Sanitas, a healthcare provider, was grappling with the challenge of understanding the impact of location on the performance of their clinics and hospitals. The healthcare industry is one where location plays a significant role, and Sanitas wanted to gain insights into the specific characteristics of their clinic locations. They wanted to understand how these characteristics, including the age and income of the local population, affected the performance and income of their clinics. Additionally, they wanted to profile their clinics based on the information about their customers, competitors, and other points of interest in the area.

T-Mobile, the second-largest wireless carrier in the United States, faced a significant challenge when it launched its 5G home internet service in early 2021. The service was made available to 30 million homes across the US, following a pilot period that began in 2019. The challenge lay in the fact that wireless internet for a fixed address requires a significantly different qualification process than a traditional internet service provider (ISP). Making informed decisions about candidacy required a large amount of spatial data. With 185 million unique addresses in the US, each with untold variables associated with them, T-Mobile needed to determine who was qualified for service. This included determining at an aggregate level which zip codes, MSAs, or states had the greatest potential.

Aena, the world's largest airport management company, was faced with the challenge of improving navigation for passengers within its airports. The company wanted to enhance the overall passenger experience at all stages of their journey. The primary goal was to enable passengers to visualize their entire journey and locate key points of interest such as restaurants, shops, toilets, or security controls. Additionally, Aena wanted to provide passengers with an estimate of the time required to reach the boarding gate. This was a significant challenge considering the vast number of passengers, over 275 million, passing through its 46 Spanish airports and 23 airports in London-Luton and America annually.

ASDA, one of the largest British supermarket chains, faced a challenge in expanding their 'toyou' parcel service. Initially, the service was available in 639 locations within their estate. However, ASDA aimed to grow this number to 5,000 locations by incorporating external drop boxes closer to their customers, rather than relying solely on their traditional estate, which was typically a 10-minute commute for consumers. To kickstart their expansion plan, ASDA needed location data and insights to understand the key drivers for the site selection of new click-and-collect locations before deployment. They also needed to understand the proximity to target demographic and socioeconomic segments, such as universities.

The team at Bumble was seeking opportunities to expand in under-penetrated markets. Traditionally, they gauged their market presence by looking at monthly and daily active users. While this gave them an idea of their user base size in a particular market, it did not provide a clear picture of their actual market penetration. For instance, markets with large populations like the US, China, and Brazil naturally have larger user bases. However, the number of active users does not necessarily reflect the company's market position. Therefore, Bumble needed a more accurate method to estimate their total addressable markets (TAM) and market penetration.

The Valencia region in Spain, a popular destination for both local and international tourists, is home to over 340 beaches. During the de-escalation phase, the region was receiving a high number of visitors. The Generalitat Valenciana, the government of the autonomous region of Valencia, identified a challenge in managing the beach capacity to ensure the safety of the visitors. They needed a tool that could provide real-time information about the beach capacity to the visitors. The tool needed to be mobile-friendly as most visitors would likely access the information on their mobile devices.

Spatial data analysis is at a critical juncture. Over the past decade or so, customers have had to manage their own infrastructure, including the installation and maintenance of their GIS software. This could be on a laptop, desktop, or even a mainframe, but the responsibility of determining the size of the computer, the CPU cycles needed, and the storage capacity required fell on the customer. This process was not only time-consuming but also required a significant amount of technical expertise. Additionally, customers had to find and prepare the datasets they needed for their analysis, which could be a challenging and costly process.

Hodges Ward Elliott, a commercial real estate investment company, was looking to leverage data science to gain a competitive edge in the real estate market. The company recognized the potential of data science in real estate, an industry that has traditionally been slow to adopt such technologies. The challenge was to find a way to effectively use data science to understand the preferences of groups of people and how these preferences change over time. This understanding is crucial to predicting trends in real estate, as it can provide insights into where people want to live, shop, and work. However, obtaining and analyzing relevant data was a significant challenge due to the lack of readily available data in the real estate industry.

The city of Medellín wanted to serve a broader demographic by applying new technologies to connect city departments and agencies like transportation, administration, healthcare, education, utilities, and construction. To accomplish Medellín’s objectives, priority was given to the areas of the city where bigger social and economic issues were found. Emphasis was placed on the planning and development of urban projects to transform the physical environment in order to implement profound social and cultural shifts in the targeted communities.

Red Eléctrica de España (REE) wanted to provide the public with real-time information on over 2,000 indicators related to the Spanish electrical grid. It was critical to find a solution that was comprehensive and easy-to-use because indicators are responsible for making timely information public by law. With thousands of indicators, many updating every ten minutes, REE had to find a way to organize and display them so they could be found, analyzed, and understood. There was also a desire to show the data to an audience that lacked technical knowledge. They needed a solution that was fully customizable and provided an easy platform for spatial data analysis. Additionally, REE needed a model to visualize the power supply across the whole country, liaise with suppliers, coordinate supply with neighboring countries, and many other measures, that would reveal the performance of the energy market and other interesting data in a way the public could understand.

Fon Wireless Ltd. (Fon) is a leading, worldwide WiFi provider that operates a system of dual access wireless networks. With more than 19 million crowdsourced hotspots, their goal is to extend WiFi around the world. Fon wanted to develop a tool to expose their access point coverage throughout the world, as well as their partner network in various countries. They needed to build an open portal to help customers and clients explore and find millions of access points that they could connect to in the Fon Community WiFi. The portal required a visualization that could be adapted at every zoom level, preserving optimum performance, and the ability to include logos and details of all of Fon’s partners. Additionally, Fon wanted to have a more appealing visualization on their corporate website that enabled partners to have their own map including their specific access points.

Endesa, the largest electric utility company in Spain, was looking to grow its customer base and maintain its leadership in the Spanish market. They needed to understand how to best serve their existing B2C customers and how to gain market share from their competitors. Their Business Intelligence team had previously used Microsoft Excel to evaluate their coverage and measure demand in complex models which were not always accessible to business users in other departments across their business. Endesa wanted to make this process easier, using a more intuitive tool to share insights with departments such as Corporate Strategy, Finance and Location Planning.

OneMap, a real estate and property development mapping platform, was facing a time-consuming process for producing custom maps and reports. They also had to undertake a substantial amount of data entry for their weekly updates. Their primary objective was to reduce the time involved in providing their customers with complete and accurate property reports. They aimed to compile detailed property information into a single, easy-to-use platform capable of leveraging location data and processing a high volume of attributes from each point. They also sought the capacity to create high-quality digital maps that would empower their customers, providing them with the means to visualize multiple data streams and perform their own filtering and spatial analysis.

Descifra, a global Location Analytics provider based in Mexico City, aimed to disrupt the site planning and sales prediction process with their product, Omen. The product brings together a large and diverse range of datasets, making it crucial to have an intuitive and slick interface for decision-makers to extract insights. The visualization tool needed to handle millions of data points, often in real-time, without compromising on speed, security, and geographical granularity. The challenge was to forget traditional 150-page PDF reports and put cutting-edge Location Intelligence visualization in the hands of their clients.

Securitas Direct, a leading connected alarms provider, had a large and growing sales force of 1,000 reps tasked with visiting existing and potential customers to retain and acquire new business. However, the allocation of leads and opportunities from their marketing department was done manually without exploiting the context of location data. This resulted in several different sub salesteams looking at the same opportunities and leads often being manually assigned by province, rather than geographical proximity, wasting sales rep’s time and resources. Securitas Direct needed a location-driven solution that would allow their team to be more efficient and productive, and that would work smoothly alongside their existing CRM technology (Force Manager).

Wecity, a sustainable mobility app, wanted to crowdsource and visualize smartphone-generated location data collected from thousands of users every month, for both their web and mobile app. They needed a location technology that would allow them to seamlessly collect, visualize and analyze user-routing data from cities and governments, and also about intermodality for potential partners, to create safety insights and indicators for its users. They also wanted to create a compelling platform by embedding maps that were beautiful and easy to use, as well as in line with the rest of their app.

Indigo Agriculture, a company that works with growers to reimagine the entire agriculture system, faced several challenges in their data-driven approach to business. Their analytics team had to deal with a wide range of internal and external data sources, including internal CRM data from Salesforce, external meteorological datasets, and data collected in the field. The team needed to pull data from existing systems and upload a variety of data formats for collaboration across departments and the creation of a seamless end-to-end experience for customers. Another challenge was turning complex geospatial data into actionable insights for business users. The team sought to provide business users in marketing, sales, and field operations a more intuitive and interactive format for viewing data, instead of relying solely on traditional, tabular data. Lastly, the business units often needed to see prototypes or analysis against tight deadlines. The team needed to quickly bring together data, carry out complex geospatial analysis, and deliver dashboards to ensure geospatial analytics were considered in all business decisions.

Mastercard, one of the world's largest financial services companies, processes over 160 million transactions every hour. This transaction data is extremely valuable and represents a significant resource for the company. However, monetizing this data presents several challenges. Firstly, any data monetization strategy must prioritize data privacy and security to maintain Mastercard's promise of keeping payments safe and secure. This requires anonymizing and aggregating the data to remove individual identifiers and prevent the inference of specific identifiers once the data has been aggregated. Secondly, the data needs to be productized in a way that appeals to a diverse audience with varied needs. This requires delivering an intuitive and singular user interface while ensuring the user experience is tailored based on industry and role. Lastly, the spatial nature of the transaction data presents challenges in determining the spatial scales at which to aggregate data. This is particularly complex when working internationally, as different countries have distinct geographic units.

Tinsa Digital wanted to provide more powerful solutions that would allow their clients (Financial Institutions, Investment Funds and Real Estate agencies) to manage their portfolios in a more agile way - with more spatial insight. The solution needed to provide a series of indicators in relation to asset locations so that clients could see relevant segments using multiple layers of information in just a few clicks. Working with CARTO’s platform allowed Tinsa Digital to automate processes, such as the loading and visualization of information, generating a 'user-friendly' display, requiring no specialized training. This allowed these different divisions of their business to perform complex calculations, delivering results faster, as well as including different layers to complete Real Estate, financial, or geospatial analyses. This would allow them to create AOIs (Areas of Influence) correctly attributing values to different administrative regions, as well as optimizing their own geometries. The key challenges included: User management for large volumes of information, Flexibility and responsiveness when working alongside Tinsa Digital clients, Effective integration and automation of ETL processes.

Securitas Direct, a leading connected alarm provider in Europe, faced a significant challenge in their sales department. Despite having a robust sales force of 1,000 representatives, the company was not fully leveraging the potential of location data in their sales strategy. The sales team had been manually allocating leads and opportunities from their marketing department, without exploiting the context of location data. This approach was not only inefficient but also failed to maximize the productivity of their sales force. To enhance their sales performance, Securitas Direct needed a location-driven solution that would not only increase the efficiency and productivity of their team but also seamlessly integrate with their existing CRM technology.

SEUR, a leading parcel delivery company in Spain, was facing challenges in optimizing their cold transportation network. The company was looking for a solution that would allow them to assess the current state of their network, identify areas of high demand, and determine if their distribution centers (DCs) were strategically located. They also wanted to quantify the impact of changes in their current network, such as the opening or closing of DCs and changes in delivery areas. Furthermore, SEUR was seeking to build an optimization model to identify where DCs should be located and design their transportation network (supply chain network design).

Telefónica, a multinational telecommunications company, was looking to enhance its data analytics capabilities. The company identified three levels of data analytics - descriptive, predictive, and prescriptive. The descriptive aspect, which involves visualizing data patterns, was a particular challenge. Traditional methods of data representation, such as Excel sheets, were not effective in conveying the patterns of smart steps, including how people move, where they come from, and where they go. The company needed a solution that could provide a more interactive and visually appealing way of presenting data to its clients, who are very visual and prefer to interact with data.

Vodafone, a leading telecommunications company, relies heavily on location intelligence to manage its networks and provide superior services to its customers. The company aims to understand where a customer is, where they are moving, and how to provide them with the best service, including fiber and other technologies. The challenge lies in the accuracy and precision of the data. Vodafone aims to build a product that can predict where people are moving with such precision that it knows exactly where people are going from and to. To achieve this level of precision and to outperform their competition, Vodafone needs to overcome the challenge of building technologies using complex algorithms and software.

The city of Madrid, like many other major cities worldwide, grapples with the challenge of traffic congestion. With half a million cars entering and exiting the city daily, managing traffic flow and reducing congestion is a significant issue. Waze, a crowd-sourced navigation and traffic app with over 100 million active users globally, sought to address this problem. However, while Waze had access to a wealth of user-generated data, it needed a way to connect with governmental organizations and municipalities to leverage this data effectively. The challenge was to transform the raw, open data into actionable intelligence that could be used to improve traffic management and provide citizens with accurate, real-time traffic information to plan their daily commutes.

During the COVID-19 pandemic, the Center on Rural Innovation (CORI) was faced with the challenge of analyzing disparity levels across the United States to identify areas in greater need of support. The pandemic had a significant impact on rural communities, and there was a pressing need to understand the extent of this impact. The challenge was not only to gather relevant data but also to analyze and present it in a way that would provide clear insights into the situation. This included identifying school districts impacted by the lack of broadband availability and counties facing significant employment risks due to the pandemic.

Clear Channel Outdoor, one of the world's largest outdoor media companies, faced a significant challenge in their operations. They were using mobile location data to link the physical and digital worlds, identifying devices exposed to their Out-of-Home (OOH) panels and collecting these insights within their RADAR® product. However, they encountered a problem in making this data easily accessible to advertisers for campaign planning. The challenge was to create a solution that would allow advertisers to easily access and utilize the insights derived from the mobile location data collected by Clear Channel Outdoor. This was crucial for the advertisers to plan their campaigns more effectively and reach their target audience more efficiently.

The financial technology (fintech) sector faces a significant challenge in recognizing the existence and availability of location data and its potential benefits. The sector has a wealth of internal datasets and monitoring systems that could be enhanced with the integration of location data. Particularly in the field of anti-money laundering, the combination of location data with financial data could be instrumental in aiding law enforcement and identifying illegal activities. However, the awareness and understanding of how to incorporate this data into day-to-day operations remain a significant hurdle. Additionally, the potential of location data extends beyond anti-money laundering, offering opportunities in marketing and credit risk management by providing insights into where people live and the communities around them.

Enveritas, a non-profit organization, was founded in 2016 with the mission to eradicate global poverty in the coffee sector by 2030. A significant part of this mission involved tracing the journey of coffee from its source to the consumer. This process not only required identifying the geographical locations where the coffee was grown and processed, but also understanding the social, environmental, and economic conditions prevalent in those places. The challenge was to create a comprehensive and dynamic visualization of the complex types of coffee based on the ECX classification system. The organization needed a user-friendly solution that could provide a clear and accessible representation of the coffee supply chain.

Google's BigQuery was facing a challenge in the field of geospatial analysis. The traditional hardware and software used for geospatial analysis were limiting the potential of BigQuery. The constraints of the existing infrastructure were hindering the scalability and performance of geospatial analysis. Additionally, the process of integrating geospatial data was cumbersome and time-consuming. Users were often required to perform administrative tasks and reference tables, which were considered 'boring' but necessary. The challenge was to enhance the geospatial analysis capabilities of BigQuery and make the integration of geospatial data more efficient and user-friendly.

Hodges Ward Elliott, a real estate company, was facing a significant challenge in creating data-driven maps, a fundamental aspect of their daily operations. The process was labor-intensive and time-consuming, often taking the graphics department 3-4 days to produce a single map. The introduction of the data science team and the use of open-source tools like Leaflet improved the situation, but it still took four hours to create a high-quality map. The company was looking for a solution that could further streamline this process, reduce the time taken, and make it easier to create and iterate on these maps.