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.