How important is IT Infrastructure for Enterprise Spatial Deployments?

Is Your IT Foundation Ready for the Future of Spatial Computing?

As digital transformation accelerates, IT infrastructure has evolved from a background necessity to the backbone of advanced technological deployments. Leading the charge is spatial computing—a game-changer for industries utilizing Geographic Information Systems (GIS), remote sensing, and location-based services to create immersive, real-time, location-aware experiences. But unlocking the full potential of these technologies requires a solid IT foundation built on cloud infrastructure, DevOps best practices, and AI-driven workflow automation.

So, is your enterprise ready to leverage spatial computing’s full power?

What is Spatial Deployment?

Spatial deployment is the integration of spatial data and computational resources to deliver real-time, location-specific insights. This involves layers of data from sources like sensors, AR devices, IoT inputs, and machine learning, all working together to create an enhanced digital understanding of physical spaces. For enterprises, successful spatial deployment requires robust IT infrastructure capable of supporting these dynamic, interactive experiences at scale.

Spatial computing can elevate decision-making, streamline operations, and fuel innovation. Here are a few transformative ways enterprises benefit:

In essence, enterprises implementing spatial deployments see tangible returns not only through enhanced operational efficiency and cost reductions but also through increased customer engagement, market responsiveness, and technological agility. So, considering the financial and operational benefits, which steps do enterprises need to take to ensure that their IT infrastructure is ready to dive into the world of spatial computing?

Cloud Infrastructure: The Backbone of Spatial Deployments

Cloud infrastructure is indispensable for enterprise spatial deployments, offering the scalability, cost-efficiency, and agility needed to manage vast amounts of geospatial data. With cloud storage solutions like Amazon S3 and Google Cloud Storage, enterprises can store terabytes of spatial data in a cost-effective manner, while also enabling real-time access and analysis capabilities. Additionally, the integration of specialized cloud services for geospatial computing, such as Google Earth Engine or AWS Ground Station, empowers organizations to harness satellite data, automate updates, and scale their spatial operations efficiently.

A robust cloud infrastructure also facilitates distributed processing, which is essential for handling the massive volumes of data involved in spatial analysis. By leveraging distributed cloud services, enterprises can run parallel processes, analyze data streams in real-time, and deliver results faster and more accurately. Moreover, with the advent of hybrid and multi-cloud strategies, companies are no longer tied to a single provider. This flexibility allows for improved redundancy, risk mitigation, and the ability to leverage best-in-class services from multiple providers, significantly enhancing the robustness of spatial deployments.

Success Story: Esri (Environmental Systems Research Institute),, a leader in GIS (Geographic Information Systems), relies heavily on cloud infrastructure to support its spatial analytics platform, ArcGIS Online. They use a combination of public and private cloud setups, allowing real-time processing of massive geospatial datasets. Esri's hybrid cloud model, using providers like AWS and Azure, allows them to manage scalability while providing redundancy to handle high user demand across the globe. They also integrate tools like AWS Lambda for real-time data streaming, enabling efficient processing and analysis of spatial data.

What to avoid: Mapillary, a crowdsourced street-level imagery platform, initially relied solely on a single cloud provider without redundancy or a multi-cloud strategy. When faced with a provider outage, their system suffered downtime, impacting users globally. The key takeaway here is that spatial computing requires not just cloud but resilient, multi-cloud strategies to prevent service interruptions, especially given the vast amounts of data involved.

DevOps and Continuous Integration for Seamless Spatial Data Management

DevOps practices play a crucial role in ensuring the continuous development, integration, and deployment of spatial applications. Through automation and collaboration, DevOps bridges the gap between development and operations teams, leading to more reliable and efficient processes. Tools like Jenkins, GitLab CI/CD, and Docker enable enterprises to deploy, manage, and scale spatial applications rapidly, allowing for real-time updates and minimal downtime.

In spatial deployments, where data is continually updated from sources such as satellites, drones, and IoT devices, CI/CD pipelines are essential. Automated testing and deployment processes can quickly detect errors, roll out updates, and maintain high system integrity. Containerization with Docker and orchestration with Kubernetes are also pivotal in DevOps for spatial computing, as they offer the flexibility to deploy applications across different environments seamlessly. Together, these tools support agile, scalable, and highly available deployments which are essential for mission-critical spatial applications.

Success Story: Uber leverages DevOps practices to manage their real-time spatial deployments for ride-hailing, which depends on live geospatial data and updates. Their DevOps team uses Docker containers and Kubernetes orchestration to enable continuous integration and deployment. This setup allows Uber’s engineers to update the platform quickly without disrupting services. Their CI/CD pipelines ensure that bug fixes and enhancements reach the end-user without delay. Tools like Jenkins and GitLab CI/CD help automate the testing and deployment processes, which is critical for maintaining the reliability of spatially-driven services.

What to avoid: Nokia’s HERE Maps (in its early days): Early on, HERE Maps encountered challenges with DevOps workflows, as manual deployment processes resulted in slower updates and system inconsistencies. This made it hard to keep up with competitors and provided a disjointed user experience. Nokia’s later implementation of DevOps and CI/CD tools improved the situation, but this example highlights the importance of DevOps early on in spatial computing projects to enable rapid iteration and ensure seamless updates.

AI Workflow Automation: Enhancing Efficiency and Decision-Making

Artificial intelligence (AI) has emerged as a transformative force in spatial computing, enabling enterprises to analyze and interpret vast datasets with unprecedented accuracy and speed. AI-driven workflow automation further enhances this capability by automating repetitive tasks and optimizing complex workflows. For instance, machine learning algorithms can be used to process satellite imagery, identify patterns, and classify land cover with minimal human intervention.

In addition to data analysis, AI workflow automation aids in decision-making processes by generating insights in real-time. Tools like Apache Airflow and MLflow provide frameworks for orchestrating complex workflows, tracking experimentations, and managing the lifecycle of machine learning models. By integrating these tools into their infrastructure, enterprises can ensure that their spatial deployments remain adaptive, resilient, and continuously improve through AI-driven insights.

Success Story: Planet Labs, which operates a fleet of imaging satellites, uses AI-driven automation to process terabytes of spatial data daily. Their infrastructure leverages ML and AI models to classify land cover, identify changes in terrain, and assess agricultural conditions. By integrating tools like Apache Airflow, they orchestrate complex workflows, ensuring efficient processing of satellite data in near real-time. This approach has allowed Planet Labs to continuously enhance its insights and provide clients with timely, actionable information.

What to avoid: Although Google Glass (original release) isn’t a traditional “spatial computing” project in terms of large-scale deployments, its initial failure serves as a lesson in how lack of AI workflow automation can hinder spatial projects. Google Glass didn’t fully leverage AI for automated data processing or real-time insights, and as a result, it failed to provide the augmented reality value that users expected. For enterprises, this emphasizes the importance of AI automation in delivering efficient, responsive, and context-aware spatial experiences.

Food for Thought

A solid IT infrastructure encompassing cloud, DevOps, and AI workflow automation is essential for enterprise spatial deployments. As geospatial applications continue to evolve, the need for scalable, reliable, and efficient infrastructure becomes even more critical. Cloud platforms provide the scalability and storage solutions necessary for handling vast geospatial data. DevOps practices enable rapid development and deployment, ensuring high availability and performance. Finally, AI workflow automation enhances efficiency and drives data-driven decision-making. By investing in these infrastructural components, enterprises can achieve significant advancements in their spatial capabilities, ultimately leading to better outcomes and more informed decisions in their operations.

About Sky-E Red

In a world where technology offers limitless possibilities, Sky-E Red is here to be your guide. Our mission is to empower enterprises to not only strengthen their IT infrastructure but to bring their visions to life. With expertise in cloud computing, DevOps, AI workflows, and enterprise gaming solutions, we deliver tailored integrations that streamline processes and drive success. Your challenges are our challenges, and with our extensive expert network and commitment, we’ll help you build the best IT foundation for a brighter, tech-driven future.