Apiiro, a cloud-native application security platform, was faced with the challenge of providing complete visibility of all application components to security and development teams. This was crucial in order to map the application attack surface and proactively remediate critical risks before releasing to the cloud. The complexity of security issues was increasing as software development accelerated and businesses used agile models to deploy more apps at a faster rate. Traditional application security tools were proving ineffective in improving a company's security posture due to the intricate combination of multiple factors that contribute to risks. Furthermore, Apiiro needed to ensure near-perfect uptimes to deliver resilience and grow its business on Google Marketplace.

Arab Bank, a leading financial institution in the Middle East, recognized the need to innovate and modernize its services to maintain its market position and attract new customers. Despite its strong presence in the market, the bank faced challenges in responding quickly to the growing consumer market and the small and midsize enterprise (SME) sector. The Middle East and North Africa (MENA) region, where Arab Bank operates, has a largely unbanked population, with a significant portion interested in new digital tech capabilities. The bank also had to deal with legacy systems and applications, which made it difficult to release new features and functionalities frequently. Furthermore, Arab Bank had to navigate regulatory constraints such as data residency rules in the countries where it operates.

Body & Fit, a leading sports nutrition and dietary product business, was facing challenges with its existing ecommerce platform. The company, which processes almost two million orders a year and offers more than 3,000 items, was planning a strategy of rapid growth, aiming to double its presence from seven to twelve countries. However, scalability was a challenge on its existing ecommerce platform, and weak integration between components made it difficult to track customer journeys across touchpoints. The company needed a solution that could deliver excellent customer experience, potential for global expansion, continuous optimization, and personalized content. Furthermore, Body & Fit wanted to create a unified customer journey by combining data from multiple sources, including its ERP, ecommerce solution, event-stream processing, frontend interfaces, online marketing, customer service solution, and analytics.

Bonnier Publications, a Copenhagen-based media company, was facing the challenge of transitioning from a print to a digital multi-format business model in an ever-changing media environment. The global magazine industry was shrinking every year, and Bonnier knew that this digital transformation was critical for its survival and growth. The company set a six-month deadline to achieve this goal, with the challenge being a complete overhaul of its customer data and the way it engaged with customers online to maximize ecommerce conversion rates. The initial target was to gain 25,000 new subscriptions. Bonnier's success largely depended on its reader base, with B2C business accounting for 75% of its revenue. Therefore, it was crucial for the company to understand its readers and their preferences.

Book Creator, a self-publishing tool, aimed to shift its focus towards serving the K-12 market and enhance student engagement by unlocking their creativity. The company wanted to provide a platform where students and teachers could collaboratively create and publish ebooks and learning materials. However, with a small team of 13, they faced the challenge of reaching millions of students and handling high-variability workloads. Additionally, they needed to comply with strict data access management requirements including GDPR in the European Union and FERPA/COPPA in the U.S. The company also wanted to rapidly develop new features while reducing the complexities of storing data and scaling access to it.

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.

Airbus Intelligence, a unit of Airbus Defence and Space, provides comprehensive satellite imagery and data to over 1,200 customers in more than 100 countries. Over the years, Airbus Intelligence has expanded its client base from governmental security to include entities that benefit from high-quality imagery such as agricultural companies. However, the company faced a challenge in meeting the growing expectations of its customers due to the limitations of its existing solution. The traditional process involved capturing images from satellites, processing them, and downloading them into the company’s library. Customers would then select the images they wanted from a catalogue and receive them several hours later. As Airbus added more satellites and technology became more sophisticated, the company was able to provide better quality data and imagery at increasingly large volumes. However, the existing solution could not speed up the production and delivery of its image and data products.

BBVA, a leading bank in Spain, was facing the challenge of digital transformation in the highly regulated banking industry. The bank was under pressure to compete with digital-native fintech companies while maintaining the trust of its 78.9 million customers. The banking industry, being an attractive target for cybercriminals, had traditionally kept their IT infrastructures on-premises to maintain control over data security and privacy. However, BBVA recognized the need to embrace cloud computing to gain agility and anticipate future banking needs. The bank also aimed to reduce its carbon footprint and become an environmentally sustainable business. The challenge was to navigate this transformation while maintaining stringent data security and privacy standards.

AgroStar, an ecommerce platform selling farm supplies, was facing challenges in expanding its services to small farmers in India. The company aimed to provide a full-service platform that combines agronomy, data science, machine learning, and analytics to boost crop yields and improve income for these farmers. However, the lack of access to new, higher yield seeds and improved soil analyses for small farmers, who had to rely on traditional methods, was a significant hurdle. Additionally, the dissemination of innovative information from universities to small, grassroots farmers was slow and inefficient. The company also faced the challenge of providing timely advice in five languages on various aspects of farming, including seed optimization, crop rotation, soil nutrition, and pest control.

Airwallex, a global payments platform, was facing challenges in expanding into new markets and complying with stringent regulations such as the Payment Card Industry Data Security Standard (PCI DSS). The company's existing cloud service was unable to support its growth plans and ensure compliance with these regulations. As a startup, Airwallex also needed to focus on the application layer of its offerings rather than dedicating time and resources to infrastructure. Furthermore, the company required high security, reliability, low latency, and manageable costs to build and retain trust with its customers. The company also needed to ensure low latency to provide data consistency and offer low rates to customers without applying a high spread to account for rate fluctuations due to the fast-moving nature of financial markets.

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.

A2A, an Italian utilities provider, faced the challenge of scaling its business to meet the needs of over 2.5 million customers while also striving to meet the United Nations’ 2030 sustainability goals. The company aimed to double its client base by 2030, in line with the liberalization of the Italian energy market. However, the increasing customer numbers, shifting expectations, and the uptake of new technology, such as smart electricity meters, made it difficult to predict infrastructure requirements. A2A's data warehouse would refresh every 24 hours, which was not sufficient for real-time customer service. The company needed a solution that could provide real-time data to respond to customer needs immediately, especially in critical situations such as power cuts.

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.

ATB Financial, a financial institution with $55.1 billion in assets, was seeking to enhance its IT environment and improve client experiences. Despite having a loyal client base, ATB faced growing competition from larger banks and FinTech companies. To stay competitive, ATB launched a 'Work Reimagined' initiative, powered by Google Workspace, to improve collaboration and client service. However, the bank wanted to further modernize its operations by migrating from SAP ERP Central Component (ECC) to SAP S/4HANA for improved performance and more dynamic data models. Additionally, ATB planned to move from SAP Bank Analyzer to SAP Finance and Risk Data Platform (FRDP) to consolidate applications on a HANA-based data platform. The challenge was to manage the increasing data storage and compute power needs, which were difficult to handle on-premises.

Blue Apron, a pioneer in the meal kit delivery service industry, faced challenges in managing its complex operations. The company needed to source ingredients at the right time, quality, and price, pack orders efficiently and in exactly the right proportions, and ensure meal kits were delivered to the customer fresh and on time. As data volumes grew and queries became more complex, it became difficult to scale their existing data warehouse hosted on another cloud provider. The only options were choosing ever-larger server classes and increasing storage throughput by purchasing a higher number of provisioned IOPS. This led to a need for a more efficient and scalable solution.

Moderna, a research-driven organization, has always relied heavily on data for its operations. However, the company faced challenges in accessing actionable insights from its data. The majority of employees relied primarily on Excel for data analysis, with some researchers utilizing Spotfire Desktop. These tools required significant manual work and set a high barrier to entry. This manual process led to data silos across the organization, limited opportunity to further explore data, and created issues of consistency resulting from various and conflicting versions of the same report. The company needed a solution that would improve self-service and exploration, maintain data quality and consistency, and ensure the new tool would be cost-effective and integrate with the tools Moderna already had in place.

Sunrun, a leading provider of residential solar power, was facing challenges in managing their growing volumes of data across installation operations, installed systems, customer operations, and sales. The company was using a legacy data stack that required IT and data team support for almost every internal data request. This reliance on IT and the data team drained time and resources with ad-hoc requests, changing requirements, and backlogs of reporting requests. Moreover, the data pipelines and infrastructure weren’t scaling to meet either data growth or increased demand for data access. The data team struggled to respond to changing data sets or new sources of data as quickly as the business demanded, and Sunrun's legacy Oracle data warehouse was not equipped to scale across growing analytics demands or unlock predictive insights with ease.

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.

Boa Vista Serviços, a leading analytical-intelligence and credit-bureau player in Brazil, was facing challenges with its previous technology infrastructure. The company's physical servers located in three data centers were hindering its speed and ability to innovate. Limited scalability of resources and processes meant that they could not train many analytical models simultaneously or implement modern machine-learning techniques. One of its products was on the verge of halting sales because the company's processing capacity had reached its peak, directly impacting business expansion. The company needed to embrace digital transformation by migrating all its information from physical servers to the cloud. They also needed to switch from a traditional organizational model to a squad-based model to focus more efficiently on various technology and business aspects and challenges.

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.

Grand Rounds, a healthcare company, was established to connect patients with the most qualified experts for their medical needs. The company works with firms of various sizes, from 1,000-member firms to Fortune 50 employers, in over 120 countries. They provide expedited access to top physicians for better health outcomes. However, Grand Rounds faced a challenge in managing and accessing its vast network of data. They needed a partner to help build a scalable, external dashboard to provide its customers with valuable reporting.

Deliveroo, a food delivery service, was facing challenges with its initial BI tool as the company expanded. The tool lacked the necessary security, user management, and automation to support the company's growth. Business users were unable to make agile decisions as they had to wait for technical staff to write SQL for each report they requested. Code management became time-consuming for the data team as each dashboard had a unique code base. Changes in the underlying data architecture required updating the code in about 100 different locations. Additionally, variable demand based on the day of the week or time of day could put unpredictable strains on the data warehouse capacity.

PeopleDoc, a leading provider of SaaS HR platforms, needed a solution that could meet its complex requirements while also complying with the legal framework set out by GDPR. The company aimed to optimize communication and streamline administrative processes between HR departments and employees. However, they lacked a system that could handle complicated data modelling, provide access to real-time accurate data, ensure highly confidential HR information was secure and offer an intuitive user experience.

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.

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.

Guidewire saw a market opportunity for industry-specific business intelligence software, but it wasn’t practical to build a solution from the ground up. Guidewire customers relied on the SurePower Innovation® enterprise suite for managing their day-to-day business operations, but they had to use third-party tools for business intelligence. They struggled to make sense of limited canned reports after enduring delays from data transfers, and the reports weren’t customized for the insurance business. Guidewire stepped in to close the gap by adding insurance-specific business intelligence features to the SurePower Innovation® suite. From a technical standpoint, the ideal BI solution had to eliminate data silos to offer 360-degree views of all the data, display interactive data visualizations, and support data exploration in real time. The tool also had to be scalable, provide fast performance, align with the Guidewire public cloud strategy, and integrate seamlessly with the SurePower Innovation® platform.

Commonwealth Care Alliance® (CCA) is a not-for-profit, community-based healthcare organization that provides care for high-cost, high-needs individuals. When the COVID-19 pandemic started, CCA knew their members, who live with complex medical, behavioral health and social needs, would be faced with new challenges and require an enhanced level of support and care. CCA also knew the best way to understand, prepare, and protect their members was through the use of timely and reliable data. The challenge was to quickly build monitoring dashboards and infuse COVID-19 information throughout other existing clinical dashboards to allow CCA's clinical workforce to understand how their members were being impacted, their potential risk, and the best next steps to help each member.

GoCardless, a rapidly growing payment platform for businesses, was facing challenges in accessing accurate and reliable data. The company was using a combination of traditional internally developed business intelligence tools and third-party SQL clients that required manual queries. Non-technical business owners had to submit tickets to the analytics team, who wrote code to extract insights. This process was time-consuming and inefficient. The finance team spent four days at the end of each month running revenue numbers, which delayed critical visibility into revenue trends and frustrated executives. The product team also lacked easy and rapid access to product usage data that could help drive a roadmap for accelerated company growth.

Vivino, the world's largest online wine marketplace, was facing challenges with data management. The company was using a plethora of spreadsheets owned by different stakeholders in different time zones, leading to inconsistent and inaccurate data. This was particularly problematic for the eCommerce side of the business, where data was being used to influence strategy and decision-making as the team scaled globally. In addition, technical users who ran their own database queries needed help making sense of conflicting results from queries run without consistent data definitions across the organization.

Charlotte Tilbury, a rapidly-growing global luxury beauty and skincare brand, was seeking to advance its data analytics processes while scaling up its ecommerce platform to meet growing customer needs across channels. The company needed a unified approach to analytics and reporting that would provide access to real-time, critical data insights across the business, from ecommerce to supply chain and finance.