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How geospatial insights can help meet business goals

Organizations that collect geospatial data can use that information to understand their operations, help make better business decisions, and power innovation. Traditionally, organizations have required deep GIS expertise and tooling in order to deliver geospatial insights. In this post, we outline some ways that geospatial data can be used in various business applications. 

Assessing environmental risk 

Governments and businesses involved in insurance underwriting, property management, agriculture technology, and related areas are increasingly concerned with risks posed by environmental conditions. Historical models that predict natural disasters like pollution, flooding, and wildfires are becoming less accurate as real-world conditions change. Therefore, organizations are incorporating real-time and historical data into a geospatial analytics platform and using predictive modeling to more effectively plan for risk and to forecast weather.

Selecting sites and planning expansion

Businesses that have storefronts, such as retailers and restaurants, can find the best locations for their stores by using geospatial data like population density to simulate new locations and to predict financial outcomes. Telecom providers can use geospatial data in a similar way to determine the optimal locations for cell towers. A site selection solution can combine proprietary site metrics with publicly-available data like traffic patterns and geographic mobility to help organizations make better decisions about site selection, site rationalization, and expansion strategy.

Planning logistics and transport

For freight companies, courier services, ride-hailing services, and other companies that manage fleets, it’s critical to incorporate geospatial context into business decision-making. Fleet management operations include optimizing last-mile logistics, analyzing telematics data from vehicles for self-driving cars, managing precision railroading, and improving mobility planning. Managing all of these operations relies extensively on geospatial context. Organizations can create a digital twin of their supply chain that includes geospatial data to mitigate supply chain risk, design for sustainability, and minimize their carbon footprint. 

Understanding and improving soil health and yield

AgTech companies and other organizations that practice precision agriculture can use a scalable analytics platform to analyze millions of acres of land. These insights help organizations understand soil characteristics and help them analyze the interactions among variables that affect crop production. Companies can load topography data, climate data, soil biomass data, and other contextual data from public data sources. They can then combine this information with data about local conditions to make better planting and land-management decisions. Mapping this information using geospatial analytics not only lets organizations actively monitor crop health and manage crops, but it can help farmers determine the most suitable land for a given crop and to assess risk from weather conditions.

Managing sustainable development

Geospatial data can help organizations map economic, environmental, and social conditions to better understand the geographies in which they conduct business. By taking into account environmental and socio-economic phenomena like poverty, pollution, and vulnerable populations, organizations can determine focus areas for protecting and preserving the environment, such as reducing deforestation and soil erosion. Similarly, geospatial data can help organizations design data-driven health and safety interventions. Geospatial analytics can also help an organization meet its commitments to sustainability standards through sustainable and ethical sourcing. Using geospatial analytics, organizations can track, monitor, and optimize the end-to-end supply chain from the source of raw materials to the destination of the final product.

What’s next

Google Cloud provides a full suite of geospatial analytics and machine learning capabilities that can help you make more accurate and sustainable business decisions without the complexity and expense of managing traditional GIS infrastructure. Get started today by learning how you can use Google Cloud features to get insights from your geospatial data, see Geospatial analytics architecture.

Acknowledgements: We’d like to thank Chad Jennings, Lak Lakshmanan, Kannappan Sirchabesan, Mike Pope, and Michael Hao for their contributions to this blog post and the Geospatial Analytics architecture.

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