UrbanFootprint uses Amazon QuickSight to provide business intelligence analysis over thousands of datasets

This is a guest post by Michelle Atkinson from UrbanFootprint.

UrbanFootprint provides spatial data and analysis technology to deliver urban and community resilience insights to power decision-making. UrbanFootprint brings together large volumes of data so decision-makers can perform complex analysis around multiple dimensions of risk. We provide parcel-level coverage for 99% of US parcels. 160 million parcels across 10,000 jurisdictions are assembled, normalized, and aligned with other data. UrbanFootprint’s technology analyzes data on climate risks, community resilience, and the built environment based on over 150 datasets.

In this post, we share how UrbanFootprint used Amazon QuickSight, a cloud-centered, fully managed business intelligence (BI) service, to provide insights to customers through interactive dashboards and rich visualizations.

People making critical decisions about community and infrastructure planning and investments—like energy utilities, financial institutions, government agencies, and private corporations—analyze huge quantities of data to enable sound decisions in the face of increasing climate threats and regulatory complexity.

But it’s incredibly difficult to derive a view of the specific risk factors for each situation—let alone how those risk factors interact with each other.

Decision-makers can collect and analyze complex data using consumer-grade software, but it’s expensive, difficult, and time-consuming to bring all the data needed into one view. Often, people aren’t able to examine every different risk factor, and this limited view can contribute to unfavorable investment decisions, regulatory risk, and ineffective public policy. The other method is consulting with specialist teams that manually assess risk, which can be a considerable expense.

Both methods consume time and resources, and they typically rely on static data that gives decision-makers a point-in-time snapshot rather than a real-time flow.

UrbanFootprint solves that problem for decision-makers. Our platform connects data across environments, communities, socioeconomic factors, and hazards with precise geographic locations. Although some users can find use in data presented in spreadsheets, intuitive dashboards and rich visualizations are important components that enable a variety of roles to derive the insights they need.

From in-house dashboards to rich BI insights with QuickSight

We were building charts and graphs in spreadsheets for customer data deliverables. We had also built a web application to create some simple charts, but we found ourselves asking how we could extend the capabilities to provide richer, self-service visualizations.

We provide map-based visualizations using a lightweight Geographic Information System (GIS) tool, and wanted to complement the data experience with more traditional charts, graphs, and summaries. In addition, we wanted to offer users the ability to explore a dataset by filtering and segmenting.

We quickly realized we would have to devote significant internal resources and time to build the product from the ground up. We decided to use an existing established BI analysis solution and explored several options.

Our evaluation criteria included the following:

• Feature set
• Ability to meet end-customer preferences
• Ability to embed within our application to enable self-service
• Developer community
• Robust visualizations within the dashboards
• Ease of integration

We chose QuickSight because of its strong dashboard editing capabilities—meaning our users are provided with dashboards that can be customized, allowing them to choose how to interface with dashboards. They can select different criteria to evaluate and change datasets or subsets. We also selected QuickSight because we were already using AWS services such as Amazon Elastic Compute Cloud (Amazon EC2), Amazon Relational Database Service (Amazon RDS), and Amazon Simple Storage Service (Amazon S3). QuickSight would fit seamlessly into our tech stack.

We were also impressed by other advantages of AWS, particularly the support of their team, but also their technical resources, which reduced the technical burden of building a product from the ground up. We had an opportunity to utilize Powered by QuickSight team resources to build a compelling product offering, and the AWS team also offered to cocreate new solutions, which would help us from a technical and financial standpoint.

Timeline and implementation

This process moved quickly. In Q1 of 2023, we evaluated several BI tools, selected QuickSight, and began building dashboards. By the end of the next quarter, we released multiple dashboard templates, which is the starting point for customers to interact with one of our dashboards—the essential components of the data insights we can provide. This included constructing the data pipelines to populate the templates, adding authentication APIs, and embedding the dashboards in our app. The AWS team was very helpful in making sure we had direct access to technical support as we stood up the first QuickSight dashboards.

QuickSight enhances the UrbanFootprint product experience

With QuickSight, we’ve expanded our experience to include dashboard-based visualizations of our customer’s assets and insight describing them. This complements our existing map and GIS-based visualization tools. The following screenshot shows an example dashboard.

The dashboard provides new ways to analyze and work with our data, including the ability to filter, segment, and isolate sets of locations, different hazards, community characteristics, and built-environment characteristics.

QuickSight is straightforward for our team to manage. UrbanFootprint Dashboards automatically generates and provisions dashboards for each new portfolio of assets we process—without any manual steps. We have established templates to process new portfolios of locations, and created pipelines and provisioning systems so we can provide incremental deliveries for our customers with no additional work from our teams.

We’ve realized the following benefits:

• Faster time to insight—our customers tell us they’re having lots more “aha” moments when using our platform
• Less need for manual risk assessments, saving our customers time and resources
• Clear, actionable data visualizations
• Multiple datasets brought into a single-pane view

We’ve seen a boost in sales call engagement, with a significantly higher number of follow-up calls. Additionally, the pace of new opportunities created from both new and existing customers has increased.

Next steps

We’re beginning to develop prototypes that use Amazon Q, a generative BI assistant that allows non-technical users to use natural language to quickly build BI dashboards, create visualizations, and perform complex calculations, as well as do cool things like create data stories and get executive summaries of dashboards. We hope to incorporate this into our product.

Enhance your products with QuickSight

QuickSight made it effortless for us to enhance our data platform and equip our customers with intuitive exploration of complex data. Find out more about how Amazon QuickSight Embedded Analytics can help you embed robust BI in your products.

About the Author

Michelle Atkinson is a product manager dedicated to empowering the varied users and stakeholders in areas of urban planning, infrastructure planning, and disaster response/recovery with tools to make better data informed decisions. With a background in integrating data and technology into state, local and federal public-sector processes, Michelle focuses on designing solutions that enhance autonomy and efficiency ensuring informed decisions can be made quickly and with richer insights.