数字批发商 Boxed 在 COVID-19 大流行期间其平台上的流量和需求显着增加。该公司需要扩大其基础设施和数据处理规模，以应对大量订单的涌入。

在教育中，学生在课堂外需要帮助时经常面临挑战。 Brainly 旨在为学生提供一个 24/7 提问和获得专家指导的平台。

Breuninger 需要将其分散的 IT 环境整合在一起，并统一许多不同的事实观点，以便为整个组织的决策提供信息。

为遍布巴西的多元化受众提供最佳的客户体验

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.