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Open-source technology runs the world. It builds communities, increases transparency, and benefits from the power of many hands — which leads to quicker development and troubleshooting when problems arise. What’s more, open-source technology can serve as a solution to privacy issues and IP asset security, and even create value for the end user. Keep reading to learn how Spectus is integrating open-source tech to create a true community around location data.

Spectus Integrates Open-Source Tech

While Spectus location data is itself not open source, Spectus Data Clean Room has integrated a number of open-source technologies into its platform including JupyterLab, Trino, Apache Spark and various common Python packages used by data scientists globally. Given how commonly used these technologies are by data practitioners everywhere, Spectus users can focus their precious time on exploring Spectus’ data assets and deriving insights rather than learning how to use proprietary tools. This standardization of tools also makes it easier for users across organizations utilizing Spectus to share and learn from each other.

The same benefits achieved by the standardization of tools can result from the standardization of data. This is why open standards and protocols that gain wide adoption are so powerful. With Spectus, all users read from the same data schemas and tables as a starting point. This gives us the ability to craft tutorials and example use cases in the form of Jupyter notebook tutorials that we make available to all clients. As of right now, we have over thirty different tutorials, (and growing) that any Spectus client can gain access to, run on their own, copy and iterate on. These tutorials and example use cases are not only powerful assets for clients to accelerate their time to value, but they draw on the years of hard-earned institutional knowledge our data scientists and solutions teams have developed on Spectus. We see this work as a tremendous benefit to clients and an area where we intend to continue investing heavily over time.

Combining standardization of open source tools as well as data paves the way for us to build a rich community around Spectus and the myriad of potential use cases it can serve. Over time, this can take the form of our commercial clients interacting with researchers in our Social Impact program, learning from each other and building upon their respective efforts. Much like open internet protocols, new layers of abstraction can be developed and exchanged on top of Spectus, unleashing innovation around location intelligence.

Social Impact and Open-Source Tech

Our Social Impact initiative is a great example of how that community can play out. By democratizing access to location data, we can unlock use cases for the betterment of society. Consider the following examples:

  • A data scientist can put together a “How to Leverage Spectus Data” document to understand how COVID-19 may spread.
  • A university could create a template that uses Spectus’ privacy-enhanced location data.
  • A researcher could build a dashboard on Spectus for other researchers to benefit from.

Organizations can also partner through data collaboratives to increase the value of their research. For example, in a data collaborative between Spectus and the University of Toronto Rotman School of Management and Munk School of Global Affairs & Public Policy, Spectus data helped produce metrics that offered insight into changes in human mobility since the pandemic began, highlighting the importance of social-distancing measures. Our case studies highlight the efficacy of multi-institution collaboration in understanding complicated systems in times of crisis.

Want to see Spectus in action? Connect with an expert on our team to learn more today.

This article was originally authored while the Spectus Data Clean Room product was known as Cuebiq Workbench.