Crowd-source data is a term that has gained significant traction in recent years. In a recent webinar, we brought in our own Adam Charlton to talk about what can and can’t be trusted when using Open Source or Crowd-Sourced address data.

Defining Crowd-source Data

At the outset, it's imperative to understand what we mean by crowd-source data. It essentially refers to data that is both gathered and curated by individuals. The distinguishing feature here is that the data's curation process isn't dictated by a centralized authority or stringent verification system but by the same community that's gathering and supplying the data. If you're familiar with Wikipedia, that's a practical example of this model in action. For a more industry-centric illustration, consider OpenStreetMaps, an exemplar of crowd-source data in the addressing world. 

The Good

Local Regionalization

One of the standout advantages of crowd-source data is its ability to reflect local nuances. Whether it's New York, New Orleans, Hawaii, or Los Angeles, addresses are influenced by the local culture, traditions, and customs. OpenStreetMaps is a testament to this, capturing the authentic flavor of addresses in places like Utah, where traditional suffixes like 'street' or 'road' aren't commonly used. 

Global Scope

Beyond the US, crowd-source data projects a global narrative, capturing information from every corner of the globe - be it London, Canada, or Australia. 

Community Engagement

Perhaps the biggest boon of crowd-source data is the community that backs it. These passionate individuals not only provide the data but also assist others in making the most of it. 

Cost-Effectiveness

A significant upside is the affordability factor. Most crowd-source data is available at minimal to no cost. 

Swift Updates

Another upside is the rapid update cycle. For instance, a new building's address might find its way onto OpenStreetMaps before it's officially registered by the government.

Lesser Vandalism Concerns

Compared to larger platforms like Wikipedia, platforms like OpenStreetMaps experience lower levels of sabotage or vandalism. While not entirely immune, the threat is considerably reduced.

The Not-So-Good

Quality Consistency

The flip side of local regionalization is the lack of standardization. Without centralized standards, data can vary widely, even within the same vicinity.

Genuine Errors

While vandalism might not be a significant concern, genuine mistakes do creep in. For instance, mistakes in designating one-way roads can have real-world implications.

Incomplete Data

The voluntary nature of data input means there can be considerable gaps. Some areas might be densely populated with information, while others are glaringly empty.

The Balancing Act

Crowd-source data presents an intricate balancing act. While the benefits are clear, they are intrinsically tied to the drawbacks. Excellent localization comes at the price of inconsistent standardization. Swift updates might mean lesser verification. And while you might get high-quality data, the coverage could be inconsistent.

Want to learn more about what Adam covered in this webinar? You can view the recording right here: