Arpeely, an Israeli ad-tech startup, aimed to revolutionize the media-buying process by leveraging machine learning and feature engineering techniques. The company sought to process billions of ad impressions daily and cherry-pick traffic based on in-app or post-conversion behavior KPIs. However, as a bootstrapped startup launched in 2017, Arpeely faced the challenge of managing global ad operations with a small team. The company needed a solution that would allow it to scale up quickly without having to invest heavily in developing complex services or expanding its team.

Banca Mediolanum, a leading Italian bank, insurance, and asset management company, was facing challenges in managing and analyzing the vast amount of data it was collecting from various sources. The bank's sophisticated marketing operation, which brings in over 10,000 new customers a month, was being hampered by the diverse formats in which data was arriving. The bank was struggling to make the most of its available information and was in need of a powerful data hub that could handle the massive and highly varied data. The bank was also looking for a solution that could minimize infrastructure maintenance, allowing its teams to focus on adding value.

The music industry has undergone a significant shift from physical sales to digital streaming, which now accounts for more than half of all sales globally. This shift has made the process of paying artists more fragmented and complex. Artists are paid a small royalty for each song downloaded or streamed, which means that the volume of data that needs to be processed has grown exponentially. BMG, a Berlin-based international music company, found itself needing to process 1,500 times the amount of data to calculate payments for artists. Until 2019, BMG’s infrastructure was entirely hosted on-premises. The hardware limitations made it challenging to scale on-demand, making it harder to handle the data peaks that royalty processing can bring. The company was facing a ceiling in a few years, and processing royalty payments was becoming increasingly time-consuming and expensive.

7Learnings, a Berlin-based startup, provides browser-based software services that use machine learning tools to automatically optimize pricing for its customers. The company helps online retailers set their product prices based on desired outcomes and tracks the impact of these decisions. The software considers a wide range of data to suggest optimal price changes, including product stock levels, seasonal changes, competitor actions, and upcoming sales events. However, the company faced challenges in storing, managing, and analyzing the vast amounts of data it needed to provide these insights. The data came from various platforms used by its clients, which added to the complexity of the task. Additionally, the company needed a way to visualize the data insights and scale its storage in line with customer demand.

90 Seconds, a leading video creation platform, faced challenges in scaling its operations due to rapid expansion. The company, which manages a marketplace of 12,000 video creative professionals across 70 categories in over 160 countries, was initially operating in a colocated data center in the United States. However, the rapid growth of the business led to scalability and cost issues, particularly around content delivery and storage. The company then decided to move to another cloud service. Despite receiving millions of dollars in venture capital and using new technologies to scale the business, 90 Seconds continued to face issues in supporting the growing demand for cloud video production, accelerating software development, and capturing and analyzing data from multiple services to facilitate decision making.

AB Tasty, a global company with seven offices worldwide, provides an all-in-one website optimization platform that enables its clients to make data-driven decisions to improve conversion rates. The company processes over 17 billion visitors and two billion events daily. An event is a message that corresponds to an action carried out by a visitor on a website, such as a click. However, AB Tasty's previous data architecture, hosted in the cloud with another provider, was not able to deliver the performance necessary for processing the two billion events that AB Tasty handles every day. The company reached a limit of one billion events a day and couldn't move above that, despite various attempts. The challenge was to find a solution that would enable them to scale up the storage volume and processing power while reducing the response time for queries.

Acciona Mobility, a Spanish multinational company, is committed to leading the transition towards a low-carbon economy. The company, which operates in 65 countries, launched an electric scooter sharing service in 2018. The service, powered 100% by renewable energy, aims to contribute to the decarbonization of the transport sector and alleviate traffic congestion within cities. However, to achieve this, Acciona Mobility needed a scalable and reliable IT infrastructure to support its long-term vision. The company also needed to develop an application that would allow users to find, reserve, and rent the scooters easily. Furthermore, the company faced the challenge of managing and processing real-time data from thousands of scooters, ensuring the availability and location of scooters are accurately shown to users.

Before implementing Looker in 2014, Simply Business did not have a centralized data platform or process, which resulted in challenges bringing siloed information together and accessing consistent metrics. Without a single source of truth, many decision-makers across the organization spent their time manually creating their own reports. However, many of these reports and metrics didn’t match up, which led to confusion and mistrust of data, and ultimately delayed decisions and action. The insurance brokerage needed a single source of truth to unify data across its multiple applications. Additionally, since insurance is a highly-regulated industry, it was critical that information be consistent, auditable, and secure.

Raisin, a successful Fintech start-up in Germany, was facing challenges with data management. Much of the company's data was stored in Excel spreadsheets, making it difficult to access and utilize effectively. The rapid growth of the company and management’s vision of a strong data culture necessitated a streamlined approach to data handling. The company's vision was to make data accessible to every employee, enabling them to make as many decisions as possible with data support. However, they lacked the necessary infrastructure to achieve this. They needed a self-service platform that would provide a unified view of all data sources and allow each employee to obtain easy access to the relevant insights.

Waterscan, a leading provider of commercial water management services in the United Kingdom, was facing challenges with its existing business intelligence system. The company was heavily reliant on Excel for its operations, which was proving to be an inhibiting factor for growth. The lack of a business intelligence tool or data model meant that Waterscan teams were constantly uploading and downloading Excel documents to and from their Waterline product to do their jobs. The static nature of Excel reports made it difficult and time-consuming to update numbers with the data from Waterline. Development of new reports or amendments to existing reports used to take up to 4 weeks due to development sprint cycles and release dates. Each new report or amendment would cost Waterscan more than $2,400. Waterscan needed a modern data solution that would save their team time and money in development resources, provide customers with up-to-the-minute insights, and scale as their customer base and demand for data grew.

In early 2014, Dollar Shave Club, a small e-commerce business, was struggling to make use of the vast amount of data they were collecting. They were up against established retail giants in the men's grooming products industry and needed to leverage their data to gain a competitive edge. However, they were facing a bottleneck problem where only a few people could access the data, and everyone else had to wait to get their questions answered. This was causing delays and inefficiencies in their operations. For instance, any reports they needed would have to go through a developer named Juan, which was a time-consuming process. They were also using traditional BI tools, which were not able to fully utilize their data.

Aclima, a San Francisco-based company, specializes in the design and deployment of environmental sensor networks. The company aimed to measure, map, and better understand air quality across America’s cities. The challenge was to track air quality at a granular level, block-by-block and neighborhood-by-neighborhood, which had never been done before. The company's fleet of Aclima-equipped Google Street View cars had to collect nearly a billion data points, covering more than 100,000 miles. This massive data collection required a scalable, secure platform to manage what was quickly becoming one of the largest datasets in the world. The company needed a solution that could handle vast amounts of data and provide scalable growth potential.

AgrodatAi, a Colombian company that develops technological solutions for the farming industry, faced a significant challenge in organizing and centralizing scattered information across the Colombian farming industry. The company aimed to provide a centralized service that would help stakeholders in the production chain make better decisions based on their business needs, location, and products of interest. One of the company’s main goals was to calculate the best locations to sell products, requiring a model to link the location of the production units (farms) with marketplaces or livestock auctions throughout Colombia. However, the lack of internet coverage and limited access to computers in Colombia’s rural areas presented AgrodatAi with its first challenge: building a 24/7 tool. Another challenge was digital literacy, as the tool needed to be accessible and easy for users to use and understand.

Altavia, a global retail marketing and publishing service provider, was facing challenges with its legacy infrastructure. The company's infrastructure, which ran on 300 servers located at its headquarters in Paris, was outdated and causing issues for the growing business. The in-house developed ERP system suffered from weekly crashes, and services for clients in distant locations like Japan, Korea, and Canada were significantly slower than for those in Europe. Altavia needed to keep the technology underpinning its services up-to-date to manage its printing processes efficiently, optimize logistics for production and distribution, and use data analysis to understand clients' data. In 2019, Altavia decided to renew its legacy infrastructure, which required significant investment in hardware to improve its performance and reliability.

AnyMind Group, a rapidly expanding company with three core businesses in advertising, marketing, and human resources, faced the challenge of managing its growth across Asia. The company needed intelligent workplace applications that would allow staff in different countries to collaborate on business planning, new product launches, and other important tasks. Additionally, they required a stable, scalable infrastructure that could meet client expectations while minimizing the maintenance and administration load on technology staff. The company also needed to accommodate 5X growth fluctuations in user numbers and add hundreds of new clients per week without compromising the speed and stability of its service.

AODocs, a content services platform, was faced with the challenge of integrating document management directly into the Google Workspace environment. The company aimed to provide a solution that would allow businesses to organize their data and documents effectively, establish permissions and ownership controls, automate business workflows, apply retention policies, and comply with standards and regulations, all while saving infrastructure and administrative costs. The challenge was to build a platform that could scale with any project size without impacting the performance of other users on the multi-tenant platform. Additionally, the platform needed to be secure against all possible security incidents, from network intrusions to hackers or denial of service attacks.

Apester, a company that provides tools for creating, distributing, and monetizing interactive visual content, was facing challenges with its business intelligence (BI) and data warehousing systems. The existing solution was adequate for small amounts of data, but as the company grew, attracting approximately 100 million unique users per month, it began to show signs of strain. The system also placed limitations on the kind of analytics Apester could run. The company wanted to capitalize on its growing customer base and gain as much insight as possible, without worrying about scale or cumbersome licensing fees. Additionally, Apester’s developers and data scientists wanted to use open source technology as much as possible to avoid over-reliance on any one vendor.

Arcules, a Google Cloud partner and a fast-growing Canon company, was launched to pursue new opportunities based on Milestone’s 20 years in on-premises video monitoring and video management software solutions. The company aimed to provide a unified solution that analyzes data from video cameras and IoT devices for actionable insights to improve security measures and boost operational efficiencies. However, Arcules faced a significant challenge in terms of latency due to the high bandwidth of video data. During the testing and development of its solution, the Arcules IT team expected to see up to 8 seconds of latency. Storage was also a major concern for the company, as the platform was expected to handle a large volume of video and IoT data. Additionally, the company wanted to eliminate mundane infrastructure management tasks such as security patches and upgrades to focus on higher-value, customer-centric solutions.

Area 1 Security, a leading anti-phishing solution provider, was faced with the challenge of analyzing massive streams of information to identify and preempt targeted phishing attacks. The company needed to process over 3 billion events every day and manage a data warehouse of approximately 3 petabytes, including a quarter of a trillion attack metadata records. The company's service required the ability to analyze this vast amount of information using sensors across the internet, a high-speed web crawler that spiders up to six billion URLs every month, and a distributed sensor network that gathers billions of network events in a day. The challenge was to find a scalable, high-performance platform with sophisticated data analytics tools that could handle this massive data load and help identify impending attacks.

ATB Financial, a Canada-based financial institution, was facing challenges with its legacy data platform. The analytics processes were ad hoc and deeply manual, often requiring multiple data copies and heavy manipulation to produce reports. The legacy data environment needed enhancements to support future business requirements that have a high dependency on real-time data analysis and insights. ATB was only able to manage structured data using its traditional data warehouse platform. The bank was seeking to innovate to expand services, increase client satisfaction, and achieve operational efficiencies to compete with big, established banks as well as financial technology startups.

Auka, a Norway-based fintech company, provides white-label technology to banks on a SaaS model. The company connects banks, consumers, and merchants, enabling a new generation of commerce and financial services. However, Auka faced the challenge of building a safe, reliable payments platform that could also scale at great speed. The company's home market, Norway, had over 2 billion card transactions in 2016 alone, the highest per-capita rate in the world. The mobile wallet adoption rate in the Nordic region was even more impressive, reaching 65% in 2016. This paved the way for mobile payments to replace cash and cards in a few years. Auka needed a solution that could handle this extreme scaling, from ten to thousands of transactions a second, while also complying with international financial regulations.

Banco de Crédito del Perú (BCP), a leading financial institution in Peru, embarked on a transformation journey to enhance its digital sales and improve customer experience. The bank's digital marketing department sought to increase cross-sell rates and the average number of products per customer. However, they faced challenges in implementing new strategies on the bank's website front end, such as A/B testing with propensity models and maintaining secure lead databases. BCP also recognized the need to simplify the purchasing process for customers, as many people have a negative perception of banks and only purchase their products when necessary. The bank aimed to overcome these challenges in a market that is striving to increase banking penetration to 60% of the country's population in the next few years, amidst a digital transformation accelerated by the COVID-19 pandemic.

Benefício Fácil, a Brazilian company specializing in the management and distribution of employee benefits, was facing a significant challenge. The company was experiencing rapid growth and had introduced a new feature that allowed clients to split monthly benefit orders into weekly ones. This feature increased data processing four times all at once, putting a strain on their existing infrastructure. At the time, Benefício Fácil was running its systems on Amazon Web Services, using in-house personnel to manage and maintain the platform. However, as system performance began to slow due to the increased processing burden, the company started looking for alternatives. They needed a solution that could offer scalability and performance without requiring extensive time spent on infrastructure management.

Bepro, a Korean company, operates Bepro11, a platform that records and analyzes football matches using machine learning. The platform records matches of over 500 professional football teams worldwide in real time, edits all in-game situations within 24 hours, and makes them available online for review. The challenge for Bepro was to handle large video files, process them quickly, and provide related services efficiently. The company needed a reliable and fast network and storage solution to record and transmit match records, process and analyze videos, and deliver the analyzed data to the clubs. The company also needed to automate complex video processing tasks and handle large datasets without a Content Delivery Network (CDN).

Bit Capital, a Brazilian startup, has been developing 100% digital financial solutions in the cloud since 2018. The company's goal is to help its clients adapt to the rapidly changing financial industry, driven by new technologies and regulatory changes. The launch of Pix, a new payment method by Brazil's Central Bank (BC), presented a unique challenge. Pix required a solution that could be used by both direct and indirect participants, without the need for them to build an infrastructure or hire different providers. Bit Capital needed to develop this solution quickly and efficiently, while ensuring high security, scalability, and availability. The company also faced the challenge of integrating its platform's solutions with those from other companies.

33Bondi, a technology, product, and design consultancy based in Sydney, Australia, was faced with the challenge of delivering powerful advertising, marketing, technology, and transactional solutions to clients within tight timeframes. The company needed to manage spiky demand, particularly when sending out large volumes of emails to a user base of approximately 20 million. In one specific case, a bicycle insurance company wanted to launch a web presence in Australia, the United Kingdom, and the United States within 12 weeks, and needed to partner with a prominent social network for athletes. This required 33Bondi to create multinational web presence incorporating full transactional capabilities within a very short period. The company also needed to ensure that the solutions they provided met key security and compliance requirements, and instilled confidence in their clients.

The rise of online marketplaces such as Amazon, eBay, and Walmart.com has led to an increase in third-party sellers, some of whom sell counterfeited or misrepresented products. This practice not only deceives consumers but also damages the reputation of manufacturers, retailers, and brands, leading to significant revenue loss. Despite efforts by marketplace websites to address this issue, the problem persists. 3PM Solutions, an eCommerce brand analytics and protection company, sought to provide a solution to this problem. However, the company faced challenges in processing and analyzing large, unstructured datasets related to third-party sellers. Their existing hosted cloud environment with Hbase clusters running on top of Hadoop required significant manual deployment and maintenance, which was time-consuming and inefficient.

99.co Group, a property technology business operating in Singapore and Indonesia, was faced with the challenge of improving the property search experience for its 20 million monthly active users. The company wanted to redesign the user experience in the renting and buying journey through powerful search, layered with non-transactional data dimensions such as travel time, point of interest, and other attributes. The company also wanted to differentiate itself by classifying listings based on their quality rather than the conventional 'pay-to-list' model. Another challenge was the need to revamp the infrastructure when the group acquired Rumah123 in Indonesia. The original technology stack was hosted from servers in Australia, which affected the platform's performance. The company also aimed to attract a strong talent pool of engineers and scale up its operations to keep up with the fast-growing number of daily users.

AB Tasty, a leading customer experience optimization SaaS provider, was facing challenges with its technical infrastructure in 2017. As the company took on bigger clients, the management of increased web traffic became a significant issue. The cloud-based infrastructure, designed around virtual machines, was proving to be inadequate for the scale at which the business was operating. The company needed a new kind of architecture that could handle massive amounts of web traffic and provide high-quality data to its clients. AB Tasty's platform, which allows clients to experiment with new website designs, apps, and other features, was also in need of an upgrade to provide more resonant campaigns for their chosen demographics.

abastece-aí, a fintech company with over 36 million customers, was facing a significant challenge in its growth trajectory. The company lacked a structured data environment that could cross-reference data from various sources and analyze longer historical periods. This lack of a comprehensive data environment resulted in delays in data acquisition, hindering operations. The company also needed dashboards with automatic updates to streamline its operations. The company was in urgent need of a service provider that could offer a customizable solution and enhance the insights generated from its data. The requirements for the new service provider included lower deployment cost, shorter development time, improved scalability, a steeper learning curve for employees, and more tools for integrations.