CARTO

Coca-Cola Bottlers Japan Inc. (CCBJI), the largest Coca-Cola bottling company in Asia, operates a network of over 700,000 vending machines across Japan. The company collects a vast amount of data regarding the overall sales performance of each machine and how individual products perform per machine and location. Historically, CCBJI had to extract the necessary data for analysis from the core system, build their own mechanism to create a data warehouse using ETL tools, and perform various analyses. The sheer size of the data being produced posed several challenges for the company. These included the length of time needed to return the results of a simple query and the complex maintenance of such a legacy system.

Swiss RE, a leading global provider of reinsurance and insurance, was facing a series of challenges in the insurance market. The market was showing slow growth, with a significant percentage of customers acting like booking.com, reading reviews before joining an insurance product. Additionally, changes in law regulation, from IT to Internet to privacy, made it extremely difficult to release new products into insurance. This resulted in a lack of trust from regulators to insurance companies, from insurance companies to the people, and from people to insurance companies. Swiss RE needed to find a way to extend its geo-reach, geo-enable its data, and monitor its assets to overcome these challenges.

GDELT, the world's largest and most comprehensive open database of human society, faced a significant challenge. The Global Geographic Graph, a part of GDELT, spans more than 1.7 billion location mentions in worldwide English language news coverage dating back to 2017. The organization wanted to map the geography of the global news narrative, a task that was proving to be complex due to the sheer volume of data involved. The challenge was to find a way to effectively visualize this massive amount of data in a way that was both meaningful and accessible.

Philip Morris International, a leading international tobacco company, is faced with the challenge of cigarette butts being the most littered item in the world, with 4.5 trillion discarded each year. As part of their global initiative 'Our World Is Not an Ashtray', they aim to increase the scale and reach of their participation in clean-up activities. They also want to raise awareness of the issues of littering, and cigarette butt littering particularly, in local communities. The challenge lies in the sheer scale of the problem, the need for accurate data on litter hotspots, and the requirement to monitor the impact of anti-littering activities effectively.

Premier Utility Services was faced with the challenge of managing their fieldwork operations, which included a large gas meter inspection and leak detection project in upstate New York. The project required the processing and management of large data sets, including data on over half a million gas meters. While Premier had turned to Fulcrum for help with data processing and management, they still needed a solution that would allow them to visualize and analyze the data at scale. The complexity of the application they needed was significant, as it had to handle vast amounts of data and provide useful insights for decision-making.

Skyhook Wireless, originally a provider of location on mobile devices, was facing the challenge of managing and utilizing the enormous amount of data generated from their location services. The data, which was being used to provide location-based services like pinpointing a device's location on a map, was growing exponentially as the company expanded its services globally. The challenge was not just managing this data, but also compartmentalizing it into usable pieces for analysis. The expectation of high spatial-temporal accuracy, which was once a stretch for a county or a city, was now expected on a global scale. Furthermore, the company was also facing the challenge of meeting the growing expectations of businesses that required detailed data analysis, such as the number of burgers sold in one McDonald's location versus another.

The Centre for Advanced Spatial Analysis (CASA) at UCL was faced with the challenge of identifying, characterising, and locating neighborhoods in London that have recently undergone gentrification. They needed to disaggregate the different types of changes revealed by the data. Additionally, they aimed to predict which neighborhoods are likely to be the next targets of gentrification. The ultimate goal was to present and make available data, code, and novel interactive visualisations as a comprehensive tool for supporting policy and decision making in the city.

The case study revolves around a private equity firm, American Securities, which was considering the acquisition of a retail chain. The primary question that needed to be answered before making the investment decision was whether the retail chain could continue to be profitable by building more stores. The firm was interested in two expansion strategies: whitespace, where the retail chain would expand into new markets where it didn't have a current brand presence, and In-Fill, where more stores would be created in the market that the retail chain was currently operating in. The challenge was to understand the potential profitability of these strategies in a short time frame, given the competitive and time-intensive nature of the bidding process. The firm also needed to understand the factors that could affect the performance of the stores, such as the impact of distance from the core market and other potential confounding factors.

Avison Young, a global commercial real estate services firm, faced a significant challenge due to the Covid-19 pandemic. The widespread retail slowdown and permanent work-from-home policies led to a drastic reduction in the demand for commercial spaces. This situation greatly impacted the commercial real estate market, which Avison Young is a part of. However, with vaccines becoming increasingly available, the commercial real estate market was expected to experience a sharp rebound. Many people were anticipated to return to shopping, traveling, training, and enjoying spaces outside their homes. Avison Young needed a way to visualize and understand these movement trends across local authority districts over time to better strategize and plan for the expected rebound.

idealista, an online real estate platform, was faced with the challenge of understanding the trends and patterns in their web traffic. The company noticed that a significant 19% of their web traffic for Spanish coastal property listings was coming from foreigners, with the remaining 81% from a national audience. The popular Spanish slogan “sol y playa en España (sun and beach in Spain)” was attracting people from all over the world to invest in property in the Iberian Peninsula. idealista wanted to delve deeper into this data to identify interesting trends and gain a more visual understanding of their audience's preferences and behaviors.

The National Renewable Energy Laboratory (NREL) is the only federal laboratory in the United States that focuses solely on renewable energy, commercialization, development, and research. The challenge NREL faces is how to realize high penetrations of renewable energy while achieving broad goals of reliability, resilience, and affordability. The complexity of the energy grid, with its numerous generators and variable load, requires sophisticated tools and visualizations to understand and manage. A fundamental challenge with renewable energy is its variability and continuity in both space and time, which poses challenges to traditional models. The question is how to take a phenomenon that’s inherently continuous and variable, and fit it into a discrete model space, whether it’s nodal or regional. The biggest question is how to ensure that the resource is properly characterized and preserved.

In the aftermath of Hurricane Sandy, New York City (NYC) was faced with the challenge of scaling its ability to perform analytics immediately after a disaster and in preparation for future disasters. The city had a significant need to enhance its geospatial analytics capabilities, particularly in terms of map-making. The primary challenge was finding a tool that was user-friendly enough to be used by any analyst, regardless of their level of expertise in geospatial analytics. The goal was to quickly visualize data and use it to inform response efforts. The city was also looking to move away from segmenting analysis by a specific type of tool, which limited the ability of analysts to fully utilize and visualize the data.

Posterscope, a location-based marketing agency of Dentsu Aegis Group, was faced with the challenge of innovating the way brands are built by leveraging location intelligence. The agency aimed to connect brands with Out-of-home (OOH) audiences at the right moment, making communication more personal, contextual, and relevant. The challenge was to understand the location of these moments and use this information to develop efficient communication solutions. The retail industry was evolving beyond transactions, with activation, engagement, and transactions becoming closer than ever at any touch point. Posterscope needed to adapt to this change and make retail more experiential using location and data. Despite the rapid growth of ecommerce, physical retail was still a significant contributor to the global industry. The challenge was to apply the concepts that made ecommerce grow so fast, such as contextuality and data analysis, to physical retail.

The challenge faced by Postmates, a leader in on-demand delivery in the US, was to efficiently connect consumers with merchants and deliver goods from any merchant to the customer's door in minutes. The company had to deal with the complexities of location data, as location carries weight and meaning. The company had to understand the preferences of customers which varied throughout the year, the week, and within a city itself. They also had to deal with the challenge of delivering a wide variety of items, from furniture to late-night medicine runs and diapers, across 300 cities in the US and Mexico.

The Sociedad Municipal de Aparcamientos de Las Palmas de Gran Canaria (SAGULPA), a public transport company, was facing a challenge of managing and optimizing their mobility resources. They had access to multiple location data sources but lacked a unified platform to organize and analyze this information. The company wanted to better understand the mobility patterns in the city, including the distribution of journeys, the profile of the travelers, and the usage of their services. The goal was to use this information to improve their services, reduce traffic congestion, and promote sustainable transport solutions.

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.

Vizonomy, a computer software company, was faced with the challenge of making natural risk assessments and hazard mitigation plans accessible to a wider range of stakeholders. Traditionally, these assessments were only available to a select number of government stakeholders who could afford the costs associated with the process. As a result, few communities were able to develop a plan and thereby become eligible for federal resources should a natural disaster occur. Additionally, the traditional risk assessment process was built on a static model, where datasets quickly become out-of-date and new assessments cannot be developed easily without external assistance.

Tecnilógica, a digital agency, wanted to leverage a timely, newsworthy event to generate brand awareness and drive traffic to their platform. The Ashley Madison data leak provided an opportunity to create a data-driven visualization that would attract a global audience. However, the challenge was to clean up the dataset, removing any identifying information to protect users, and then find a way to visualize the data in a compelling and insightful manner.

Spotify, a digital music service with over 100 million active users, sought to increase its brand visibility and user traffic through data-driven visualizations. After an original data-driven visualization increased traffic to the music platform by 2 million in just 11 days, Spotify aimed to repeat that success. However, this time the map needed to be customized with Spotify branding to underscore the company’s global reach and further brand awareness, while driving even more user traffic to the platform. The new visualization needed to embody Spotify as a brand, and provide insights on distinct musical preferences.

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.

Le Télégramme, a French regional daily newspaper, was looking for a new way to visualize data in a timely manner to increase brand awareness and drive more traffic to its platform. They wanted to use visualizations that could be customizable and allow for easy adoption by non-coders. The collapse of the traditional business model of print newspapers has led many publications to establish themselves with original reporting, rather than just commentary or summaries of reporting from other publications to attract and retain readers and advertisers.

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.

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.

i-sense, an Interdisciplinary Research Collaboration (IRC) funded by the Engineering and Physical Sciences Research Council (EPSRC), aims to build digital sensing systems to identify and prevent outbreaks of infectious diseases and antimicrobial resistance. However, they faced a challenge in interpreting the daily or weekly data related to the COVID-19 pandemic response published by Public Health England, National Health Service (NHS), and the Office of National Statistics (ONS). The data was often difficult to understand, making it challenging to evaluate the effectiveness of the system in place.

Instacart, a grocery delivery and pick-up service, was facing a challenge in its business expansion strategy. The company's approach to connecting retailers with customers was conservative, limiting its growth potential. The company was not fully utilizing the potential of its app to connect customers with their preferred grocers, which was affecting customer engagement and spending. Furthermore, the company was not fully capitalizing on the opportunity to help its retail partners reach new customer bases. The challenge was to find a way to be more aggressive in their growth strategy, increase customer engagement and spending, and help retailers reach new customers.

Jefferies, a global investment banking firm, was grappling with the challenge of making informed investment decisions and advising their clients effectively. Traditional data strategy and research were not formally integrated into most of their research, often being treated as an afterthought. The firm was also struggling with the time-consuming process of assembling information, which could take months or even years. Furthermore, the firm was trying to shift from making assumptions to making decisions based on grounded fundamental observations. The challenge was to find a key question that drives the key variable that a human is deciding is incredibly important, and shift it from being something that was previously assumed, to something that probably still has some assumption around it but can at least be based on a grounded fundamental observation.

The challenge revolves around understanding the social segregation in cities, particularly in the United States. The traditional understanding of segregation is based on geographical locations, often characterized by the stark contrast between rich and poor areas within close proximity. However, this perspective fails to consider the 'why' behind segregation, focusing solely on the 'where'. The challenge is to challenge this traditional notion of segregation by using high-frequency location data to understand the reasons behind segregation. The data gathered from 11 of the most populated metropolitan areas in the US revealed that on average, around 75% of the people we interact with daily live more than 15 kilometers away. This suggests that to understand segregation, we need to go beyond geographical locations and start considering the reasons behind it.