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Hunt, Gather, an Austin-based creative agency, was struggling with limited reporting tools that hindered their ability to share deep performance data with clients. The agency was in dire need of a holistic approach to the reporting of its digital marketing efforts. They required a suite of tools that would enable the collection and analysis of data, and ultimately the generation of key insights, all in a single location. The development team had previously built a few pipelines on their own, but these were time-consuming and costly. It could take up to six months to build pipelines in-house, and the team was also spending significant amounts on ELT platforms that were proving to be inefficient.

IJsvogel Retail, a Dutch pet and garden products chain with a history of nearly 130 years, was grappling with the challenge of managing and leveraging its vast and disparate data. With over 180 stores, more than 1,600 employees, and over 800 wholesale customers, the company was generating a significant amount of data. However, this data was not being effectively utilized to inform business decisions. Instead, old data and log files were often discarded rather than compiled and analyzed. The company's small IT department found it difficult to promote the adoption of new applications across the company. The lack of a unified, reliable, and stable data source was hindering the company's ability to make informed business decisions.

Imperfect Foods, an online grocer dedicated to eliminating food waste, faced a significant challenge in managing and utilizing its vast customer data. With hundreds of thousands of customers and an increasing number of data sources, the company struggled to act on this information effectively. The lack of a centralized view of the customer data made it difficult to understand what traits led to high-value customers or what factors influenced customers to order. Imperfect Foods needed a way to consolidate all of its customer data across its entire data stack to leverage it for marketing activation to increase signups and product usage. The company was also limited on engineering resources, which further complicated the situation.

Involve.ai, a customer intelligence platform, was facing challenges in providing its customers with a holistic view of their customers due to the inability to pull data from multiple data sources efficiently. The process of data integration was time-consuming and resource-intensive, with unreliable and difficult-to-modify data schemas. The company's clients required different approaches and specific apps tailored to their sales and delivery processes, which the previous data integration solution could not scale to meet. Without access to data from source systems, Involve.ai was unable to produce comprehensive insights, leading to a more reactive approach to data analysis. The challenges included an inability to produce comprehensive and accurate insights, inflexible automation for scheduled data replications, no way to perform data transformations prior to importing into Snowflake, and slower time to market, which limited the company’s growth rate.

The global Covid-19 pandemic forced restaurants worldwide to quickly pivot to delivery and take out services. ItsaCheckmate stepped up to help these restaurants consolidate orders from various ordering apps directly into their existing Point of Sale (POS) systems, eliminating the need to manually transfer the orders to the POS and manage their menus on multiple platforms. With business booming, ItsaCheckmate decided it needed to use data to maintain quality experiences for its customers and enable the support staff to handle an increase in orders. The data was available, but it was cost prohibitive for the company to organize and manage it in any meaningful way. The ItsaCheckmate platform is powered by dozens of integrations with online ordering apps such as Uber Eats, Grubhub, and DoorDash, as well as with all the POS systems that large chains or small mom-and-pop restaurants may use. When an order cannot be processed properly, ItsaCheckmate can resolve each individual error in real-time, but analysts need to conduct a thorough and rapid post-event analysis to resolve the underlying issues that cause these errors to arise to begin with. Systematic analysis of this siloed data was a manual process, requiring analysts to pull a list of order errors into an Excel spreadsheet – a process that could take up to a day.

JetBlue, a major airline operating over 900 flights daily to more than 110 cities, was grappling with the challenge of managing and analyzing the vast amount of data generated by its operations. Every person, plane, and journey generated data points that could provide insights into customer sentiments, revenue forecasting, fuel consumption, aircraft maintenance, and operational readiness. However, the sheer volume of data, sourced from 130 different systems, was overwhelming and difficult to organize. The airline needed a solution that could centralize this data, making it readily accessible for analysis and decision-making. The challenge was to bring all this data into a single platform quickly and accurately for analysis.

Kilo Health, a global leader in digital health and wellness, faced a significant challenge as it rapidly expanded. The company, which started with just seven people in 2013, has grown to over 700 employees managing more than 30 products with over 5 million customers worldwide. This rapid growth led to an exponential increase in data points, which the company needed to manage effectively to become a fully data-driven organization. The challenge was to find a solution that could support this rapid growth and provide intelligent and unbiased insights to stakeholders.

Kuda, a digital bank launched in Nigeria in 2019, experienced a fourfold increase in customers within six months. As a mobile-first digital company, Kuda recognized the need to be data-driven and identified the modern data stack as essential for achieving its goal. Initially, a five-person data team was manually building data pipelines and relied on SQL Server Reporting Services (SSRS) to extract insights from transactional databases. Their data was split into 12 disparate Azure SQL databases with no way to successfully join the data across their internal sources. The team was looking to move from running OLAP queries on an OLTP database, which was proving to be a challenging task. They needed a more scalable solution that would relieve them of having to build and manage data pipelines.

Learnerbly, a learning and development marketplace, was facing significant challenges in managing and utilizing its data. The company had no dedicated data organization, leading to data being siloed across different departments. This lack of a consistent source of truth made it difficult to compare and corroborate records across different sources. It was also impractical to join records across data sources, which impaired their ability to service clients effectively across their entire life cycle. Furthermore, engineers were often diverted from product development to data operations. The company needed better access and control over its data to scale and attract enterprise clients, who have higher employee headcounts and more stringent demands regarding visibility into ROI of adopting a new platform.

Lendi, an Australian mortgage broker with over $12 billion AUD in home loan settlements, was facing a significant challenge in its data management. The company's proprietary technology allows borrowers to search over 2,000 loan products from more than 40 lenders, making it a competitive player in the market. However, the industry's competitiveness and the need to deliver the right experience to the right person at the right time on the right digital platform required reliable, accurate insights into borrowers' needs and preferences. The problem was that building an accurate profile of each customer required tapping into behavioral data on third-party engagement platforms such as Facebook, Google, and Bing. This data was readily available to Lendi, but the insights from each platform were siloed and didn't integrate easily. Even when the data could be brought into the same repository, the data structure was often inconsistent, creating the need to clean the data before it could be put to use.

Lufthansa Systems, a division of Lufthansa Airlines, is a leading provider of IT services in the airline industry, serving around 300 airlines worldwide. One of its offerings is Lido/FPLS (flight planning services), which optimizes flight routes in terms of cost, fuel, and time, generating millions of dollars in extra profits for its customers each year. The challenge was that creating these optimized flight plans required massive amounts of data, including up-to-date weather reports, air traffic data, and airline-specific data such as flight schedules, payload, operational conditions, and contracted petrol prices. Lufthansa Systems needed a solution that would allow its central data repository to receive continuous updates from these data sources and distribute optimized flight plans and other data to each customer's site.

Malt, an online freelancer marketplace, was facing challenges with its data ingestion process which was slowing down analysis. The existing custom-built ingestion framework lacked performance and reliability, and the company was spending too much time debugging data pipelines. The company's Head of Data, Olivier Girardot, was tasked with enabling a small analytics team to access insights and deliver maximum value to the business with fewer resources. The company needed a consistent, automated approach that could be run by two engineers. One of the main objectives was to analyze data from digital advertising to maximize advertising spend. Another challenge was regulatory compliance. Malt needed a solution that protected the personal information of freelancers when their data was moved to the data warehouse, ensuring it was kept within the European Union.

Memrise, a language learning app used by over 50 million people worldwide, faced a significant challenge in identifying gaps in customer engagement. Despite having a robust cloud-based platform and a commitment to data analytics, the small data team was overwhelmed with coding, manually building data pipelines, and fixing broken APIs. As the customer base grew, the need for a more focused approach to analytics became increasingly critical. The team needed a solution that would allow them to spend less time on technical issues and more time on analyzing data to improve the user experience and drive business growth.

Mighty Digital, a growth, analytics, and strategy consulting firm based in Ukraine, was faced with the challenge of helping a transportation startup optimize its ad budget efficiency, user activation rates, and campaign engagement. The startup had no clear understanding of the cost-effectiveness of various ad campaigns due to an inefficient ETL pipeline built using Airflow and Python transformations. The data architecture was prone to errors, missing data points, and overall inefficiency, leading to inaccurate advertising results. The existing solution was convoluted, involving multiple softwares stitched together to create a complex architecture that provided no insights. This led to a lack of data insight, reliability, and availability, inefficient marketing campaigns and ad spend, inability to turn data into customer retention optimizations, and decreased trust in data due to errors.

MyCamper, a Swiss start-up likened to the Airbnb of campervans, faced a significant challenge in managing and analyzing the data collected on its web platform. The company recognized the importance of this data, but initial attempts at analytics were laborious and time-consuming, involving manual extraction of data from Excel spreadsheets. Using Google Analytics proved to be easier but was limited in scope. As an early-stage start-up, MyCamper had more pressing priorities and lacked the in-house skills to manually build out data pipelines. This resulted in a gap in data analytics that needed to be filled. The company also struggled with historicizing data in a way that could be retrieved for analysis. They were unable to track specific data sets, compare historical data to present, or even have a comparable baseline.

Nando’s, a popular fast-food chain known for its flame-grilled peri-peri style chicken, was facing a significant challenge with its existing infrastructure. The company, which operates over 1,200 outlets in 30 countries, was struggling to meet the demands of its data-driven marketing strategies, particularly around customer loyalty and rewards programs. The existing infrastructure was slow and inflexible, making it difficult for the data team to effectively manage data pipelines and make informed business decisions. The team, led by Miquel Puig, Technical Lead on the Engineering team, was manually working on data pipelines and making business decisions based on data at the ingestion stage. One of the key use cases was turning end-of-day data from the outlets into insights that informed loyalty and reward programs.

Nauto, a company that delivers predictive AI technology to make roads safer, was facing a significant challenge in managing its complex workflow. The company had to deal with multiple systems and stakeholders throughout the sales process, which often led to difficulties in finding a single source of truth. Nauto relied on fragile point-to-point integrations for taking new orders, processing payments, shipping hardware to customers, and managing customer subscriptions to its cloud data processing services. Any broken integration could leave its business users unable to serve customers for days. Moreover, different business systems rarely shared the same version of the truth. This situation led Nauto to seek a way to establish a single data repository that it could manage in-house using flexible modern tools.

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.

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.

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 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.

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.

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.

ACH Colombia, a financial services company, was established in 1997 with the aim of providing solutions to the financial industry and promoting financial inclusion among users. Over the years, the company has delivered several services, but it identified a new need among Colombians: the ability to send, receive, and request money easily and immediately, without having to leave their homes. This need became even more pressing in the context of social distancing and preventive confinement measures against the spread of COVID-19. Furthermore, ACH Colombia faced the challenge of delivering a 24/7 service across all regions of the country, and needed to ensure that the service was accessible to unbanked users. The company also wanted to promote the use of e-money to reduce costs, decrease the use of cash, and increase the security of transactions.

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.

Acordo Certo, a Brazilian financial services company, was facing significant challenges with its existing cloud services provider. The company's mission is to simplify debt tracking, negotiation, payment, and notification processes for defaulting consumers. However, the company's platform was experiencing performance issues, particularly with large-sized queries. The existing solution was costly, inefficient, and unable to deliver the desired results. The company needed a robust solution that could handle the vast amount of data generated by its nearly one million new subscriptions per month and dozens of billions of registrations. The company also needed to process this data to learn about consumer profiles and maintain customized, assertive communications with each of them.

ADEO, the third largest player in the global home improvement market, faced a significant challenge in the face of increasing competition from online retailers. In 2017, ADEO's CEO launched a global strategy for the digital transformation of the company, aiming to develop online activity and provide new customer solutions through the creation of digital applications. However, with its on-premises data center, it was taking up to four days to provision a complete environment to build a new application. This delay was hindering the company's ability to quickly respond to market changes and customer needs. The IT team sought a solution that would allow them to provision resources faster and enable developers to work more independently.

ADEO, a global leader in home improvement and DIY, was facing a significant challenge with its 15-year-old on-premises data warehouse. The company was struggling to manage the vast amount of data it was collecting from its customers' journeys on its e-commerce websites or marketplaces. The legacy infrastructure was unable to keep pace with the data influx, which threatened to impact customer retention. ADEO recognized the need for a data-driven strategy to align with its digital transformation and improve decision-making processes. However, the transition to a cloud-based system was a daunting task, given the age and complexity of the existing on-premises system. ADEO also wanted to adopt a platform strategy to facilitate partnerships and collaborations, but the legacy infrastructure was a hindrance.