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.

Leroy Merlin, a global home improvement retailer, needed to sense changes in selling patterns and quickly translate that intelligence into a series of decisions that go up the supply chain. Most decisions were made from headquarters, including the analysis of market areas, zones of influence, and distribution areas. This required executives and managers to know when and how much to ramp up or cut back on production, and where to distribute. It also meant choosing the right combination of transport modes to balance the urgency of delivery costs. Leroy Merlin found aggregating the data for key decisions an arduous and time-consuming task. To unify these data sources into a coherent and complete picture, managers created a seamless real-time reporting visualization to introduce precise predictive demand planning capabilities with a goal to share geomarketing and geolocalized data among store managers, marketing and business development departments, and executives.

Following seismic demographic, economic, and technological shifts across the city of London, JLL and London & Partners realized a modern site planning tool was needed that advertised the advantages of working in this global business center. JLL wanted to provide commercial Real Estate clients determining where to start, relocate, or expand operations with a solution that would process large volumes of data into accurate, precise, and up-to-date insights for clients without GIS expertise and accessible through a centralized application.

Rilos, a consulting firm operating in 15 countries, provides retail location intelligence solutions. They gather, update, standardize, and analyze data to help clients minimize risk, make informed decisions, and optimize sales and networks. However, this process was complex, costly, and time-consuming. As their client base grew, Rilos needed a solution that would allow clients to perform their own analysis with site studies and reports on demand, without the need for additional employee resources.

Vodafone Business, the B2B division of Vodafone, was faced with the challenge of managing the rapidly increasing mobile data traffic and the growing demand for location-driven insights. The company wanted to leverage the big data generated across its mobile networks to better understand human mobility patterns. However, this presented two major obstacles. Firstly, to comply with privacy regulations, Vodafone needed to extract movement patterns on populations, not individuals, necessitating a robust anonymization and aggregation methodology. Secondly, to familiarize customers with working with mobile data, Vodafone needed an accessible interface capable of presenting the true value of these insights. In addition, the company saw a larger opportunity to deliver the first mobile data business solution by combining Vodafone’s data and Location Intelligence.

ConnectEast, the owner and operator of the 39km EastLink toll road in Melbourne, was facing challenges in managing its vast array of infrastructure assets. The company was using a paper-based method of asset management, which was inefficient and time-consuming. Documents were scanned and saved or transcribed into the system manually, or in the case of photos, uploaded. This process was not only cumbersome but also posed the risk of potential loss of information. Moreover, scanned documents (PDF’s) did not allow for spatial analysis or reporting capabilities. There was a lack of real-time visibility, and it was difficult to meet ongoing reporting demands, including KPI tracking for government reporting purposes.

Gloval's Data & Analytics team wanted to start using location analytics solutions to develop Valea, a web application that would allow its clients (professionals from real estate agencies and investment funds) to make spatially-informed decisions on residential property assets. They sought to provide its clients with a solution that would allow them to analyze and visualize large volumes of data on the Spanish real estate market. This included big data processing, need for faster insights, and going beyond traditional PDF formats. They needed to support business and sales strategies for their clients with asset closure data, available assets data, financial data on potential margins, and historic data on residential property assets. They also wanted to provide a faster and frictionless experience for clients, allowing them to understand the potential sale price of their property with an enriched market study, exhaustive & spatially-informed market analysis, supply and demand statistics, and socio-economic indicators to enrich existing data.

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.

The UNHCR’s Inter-Agency Coordination Unit based in Beirut, Lebanon’s capital and largest city, operates at the epicenter of a complex web of refugee support and response. They coordinate between over 200 organizations, governmental, NGO, and private sector. In order to effectively coordinate, they created 12 working groups, each with a distinct focus area. These working groups, supported by a unified IT infrastructure, create and execute on 4-year plans while also meeting unexpected challenges with rapid response. The refugee crisis, and the support system that has risen up to help refugees in Lebanon, is complex. Unofficial settlements pop-up regularly and the landscape is constantly shifting. Government policies impact refugee settlement status, environmental factors impact refugee safety, shifting populations require constant re-prioritization and resource allocation. While having this data is a great start, for the Inter-Agency Coordination Unit’s working groups to leverage the data towards their 4-year plans, and for emergency service and rapid response efforts, they also need a system that allows this data to be accessed, visualized, analyzed, and disseminated across their 200 partner organizations.

Allegro, a leading e-commerce platform in Central Europe, decided to invest in its own logistics and delivery services. Using its own fulfillment infrastructure, network of parcel lockers, and last-mile delivery services, the company aimed to improve consumer convenience and speed up deliveries. This meant that Allegro needed to have greater control over these services, to support their transition from an online to omnichannel player. The challenge in Allegro’s case was having the right analytics platform and tools in place to support their network expansion in 3 key areas: strategy, execution, and performance measurement. Specifically, the main goal was to drive an efficient and cost-effective growth strategy for its network of parcel lockers, a type of delivery that currently makes up a staggering 70% of deliveries in Poland.

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.

Kroton, one of the largest private educational organizations in Brazil, was struggling with the processing and analysis of millions of records of government information regarding population segments and demographics. The lack of a visualization tool made it difficult for them to make fast and accurate insights, which was critical for efficient market analysis. They needed a platform that could provide specific information on educational branches and geomarketing resources, and could be integrated with their business intelligence dashboard, Qlikview. Previous software they tried had difficulty handling the large amounts of data.

BBVA Data & Analytics identified a gap in the market to provide location-based insights to small to medium enterprises (SMEs) in Spain, which drive a large percentage of the country's economic activity. They wanted to develop a solution that could answer questions related to Performance Management and Site Planning. Point of Sale (POS) card payment terminals provided a wealth of historical sales data, which was then aggregated and anonymized to ensure statistics on retail activity could not be traced back to individual merchants or transactions of specific customers.

The 7.1 magnitude earthquake that struck central Mexico on September 19, 2017, caused significant damage to the infrastructure of Ciudad de México (CDMX), posing unprecedented obstacles to response efforts. The earthquake destroyed 228 buildings in the city, with many more at risk of collapse, putting even more residents at risk. The immediate aim of the Commission for the Reconstruction, Recuperation, and Transformation of the City of Mexico was to rescue people trapped by the rubble and evacuate others in areas at risk of further collapses. However, the damaged infrastructure posed obstacles in terms of assessing citywide damage. The Commission needed a two-way digital resource where citizens could file property damage reports that provided local officials with crowdsourced data on where to allocate emergency resources.

Telefónica’s Smart Cities and IoT team needed to build a Smart Cities dashboard that could process and analyze data from thousands of Internet of Thing (IoT) sensors as actionable insights to enable their public sector customers to run their cities more efficiently and sustainably. They needed an integrated, FIWARE compliant platform to make large amounts of real-time data accessible to the general public and public administrators through data visualizations, interactive maps, and customized reports. The dashboard would focus on verticals most suited to optimization using IoT technologies: parking, street lighting, waste management, environment, transportation, and service monitoring.

Sistema.bio faced several challenges as they expanded their operations globally. They needed to visualize all existing and pending biodigester installations, over 15,000 existing digester locations on one map (and over 30,000 yet to come by 2023), to empower the staff to make informed decisions on their team resourcing and activities. They also needed to conduct market research and site selection for expansion plans. They needed to know where they currently have a strong market presence, and where they could be expanding. Exploring their existing customer base - such as through heatmaps - is a fantastic way to quickly build location into strategy. Lastly, they needed to connect their existing data pipelines and algorithms to optimize the activities of their field staff, whether that’s their credit, installation, sales or training teams. Most of these processes are currently managed in Salesforce.

SIL International and Ethnologue are adapting to a global landscape that is adopting new technologies and growing more interconnected by the day. As Ethnologue looks to be a more powerful resource across sectors, they need to keep pace with rapid technology growth and the need for more dynamic language intelligence. Given the spatial nature of language, where it is spoken and how it either shrinks or proliferates, the ability to visualize these 7,117 living languages was paramount to the educational component of Ethnologue’s mission. Historically, they had been using more static maps as a resource, which is natural given their literary roots. They would subsequently publish the maps online. And while this is certainly useful, especially at the more granular country level, it doesn’t provide a complete and living resource on where languages are spoken EXACTLY. It also lacks a level of interactivity which helps the project to transcend to the goal of language intelligence.

The Australian Football League (AFL) aims for around 3-4 percent growth in community engagement each year. Ensuring facilities are right in each area is an important aspect in attracting new players and retaining current players around Australia. Organisational effectiveness is important to ensure resources go to the right places. In each region, clubs and leagues make requests to the state and then to the national body for funding and the AFL must assess each application based on its merits. Local region managers are also required to make their case to local councils to obtain investment. Communicating the status of a region’s participation gives people on the ground the right starting information so they can take the right actions to improve participation. In the world of data driven decision making, the AFL needed a way to communicate quickly to local and state governments about their local needs. Communicating with local authorities using a standardised report that wasn’t tailored to a region made it more difficult to convey the AFL’s message.

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.