How AI is Democratizing Geospatial Data: A Conversation with Emily Lisle (dataplor)

Join Emily Lisle, Director of Product at dataplor, as she discusses how AI is making complex location data accessible to a wider audience. Discover how this shift is creating new opportunities (for example, in finance) and learn how the partnership between CARTO and dataplor empowers users to get insights faster. Ready to enrich your own spatial analysis? Explore thousands of curated datasets in the CARTO Data Observatory today https://carto.com/data-observatory #geospatial #gis #datascience #locationintelligence #CARTO #dataplor Transcript (00:02) What trends have you seen in the geospatial community? In the geospatial industry, the emergence of AI has provided more generalized access to geospatial data. Previously, it was an industry where you had to know what you were doing. You needed very technical teams to access the data, to know what data you should be looking at, and how to use it. There's been a huge emergence of tools, not only specific geospatial tools, but AI tools in general that are allowing people to access expanded types of data analysis and enabling less technical people to do more work themselves. We've seen this democratized access to geospatial data and insight. This has expanded the number of use cases for geospatial data because people can go in and access it themselves now. 00:56 How is AI impacting geospatial data and your work at dataplor? For us, there are two aspects of AI. We use AI internally from a cleanliness perspective. It's not something that we rely on from a data sourcing perspective. We don't want to be hallucinating any data within our own ecosystem. But for classification and automating some of our data quality efforts, it's made a huge impact. We have our human team that's doing the final review on the data, but we use AI to help us flag things or conduct high-level analysis to identify trends and issues with underlying sources. It's done a huge amount for us from an automating perspective around our data quality processes. Continuing to provide that democratized access to our data, we're seeing that decision-makers are able to go in and understand what's going on themselves without needing to pull in an analyst. While working with clients, we can more easily communicate the value of this and what they should be doing with it to less technical users as well. 02:07: What geospatial use-cases are you most excited about? Some pieces we've been working with lately that have seen an uptick are financial use cases. There are traditional models, for example, of what foot traffic at Starbucks looked like this year in comparison with their stock price. This is the classic use case from a location intelligence perspective. But more financial firms are identifying geospatial data as a valuable input to their analysis, their portfolios, and their own internal decision-making, whether on the real estate side or the quant side, plugging that into a model to understand the performance of these larger brands and what certain industry trends look like. This applies down to the mom-and-pop level: What do pizza restaurants in New York look like? What does foot traffic look like there? What are some of the trends? Seeing how it's plugged into the financial industry has been interesting. We've seen an uptick recently of people discovering new use cases for it. 03:12 How are CARTO and Dataplor working together to help customers & the wider geospatial community? CARTO's been a great partner for us. We operate within their ecosystem. We're a data provider, and you can access our data through the CARTO ecosystem. For us, it's about democratizing access to it. We could be providing the highest quality data in the world, but if a team doesn't know how to access it or what they should be doing with it, then it becomes useless. Being able to work with partners like CARTO expands the number of people that we can reach. They provide a lot of input as to what you should be doing with this data, why you should be using it, and what you can do with it—what insights you can derive. That's been great to see and has brought a lot of value to our partners. 03:57 How does the Agentic GIS add value to dataplor's data? From the Agentic GIS side of things, it has put the insight and analysis into the hands of the decision-makers. It's less of needing to go out and hire an analysis team, have them ingest the data, and create an ecosystem to process the raw data. With something like CARTO, and especially the agentic piece of it, you have that ecosystem at your fingertips. Not only from integrating the data into your system and getting immediate access to it, but also getting an immediate understanding of it and being able to pull that insight out of the data much faster.