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ADEO Services, a technical support provider for ADEO, the world’s third-largest home improvement company, faced a significant challenge in maintaining cloud compliance and optimizing its infrastructure. The company was implementing a new data platform within the Global Tech and Data Platform for ADEO in 2018, using a site reliability engineering (SRE) approach with Google Cloud operations suite. However, for certain services, it required custom options and the ability to automatically monitor services in real time, detecting noncompliance errors as they occurred. The operations team needed a tool that could address inventory, accountability, and real-time monitoring. While ADEO was already using Cloud Asset Inventory, the team wanted to have up-to-date overviews across all its services and be able to cross-reference data errors. They also wanted real-time compliance monitoring to ensure systems were compliant at the point of development.

ADEO Services, a company with a mission to inspire homeowners and help them create their dream home, faced a challenge in managing its vast data platform. The platform was established in 2018 as part of a digital transformation project and was designed to collect, store, and deliver capabilities that enable all of ADEO’s companies to search, consult, and use data easily. The data platform team adopted a site reliability engineering (SRE) model to administer the platform, focusing on keeping services running and users happy while identifying opportunities to automate repetitive work. However, operating in a systematic way, at scale, while staying secure and compliant with company policies, proved to be a challenge. The team needed a solution that would allow them to monitor services using self-managed solutions and improve the experience of users with automated SLO monitoring.

Adloox, an ad-verification and insights platform, was facing challenges in delivering timely and in-depth insights to its clients due to its existing infrastructure. The company was dealing with a complex digital landscape where fraudulent traffic, lack of transparency in delivery and reporting, and the ability to track real ROI were among the top challenges. Adloox's platform was designed to address these issues by ensuring ads are viewable, impactful, and delivered in a brand-safe environment. However, the company was processing huge amounts of data, with more than 7 billion fraudulent bid requests avoided every day. Its existing infrastructure, built on dedicated servers, was not scalable enough to handle the increasing data load and traffic peaks. The company was constantly over-provisioning and monitoring its infrastructure to make adjustments, which was time-consuming and costly.

Affable, a Singapore-based startup, has built an AI-based influencer marketing platform that tracks over 1 million micro-influencers across various social media platforms. The company's challenge was to process vast amounts of data and images to recommend key micro-influencers to clients for promoting their products and services. The business was processing 100 million events through its data pipelines and using machine learning models to serve up to 20 million image requests per day. The agility and responsiveness were critical to the success of the business. However, the company was struggling with the fast delivery of accurate, actionable influencer data to meet client demands. The company initially started running its platform on a traditional cloud service, but soon saw an opportunity to use machine learning and big data analysis to create even more value for its clients.

AfterShip, a Hong Kong-based company providing e-commerce shipment, label and rate calculation, and self-service return services to 300,000 merchants worldwide, was facing a significant challenge. The company was experiencing rapid growth, tracking about 30 million packages per month and doubling its revenue, package transaction numbers, and team size every year for the past three years. However, it was struggling to maintain this growth trajectory while automating key infrastructure processes, implementing a continuous deployment model, and controlling costs. The company was also keen on maintaining a global presence and high-quality service. AfterShip had initially delivered its applications and services from an incumbent public cloud service, but it was not meeting their needs for scalability and cost-effectiveness.

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.

AirAsia, a leading airline in the Asia Pacific region, aimed to become a 'data-first' business and a digital airline. The company needed technologies and services that could capture, process, analyze, and report on data, while delivering value for money and meeting its speed and availability requirements. The airline also wanted to minimize infrastructure management and system administration demands on its technology team. AirAsia realized that only a cloud service could meet its needs and began evaluating the market. The company also faced challenges with its legacy on-premises directory as it expanded to new markets and regions. Managing multiple servers across a number of on-premises data centers and the public cloud proved costly and time-consuming.

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.

Akeneo, a global leader in product experience management, was facing a significant challenge. The company had developed a comprehensive Product Information Management (PIM) solution that centralized product data, making it easier to enrich and update. However, the company was running into issues by 2016. While clients were pleased with the product, they had to either host the Akeneo PIM themselves or work with local partners to host it and get it running. This was a barrier for many potential customers who lacked the technical expertise. The existing on-premises infrastructure wasn't built for a web-based product, so Akeneo had to look to the cloud. However, they needed a cloud provider that could provide high-quality service globally and wouldn't be seen as a competitor by its retail customers.

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.

American Eagle Outfitters, a leading US clothing manufacturer and retailer, was facing the challenge of staying relevant to changing customer preferences. The company needed to innovate with investments in pricing, promotion, assortment, technology, and in-store experiences. However, executives needed confidence in the ROI of these activities to justify the associated spending. The company was also dealing with an increasing amount of data from various sources including web, mobile, and third-party data combined with transaction, inventory, weather, mobility, and other market data. This presented new opportunities to understand and shape customer behavior but also required a more efficient approach to data warehousing to increase speed and scalability.

AntVoice, a French startup, was faced with the challenge of improving the effectiveness of online advertising. Traditional ad selection methods, which analyze customers’ recent shopping history and target similar products, often resulted in customers seeing ads for products they’ve already bought. This not only wasted money on redundant ads but also risked tarnishing the brand's reputation. AntVoice aimed to solve this problem with its “predictive targeting” AI, which required a robust infrastructure to handle large volumes of data and perform complex computations at high speeds. However, as the company grew, it became clear that their existing infrastructure was not sufficient to support the heavy-duty requirements of their final product.

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.

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.

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.

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.

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.

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.

Bending Spoons, a Milan-based app developer, was faced with the challenge of standing out in a saturated marketplace where hundreds of apps often offer similar functionalities. The company needed to make data-driven decisions to identify the best apps to develop and optimize them for user experience. Their business model, built around auto-renewable subscriptions and in-app purchases, required a deep understanding of user behavior. However, to make these data-driven choices and grow the business, Bending Spoons needed to analyze large volumes of data very quickly. They were looking for a powerful data storage and analysis system that didn't require specialist technical support. Additionally, as a start-up, they wanted to grow quickly without investing too much in operations and infrastructure, but also needed the capacity to expand rapidly once the business grew.

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).

Billie, a German fintech startup, offers online invoice factoring services to its customers, helping them to advance payments for their outstanding invoices. As an intermediary in handling payments, the company faced the challenge of ensuring utmost reliability and security. Unlike its competitors who operate as subsidiaries of large, established firms, Billie operates independently and had to obtain its own operating license from the German financial authorities. The company needed to build a stable, scalable platform from scratch that could adapt to market conditions and gain the trust of their clients. The challenge was to create a platform that was not only secure but also capable of scaling quickly and efficiently.

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.

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.

The Australia Rail Track Corporation (ARTC) is a crucial part of Australia’s supply chain, managing and monitoring goods transportation across 8,500km of rail network. The corporation collects vast amounts of data to ensure the timely delivery of cargo freight. However, ARTC was dealing with the complexity of siloed data across several segregated data sources, which posed inefficiencies and unnecessary complexity. This also impacted the cost of managing a network that is segregated across remote locations in Australia. The data sets were required by more than 150 applications generated from different data sources, meaning any change to the data needed to be altered in several systems. ARTC embarked on a business transformation strategy to digitally modernize and manage physical rail assets more efficiently, while building resilience to readily respond to customer demands, regulatory compliance, and varied world events.