From Open Data to Open Infrastructure: What Municipalities Need to Get Right

Making data 'open' is no longer the challenge. Providing ‘usable data’ is. Based on my experience at Ratio.City and conversations with planning professionals across a variety of positions, this blog explores why access alone is not enough, how weak data foundations limit even the most advanced tools, and what municipalities need to get right next to make data truly work for planning.

Over the past several months at Ratio.City, I’ve had the opportunity to look at municipal data from a practical perspective. My work has involved auditing datasets, testing workflows, and speaking with professionals who have worked in both municipal government and planning technology for decades.

What began as a technical learning experience gradually became something else. This work has given me a clearer picture of where municipal open data stands today, and what still needs to happen for it to truly support planning practice. Open data is no longer a new idea. Most medium and large municipalities in Canada now publish some form of zoning data, development data, or infrastructure information online. The real question is no longer whether data should be open. The question is whether it is usable and structured in ways that support the realities of modern planning. To better understand how this gap might be addressed, I spoke with multiple professionals working across municipal government and planning technology. These included Mr.Ridley Soudack, Application Specialist with Esri Canada’s Community Planning team who works with communities across Canada to develop and implement new planning solutions with ArcGIS and Dr.Sibeal McCourt, Data Manager at Ratio.City, a division of Esri Canada, who has worked on making Urban open data accessible for over 5 years. Their perspectives shape much of what follows.

Open Data Has Evolved but Unevenly

In conversations with experienced municipal administrators about how municipalities use open data with, one pattern came up repeatedly. Open data adoption has been gradual and largely people driven. Two decades ago, municipalities were often hesitant to publish data. Concerns about governance, accuracy, and internal capacity created caution. Over time that mindset began to shift. As they explained, the technology required to publish data has existed for many years. What changes is whether people inside the organization are willing and resourced to do it. Open data becomes easier to justify when GIS and planning departments realize how much time they spend responding to repetitive data requests. Publishing information proactively reduces that burden and allows staff to focus on higher level analytical work instead of exporting spreadsheets throughout the day.

However, this evolution has not occurred evenly. Larger municipalities often have established data teams, structured portals, and governance processes. Smaller municipalities may have only one GIS staff member, or sometimes none at all. Some jurisdictions offer downloadable spatial files through APIs and REST services. Others publish static spreadsheets or PDFs.

Ridley’s insights align with these observations. From conversations with him I learned that his work across municipalities around planning technology implementation makes this fragmentation especially visible. What appears mature and structured in one city may look completely different in another. These differences are not only technical: they reflect variations in staffing, priorities, and institutional history. Open data therefore turns out to be less about software and more about organizational readiness.

Publishing Is Not the Same as Planning

Even as openness increases, another issue becomes clear. Accessibility does not automatically translate into usability. During my internship I saw firsthand how uneven formats complicate planning workflows. One municipality might publish zoning as a structured spatial dataset. Another might release a multi-page PDF. Development application data may appear as an Excel sheet with inconsistent address formatting, which makes spatial integration difficult. If you wish to know more about these experiences, I have an earlier blog about them here.

One program manager at Esri described this ‘unevenness’ challenge as a “lowest common denominator” problem. A municipality may technically meet a standard by publishing something. If the dataset only contains point locations or inconsistent fields, its value for planning analysis remains limited. Planners often need parcel boundaries, phasing information, and infrastructure context rather than a single point on a map. Ridley emphasized a similar issue from the consulting perspective. Even when municipalities publish data with good intentions, differences in naming conventions, schema structure, and update cycles create friction. Data that is technically open may still require significant transformation before it can be integrated into planning tools.

This is where the gap between GIS and planning becomes visible. Planners usually know what they want to analyse. They are often interested in feasibility, height limits, or development constraints. However, they may not have the technical background required to process raw spatial data. GIS professionals understand the architecture of spatial systems, but they are not always embedded in day-to-day planning decisions.

Sibeal McCourt, Ratio.City’s Data Manager, often noted in conversation, the real value lies not simply in aggregating data but in translating it. Data must be structured in ways that align with planning questions rather than technical architecture alone. The more time I spent reviewing datasets, the more I realized how much invisible work goes into making municipal data usable. Schemas must be cleaned, update cycles must be verified, files often need to be reformatted and APIs validated. The final layer may appear simple, but I can assure you the process behind it rarely is.

The level and quality of urban data digitization remains uneven across municipalities, reflecting disparities in resources, staffing capacity, and institutional investment.

Data Digital Tools and the limits of Innovation

As municipalities begin exploring more advanced planning tools, these foundational issues become even more apparent. There is growing interest in digital twins and 3D modelling. These tools can help visualize development proposals, test zoning scenarios, and communicate planning decisions more clearly to councils and the public. However, the professionals I spoke with consistently emphasized one idea. Advanced tools create value only when they are connected to a clear planning problem.

A former senior municipal GIS coordinator shared an example from his time in municipal government involving a proposed six storey building in a traditionally low-rise downtown area. The concern was political and visual. Council needed to understand how the building would fit into the surrounding streetscape. By modelling the proposal alongside existing conditions, planners were able to demonstrate scale and context clearly. This helped the project move forward without the anticipated backlash. If you are interested in digital twins in planning you can find an article by Michael Sprayson (now Program Manager at Esri Canada after more than two decades working inside municipalities) on the subject here, where he discusses how municipalities are beginning to integrate these tools into long term planning workflows.

Yet tools like digital twins depend on strong foundations. Parcels, zoning layers, building footprints, and infrastructure networks all need to be accurate and properly governed. Digital models do not solve weak data conditions. At best, they make those gaps more visible. In other cases, when layered onto incomplete or inconsistent data, they can do the opposite. They can present an incomplete dataset as a complete picture to a wider audience that may not have the background to recognize its limitations. This highlights a broader pattern. New tools can improve how planners analyse and communicate information, but they do not replace the need for consistent, well structured data. When those foundations are weak, even the most advanced tools struggle to deliver meaningful insight. The value of technical innovation ultimately depends on the strength of the systems beneath it, as it is the systems that determine whether such innovation will result in transformative change.

Digital Twins are an example of advanced planning tools which need robust open data infrastructure in place to function effectively.

Standards, Governance, and the Provincial Question

Given these challenges, a recurring theme in my conversations was the need for stronger coordination and clearer standards.

It was a recurring suggestion among those I spoke with that provincial leadership could play an important role in establishing planning data standards. If core datasets such as zoning, infrastructure, and development applications followed consistent formats, interoperability across municipalities would improve significantly. Ridley highlighted the practical benefits of that consistency. When standards align, planning technology can integrate more easily across jurisdictions. Instead of spending time resolving formatting issues, planners can focus on evaluating development proposals and policy implications.

At the same time, the professionals I spoke with also emphasized that standards alone are not sufficient. Legislative direction needs to be paired with funding and implementation support. A regulatory requirement without resources can overwhelm smaller municipalities that lack the staffing capacity to adopt new systems quickly. Larger cities may be able to absorb those requirements. Smaller ones often cannot.

Lessons from recent housing policy offer a useful parallel. Provincial efforts to accelerate approvals have often relied on mandates without corresponding support, emphasizing timelines without fully addressing underlying systems. Approvals alone do not produce housing. Infrastructure capacity and municipal resources ultimately determine whether development can proceed.

A similar risk exists in open data policy. Expanding requirements without providing the necessary systems and support can create expectations that municipalities may struggle to meet. This creates a difficult tension. Policy mandates can move timelines forward, but they do not always arrive with the funding, staffing, or technical capacity needed to implement them effectively. Smaller municipalities in particular may struggle to absorb new requirements when resources remain limited, reinforcing uneven outcomes rather than resolving them.

Municipalities also value autonomy in how they manage their own data and planning processes. Any provincial framework would need to balance consistency with flexibility so that local control is maintained while interoperability improves. The end goal is not centralization but ensuring the possibility and ease of coordination.

From Open Data to Open Infrastructure

Spending time inside Ratio.City and speaking with professionals like Sibeal and Ridley has changed how I think about open data. We are past the stage of asking whether data should be published. The more important question is how that data is structured, governed, and maintained. Open data becomes valuable when it reduces administrative burden, improves transparency, and supports better planning decisions. Digital tools become meaningful when they are built on strong data foundations and connected to real policy questions. Provincial reforms become effective when they recognize municipal capacity constraints and provide resources alongside mandates.

If the first phase of the movement focused on openness, the next phase may focus on infrastructure. By infrastructure I mean the systems, standards, and governance structures that allow data to move reliably across jurisdictions. Planning is increasingly becoming a hybrid field that sits at the intersection of policy, infrastructure, and digital systems. Tools can help, Platforms can translate, but ultimately the strength of the system depends on the quality of the foundation beneath it.

Open data alone is not the finish line. It is the starting point.