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Boxed, a leading digital wholesaler, was founded in 2013 with the aim of making bulk shopping easy, convenient, and accessible for consumers. As the COVID-19 pandemic hit the U.S., Boxed experienced a significant increase in traffic and demand on its platform. The shift towards online grocery shopping, which had been gradual, suddenly accelerated, and Boxed needed to scale up its infrastructure and data processing to keep up with the flood of orders that nearly doubled during the pandemic. With the large increase in concurrent users on the site, Boxed’s database was hit with thousands of read/write operations per second, specifically for operations like creating new user accounts, adding/removing items to cart, and checking out. These actions required writes to its database, and Boxed had to scale up the resources available to its database to handle this increase in operations per second.

Brainly, a Kraków-based technology company, provides an online learning platform where students can ask questions and get answers instantly. The platform is used by over 350 million students and parents across 35 countries. However, Brainly faced a challenge in making its platform more accessible and user-friendly for mobile users. Traditional typed queries on smartphones were cumbersome and less efficient. Furthermore, Brainly needed to ensure that its solutions were multilingual, given its global user base. The company also faced the challenge of maintaining stable service during peak usage times, especially during the COVID-19 pandemic when the platform gained tens of millions of new users.

Breuninger, a luxury department store in Germany, was facing a challenge with its complex IT landscape. The company was fragmented into many departments, each with their own technology stack focusing on their own use cases. They had on-premises databases and other systems such as SAP, all gathering different types of data for different business units. This dispersed IT landscape made it difficult for the company to make the most of its data. Furthermore, the company's online storefront, which brought in a significant 30% of sales in 2018, presented exciting opportunities with data. However, to optimize the website and make the online customer experience smoother and more tailored to individual shoppers, Breuninger needed to get its data on track.

Americanas sa, a Brazilian retail giant, faced a significant challenge in delivering the best customer experience across its 1,700+ physical stores and ecommerce platforms. The company, which emerged from the merger of Lojas Americanas and B2W Digital, had a diverse audience spread across Brazil, with over 90 million registered customers and 46 million unique users. Data analytics was crucial to understanding and improving the shopping journey of this diverse audience. However, the company's data environment, initially hosted on physical servers, was unable to keep pace with its expansion rate. The infrastructure lacked the maintainability and scalability needed to manage the growing data volume. The business required more efficiency to test hypotheses, validate concepts, and assist in decision-making processes. In 2017, the company decided to migrate its data environment to the cloud to improve scalability, governance, security, and speed.

Harver, a pre-employment assessment software company, was facing the challenge of scaling to meet the demands of its growing enterprise customers. Initially, the company relied on labor-intensive statistical analysis in R or Excel to surface hiring metrics to their customers. However, as Harver continued to grow and add more enterprise customers, it had to automate and scale processes in order to provide their growing customer base the insights and customization they expected. The company needed an analytics solution that could also support its multi-tiered product offering, integrate with other tools such as Zendesk, and support multi-tenancy. In addition to the technical functionality, it was also critical that an embedded solution could appear seamless and natural within the impeccably designed Harver platform.

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.