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.

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.

The telecommunications industry has limited visibility of the end customer quality experience. Assessing mobile phone users' needs is challenging due to barriers of access to actual, accurate customer experience related information. The two standard methods of collecting network quality information, through a drive test and by collecting data within the network from nodes, are either costly and inefficient or only provide an estimation and a very limited network centric view of the customer experience. Telecoms struggled to analyze trends and variances in traditional formats. In some cases, they lacked pertinent information to make timely decisions and take corrective action.

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.

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.

As Sanitas, a healthcare provider based in Spain, grows and diversifies its offerings, it needs to ensure it's expanding in the right places and in the right ways. Being a part of Bupa’s massive operation, which touches 190 countries, finding locations best suited to new healthcare centers is a complex endeavor—as is uncovering the healthcare needs of local populations. Sanitas prides itself on serving clients throughout every stage of life; doing this well across a growing variety of facilities requires a wealth of demographic and location information—as well as the means to organize and understand that data in actionable ways. To fulfill its mission, Sanitas needed location intelligence that could provide dual functions: helping the company make wise choices for future development, and enabling the company to provide exceptional care to local demographics.

JLL, a global leader in real estate services, was looking to scale their Gea solution in key countries such as the US, Australia, the UK, Italy, and France. The Gea platform brings together thousands of data types on Real Estate assets to ensure accurate valuations and analyses are provided to their clients. Connecting to their Big Data Cloud infrastructure (JLL DataHub) would be fundamental in order to ensure high-quality, automated data pipelines could support frequent updates to the billions of data points that would be available in the solution. However, they faced challenges in localization for different markets, handling big data, and ensuring the final application would be user-friendly for consultants with more commercial profiles.

Renault needed national level georeporting for local and regional analysis of their network. The goals of the Renault France team are threefold: optimization, standardization, and a more granular analysis. With 4000 outlets across the country, Renault’s presence in France is extremely significant. Each regional manager is looking for the opportunity to look into coverage levels, local competitive landscape, and outlet by outlet performance data. Having a unified system where their regional leaders can look at their own data to make decisions around resourcing and geomarketing, while standardizing language and process via a single platform across the country, would help meet KPIs for sales and aftersales outlets.

Faraday, a marketer's choice for consumer prediction infrastructure, faced several challenges in predicting the future of an ever-evolving industry. The task required a holistic approach to gathering, visualizing, and segmenting data from a variety of sources. The task became increasingly difficult if the objective was to make these predictions with minimum or no requirements for user technical expertise. Faraday needed a way to visualize vast amounts of data to bring customer personas, segments, and predictions to life and drive faster business decisions. Another challenge was ensuring that these large volumes of data or the level of sophistication of each analytical use case did not impact the speed of the end-user experience. Faraday ingests data from 200 integrations into BigQuery. Therefore, in order to take its predictive analysis solution to the next level, Faraday needed a BigQuery- native Location Intelligence platform capable of seamlessly analyzing and visualizing the vast amounts of consumer household data stored there.

ING’s marketing team wanted to provide an added-value service that would allow consumers to select their neighborhood with more location-based context, based on their own individual needs and characteristics. They noticed that their clients were often focusing on key property information such as square meters, the number of rooms, recency of renovation, or even how much sun it gets, rather than focusing on the location. They wanted to create a neighborhood selection solution, that would allow their existing and potential clients to find the perfect neighborhood for them, based on their needs, preferences and budget. The challenge was to gather and present information in a clear way for consumers, allowing the user to gain new insights from high volumes of spatial data in 5 cities, going beyond simply displaying Points of Interest on a map. The solution also needed to work seamlessly on mobile devices, creating a design that would ensure the mobile experience would be just as intuitive for users.

Perkins and Will, an interdisciplinary, research-based architecture and design firm, faced the challenge of ensuring the safety of their employees as they returned to work amidst the COVID-19 pandemic. The firm needed a comprehensive guide to facilitate the transition back to the workplace, taking into account factors such as employee readiness, distance analysis, and new protocols. The firm had to support the return of 2,600 employees across 21 studios in North America. The process of planning resource allocation in the workspace needed to be three times faster to meet the demands of the situation.

Anagraph, a Montreal-based company specializing in building custom geospatial solutions, identified a gap in the market for small, online businesses. Many of these businesses lacked information about the market and tools to provide insights into buyer personas. Geomarketing solutions have been around for decades, but due to complexity and cost, they have long been accessible only to large enterprises. Anagraph saw an opportunity to bridge this gap and provide a greater range of retail insights to smaller operations, allowing online retailers to build a more comprehensive geomarketing strategy.

Grupo Lar’s asset management and development team were looking for ways to use new types of data, and in particular location data to understand potential investment opportunities - considering more spatial factors in their decision-making. Spain is Grupo Lar’s main market, where it has consolidated a dominant position in the real estate sector, combining investment, management, and promotion. As an independent company with a sole focus on real estate, Grupo Lar is able to establish a strategy based on the best investment opportunity moments, thanks to its extensive knowledge of the sector as well as its ability to access local resources. This position allows them to gather large amounts of insights on a daily basis. Therefore, the main challenge of this project was to provide a solution that enabled them to analyze and visualize all their business KPIs in a simple and intuitive way, while processing the data in real time, in order to streamline the decision making process.