. Limited resources? Focus on high-value user segments.
What's the business case for open data investment? Economic activity, innovation, transparency, government efficiency. Difficult to measure precisely, but benefits are real.