Fivetran

Schüttflix, a German logistics start-up, aimed to disrupt the traditional construction supply chain by digitizing the industry, which was primarily reliant on pen and paper processes. The company's mission was to enable data-driven decision-making by providing stakeholders with accurate and timely data. The challenge was to build a modern data stack that could tap into key data sources efficiently and reliably. Alexander Rupp, Head of Data and Business Intelligence at Schüttflix, was tasked with evaluating connectors that could meet these requirements.

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 business model, which involves offering customers a choice of over 2,000 loan products from more than 40 lenders, relies heavily on delivering the right experience to the right person at the right time on the right digital platform. This requires accurate insights into borrowers' needs and preferences, which in turn requires tapping into behavioral data on third-party engagement platforms such as Facebook, Google, and Bing. However, the data from these platforms were siloed and did not integrate easily. Even when the data could be brought into the same repository, the data structure was often inconsistent, necessitating data cleaning before it could be used. The responsibility of ensuring that stakeholders across the company could analyze and create actionable insights from this third-party data fell to Lendi’s Data Architect, Daniel Deng.

Paytronix, a customer engagement platform for restaurants and small businesses, was facing a significant challenge in managing and deriving insights from its data. The company was dealing with data from multiple sources, running on various databases, and in disparate formats. The data ingestion tool they were using was unreliable and missed many transactions, leading to a lack of trust in the underlying data. Additionally, the company was using a mix of Scala and PySpark jobs for data transformation, which was custom code and handwritten. This toolset was unable to keep up with the growing demands of the business, and a lot of time was spent on maintenance and break-fix support. The company wanted to focus more on experimentation, but the existing system was not conducive to quick proof-of-concept testing and rapid iteration.

Billie.io, a Berlin-based fintech startup, is revolutionizing the way businesses handle payments by providing instant financing for invoices and outsourcing the collections process and default risk coverage. However, the company faced a significant challenge in managing its data architecture. The company needed a solution that could handle the Extract, Load, and Transformation (ELT) process of their production database to the data warehouse efficiently and cost-effectively. The company also needed to avoid latency problems or Service Level Agreement (SLA) issues and prevent transformations from occurring too early. Furthermore, the company wanted to have fine-grain control over when things happen and awareness on tasks that comprise pipelines, their dependencies, and their execution.

Canva, an online design and publishing tool, was under pressure to grow its customer base across three service levels—free, pro, and enterprise. The sales, marketing, and engagement teams needed to identify targets, understand their behavior, and deliver the right message at the right time on the right platform. This required a 360-degree view of the customer across Canva’s digital properties and third-party platforms such as Google, Facebook, and other social media and SaaS tools. The main challenge was the lack of comparison insight. There was no way to analyze Facebook data against Google data or any other platform. Building a point-to-point architecture to pull data into competing platforms would be messy and expensive to maintain. The custom-built solution functioned as intended, but the demand for connectors to more data sources was growing, and the time and resources required to build these connectors were not scalable.

Parachute Home, a U.S.-based direct-to-consumer brand selling home essentials, was struggling to manage data from its two core systems, Shopify and NetSuite’s cloud ERP software. Shopify powered Parachute’s ecommerce platform and transactional process, while NetSuite triggered the fulfillment process. However, these systems were running in a siloed manner, making the data from both sides increasingly hard to manage. Parachute was using custom-built data loaders to connect into Shopify and NetSuite, but the results were inconsistent. There were data quality problems that resyncing rarely solved, and the absence of logs made it hard to identify issues. The time-consuming data ingestion process was hindering the brand's digital ambitions.

Paul Hewitt, a jewellery and accessory brand, was facing challenges in managing and analyzing its advertising spend due to the limitations of its existing tool, Supermetrics. The analytics team, consisting of three employees, had to manually enter data into spreadsheets to determine the most effective marketing channels, a process that was both time-consuming and prone to errors. To meet the needs of an increasingly complex supply chain, the company had invested in an ERP system, Microsoft Dynamics NAV, and began to make data available for analysis with Microsoft Power BI. However, the company wanted to take its data strategy to the next level by integrating data from across the business into one place on a cloud data platform, with the objective of transforming into a data-driven business.

Pitney Bowes, a global technology company that simplifies e-commerce, shipping, and mailing, was facing significant challenges with its data management. The company lacked high-quality, real-time data necessary for critical business decisions. Its Enterprise Information Management (EIM) team was grappling with siloed data, lack of scalability, and inefficient tech spending. Employees were resorting to pasting data into Excel spreadsheets for executive reporting and analytics, which often exacerbated the issues. The company was also experiencing downstream problems, such as late-arriving packages that impacted Service Level Agreement (SLA) targets. They lacked the sophistication to detect delays and notify customers in time, causing reputational risk. The COVID pandemic magnified these data challenges when online shopping increased tenfold, leading to a tenfold increase in parcel volume. The company's legacy data infrastructure was unable to handle event- and email-based data operations for 800 million packages per day. The data captured was critical, but aggregating and consolidating it to the central analytics warehouse took days, making it outdated by the time it reached the leadership team.

PopSockets, a retail and consumer goods company, was facing significant challenges with its data management and reporting processes. The company was struggling with reporting efficiencies and communicating insights across various departments, including Ecommerce, Marketing, Business Intelligence, Finance, Accounting, Supply Chain, and Operations. The lack of strict timelines for data refreshment and the tedious process of manually aggregating reports were hampering the company's growth. As PopSockets began to experience tremendous year-over-year growth and adopted an ERP system, the volume of data grew exponentially. The company was grappling with data silos, unscalable manual efforts to aggregate and store data in a single source of truth, and a lack of visibility into marketing data to understand the ROI of ad spend on various channels. PopSockets needed a scalable solution that would allow its small team of data engineers to build automated data pipelines for faster analytics and reporting.

PostNL, a mail, parcel, and e-commerce service provider in the Netherlands, United Kingdom, Germany, and Italy, decided to migrate its IT operations to the cloud to maintain its competitive edge and reduce costs. The company aimed to decommission its on-premises data centers and move its applications, infrastructure, and IT management to the cloud. The applications ran on Oracle and SQL Server, and where possible, PostNL wanted to replace existing bespoke software with Software-as-a-Service (SaaS). If a suitable SaaS replacement was not available, the company planned to implement the legacy and bespoke software on top of cloud-based infrastructure and platform services (IAAS and PAAS). PostNL initially chose the Microsoft Azure platform for these services and later added Amazon Web Services to avoid the risks of running its entire infrastructure onto a solution from a single vendor. The migration process, which took over two years, presented significant integration challenges. PostNL needed to move applications and data to the cloud, ensure the migrated applications continued communicating with the existing on-premises systems, and integrate various cloud environments.

Princess Polly, an Australian fashion boutique, was facing challenges in utilizing data effectively during a time of uncertainty. The company was preparing for a critical launch into the U.S. market and needed to support internal departments in making informed decisions. Anand Bhatt, the Head of Business Analytics, was tasked with building an analytics infrastructure that could demonstrate value quickly and efficiently. As the sole member of his team, Anand needed to maximize his time generating value for the business and minimize manual, time-consuming tasks. A key area of focus was cash flow analysis, with the aim of understanding which decisions were impacting the business’ bottom line to make more effective decisions.

Red Ventures (RV), a global company with a focus on positively impacting people’s lives and communities, was facing a challenge in managing marketing data efficiently. The company's Red Digital division provides end-to-end performance marketing services to help business-to-consumer (B2C) services providers attract new customers. To deliver greater value to clients, RV needed to use timely insights from data to reach the right consumers. However, maintaining each client’s data in a separate cloud environment and integrating each client’s data for machine learning predictions was proving to be a tedious and time-consuming task. Data engineers had to write custom scripts to ingest data for each client, which was not an efficient use of their time and skills.

Redwood Logistics, a third-party logistics and transportation management firm, was struggling with managing a complex reporting structure that relied on multiple siloed warehouses. The rapidly growing business needed a modern data stack that could support its mergers and acquisitions strategy, providing leadership with an accurate overview of business performance in near real time. The company was generating 500,000 data points per hour, which was a significant challenge to manage and process. The old system could only load data once a day and was prone to numerous daily failures, becoming a massive maintenance burden. Redwood was initially cautious about using Fivetran’s high-volume data replication because the team needed to understand how it interacted with their existing databases.

Schüttflix, a German logistics start-up, aimed to digitize the construction industry, a sector traditionally reliant on pen and paper. The company sought to disrupt local supply chains by connecting suppliers, carriers, and buyers through a digital B2B platform. The challenge was to enable data-driven decision-making to speed up transactions and reduce costs. Alexander Rupp, Head of Data and Business Intelligence, was tasked with building a modern data stack. He needed to identify connectors that could tap into key data sources quickly and reliably. The goal was to provide stakeholders with the best data to make informed decisions.

Sleeping Duck, an Australian mattress company, was facing the challenge of managing and deriving actionable insights from data scattered across various sources. The data resided in Software as a Service (SaaS) platforms, web apps, marketing platforms such as Facebook and Google Ads, and in the company’s own product. The process of extracting relevant information from these disparate sources was complex and manual. The company's engineers would have had to write and maintain custom scripts to extract data, a practice that was neither scalable nor sustainable. The company needed a solution that could efficiently pull in data from these sources, manage it, and feed it into their business intelligence solutions for data-driven decision making.

Snowflake, a leading data cloud company, was looking to centralize its data within the organization's Snowflake instance, ‘Snowhouse,’ to power segmentation models, recommendation engines, and ultimately build a 360-degree view of customers. The marketing intelligence team at Snowflake had a bold vision to predict real-time ROI to dynamically optimize all Snowflake marketing programs, disrupting legacy B2B marketing analytics practices, and create huge efficiencies. However, the company faced challenges in breaking down data silos and enabling efficient analytics. Snowflake used to keep its data modeling and transformation logic within a separate BI tool, which was time-consuming and prone to error. Every time the business needed to run models out of the tool, or conduct ad-hoc analytics, analysts needed to recreate their models from scratch.

SpotOn, a rapidly growing software and payment company, faced significant challenges in efficiently transforming their captured customer transaction data into fast, reliable, and informative reporting for their clients. As the company scaled, the complexity of turning data into reporting for customers and internal stakeholders increased, with client data scattered across 30 unconnected MySQL databases. The engineering team lacked a central repository for efficient reporting generation. The existing data transformation process using stored procedures in Snowflake became increasingly complex and resource-intensive, with over 2,000 lines of code behind a single table. Changes were not automatically monitored or logged without version control, making quality assurance time-consuming and scaling required writing code from scratch for each new use case. This resulted in high costs, resource-intensive processes, and suboptimal results, impacting the company's ability to scale quickly to meet growing customer needs.

Super Dispatch, an online platform for auto transport, was facing challenges in onboarding new users and optimizing experiences for active users. The company's data was decentralized and scattered across various digital properties, business systems, and marketing tools. Employees were relying on spreadsheets shared around the company for different purposes such as marketing, billing, and sales. The data was downloaded from business systems or Software as a Service (SaaS) platforms individually and analyzed in Excel. This posed a significant challenge for Aman Malhotra, a veteran in the marketing, sales, and operations analysis industry, who was hired to improve user activation, retention, and monetization through the use of data.

Swapfiets, the world’s first ‘bicycle-as-a-service’ company, was facing a challenge in understanding behaviors in the emerging market. The company's growth strategy relied on identifying new cities, winning new subscribers cost-effectively, and establishing an efficient local support network. However, the company was struggling with data management. The data engineering team had built custom Python scripts to extract data into their central Redshift instance, which was manageable when pulling from just a couple of data sources. However, as the business started to expand, this approach proved impractical. Swapfiets needed a more streamlined approach to data ingestion to make sense of critical subscription and usage data. It was crucial for Swapfiets to understand its target demographic and how best to provide local support, carefully target its marketing, and avoid over-provisioning stock.

Testing for All, a UK-based non-profit organisation, was launched to provide mass Covid-19 testing at a low cost. The organisation aimed to deliver 5,000 high-quality Covid-19 tests a day at half the price of other services. However, they faced a significant challenge in managing personal data, medical test results, and biological samples while maintaining a prompt and user-friendly service at scale. The process involved a six-step procedure, starting with registration and dispatching a test kit, and ending with receiving lab results. The organisation needed a privacy-centric technology stack that could handle the complexity of the process and ensure speed and efficiency in both the eCommerce part (signing up and ordering the kits) and the science part (the labs providing a range of swabbing techniques).

Untitled is a company that is building a platform to help its clients leverage data across departments. The company's data products enable non-technical staff to derive key insights, leading to increased revenue, decreased operating costs, and the development of sophisticated AI and ML capabilities. However, the traditional process of building data pipelines, which is crucial for transferring data from one point to another, was proving to be a significant challenge. This process was time-consuming, accounting for as much as 44% of data engineers’ time, and was hindering the rapid development of their platform.

Vida Health, a digital health company, was facing challenges with its data infrastructure. The company collects data on customers' medical history, past insurance claims, lab test results, and log data from health-tech devices to provide personalized virtual care. However, their custom-built solution using Python scripts and cron jobs to load and transform data in BigQuery was not scalable and often failed when data volume spiked. The pipeline was poorly documented and understood by only a few people on the data team, leading to reporting downtime of 2-3 days when issues arose. The company had recently consolidated its data engineering, data science, and data analytics functions into one team, aiming to improve collaboration. However, the existing data infrastructure was not reliable or accessible enough to best serve their customers and meet their goal of onboarding more than ten new clients in less than six months.

Wallbox, an electric vehicle charging and energy management company, faced a significant challenge in managing its data. Since its inception in 2015, the company experienced rapid growth, expanding from 50 to over 1,000 employees in a short span of time. This growth led to an increase in the number of tools and applications used across different departments, resulting in data silos that hindered insight and quality control. The company's data was scattered across various platforms, making it difficult to trace and resolve quality issues. Additionally, the business logic embedded in the dashboard was complex to evolve. Another challenge was the regular updating of tools required for custom integrations, which proved to be a costly and time-consuming process. Wallbox needed a solution to break these silos and consolidate all its data in a single, easily accessible location.

Westwing, a leading European eCommerce company, was facing challenges with its outdated technology stack and inefficient data architecture. The company recognized the importance of integrating data in a centralized location, but the manual work on their on-premise architecture was becoming increasingly time-consuming. To integrate with each different data source, every line of code had to be programmed with Python. This was slowing down the company's growth and preventing it from achieving a holistic view of the business. Westwing decided to move its architecture to the cloud, with Snowflake as the data warehouse, and outsource commodity services to focus on its strategic goal of scaling an eCommerce platform. However, with an ambitious cloud migration deadline looming, Westwing needed to find an ELT solution that could quickly and efficiently automate access to data.

WeWork, a global provider of flexible office spaces, faced the challenge of securely managing and leveraging its vast data resources to drive business decisions. As a publicly-traded company, WeWork had to meet stringent regulatory compliance requirements, ensuring data governance across disparate sources and silos. The company needed to track data movement and user access over time, demonstrating to auditors and regulators that customer information was safe from internal and external threats. The challenge was not only to pull in data and provide access but also to maintain a historical record of data ingestion, changes to the database, and access. The company also aimed to create a culture of data literacy and innovation, empowering business agility through the use of data.

World Fuel Services (WFS), a Fortune 150 company, faced significant challenges in managing and utilizing its data effectively. The company had grown through numerous acquisitions, each with its own client lists and data sources, making it difficult to gain a comprehensive view of customers across the entire organization. Additionally, the company's existing ETL pipelines pulled data in batches once a day into an on-premise Oracle database, which quickly became too large to run live queries effectively. The company also faced the challenge of managing data from dozens of ERP and billing information services across its subsidiaries, which was particularly critical during the global pandemic when the company needed to increase accounts receivable efforts to maintain revenue.

Yardzen, an online landscape design firm, was facing significant challenges in managing its data pipelines. The company's Data Engineering Lead, Andrea Kyrala, was tasked with integrating data from numerous SaaS tools and product databases into BigQuery, as well as establishing a flexible and secure data architecture. However, building custom pipelines to BigQuery in-house was a time-consuming process, often requiring weeks of work digging through API documentation. Moreover, the ETL pipelines were brittle and frequently required intensive maintenance. Analysts and marketers were manually exporting individual reports from each marketing platform to understand ad and marketing performance, a process that was not only painstaking and time-consuming but also made it difficult for leadership to gain a unified view of advertising spend and performance across platforms. Often, Andrea didn’t have the time for complex transformation and cleanup that would ultimately save the business analyst time in the backend.

YipitData, a trusted source of insights using alternative data, faced a significant challenge as it began to grow rapidly. The company's analytics activities were running on dozens of Amazon Redshift clusters, with each team within the company maintaining its own clusters. This arrangement became cumbersome, especially when YipitData needed to share common data sets across teams. The company's product teams analyze billions of data points each day to provide granular insights that drive the successful decision-making of hundreds of investment funds and highly innovative corporations. However, the existing system was slowing down analytics and making it difficult for the company to stay ahead of its market.

BizCover, Australia’s largest online business insurance provider, was facing a significant challenge in connecting data from various sources. The company's team of engineers had to build unique connectors using their own code, each requiring 40 to 80 hours of engineering time. This approach initially worked when connecting and syncing data from their database and Google Analytics. However, as the number of data sources increased, the task became overwhelming. BizCover needed to pull data from over 20 data sources into its centralized Snowflake data warehouse, with each source requiring its own connector. The company’s data engineers were managing this largely manual process, and BizCover needed to disseminate the insights they were gaining from the data across the core business more efficiently.

Blend, a fintech startup, was facing a significant challenge with its data ingestion process. Despite having adopted a modern data stack approach with Redshift at its core, getting data into and out of the data warehouse was proving to be a complex and time-consuming task. The process of pulling a single column from Salesforce or changing a field could take weeks, limiting access to time-critical data. The team was unable to prototype and rapidly iterate, and had to release straight to production to test their solutions, causing further complications for the operations team. As the company expanded, new tools like Asana, Marketo, and Lever were introduced to manage workflows and processes, each requiring data to be synced inside them to be effective. With the data engineering team’s limited bandwidth, they did not have the capacity to maintain a rapidly expanding list of SaaS platforms. This led to a decision point: commit to in-house tooling, or look for external providers.

CarOnSale, a disruptive pan-European platform for car dealers, identified data as a key differentiator in their market. As an online platform, they aimed to cut through the complexity of traditional car trading by harvesting and analyzing data around car auctions. This would provide them with unique market intelligence. The company recognized the need for a centralized architecture, hosted in the cloud, to collect and analyze data at speed and scale. They explored different options to support ELT (Extract, Load and Transform) as opposed to the traditional ETL approach. After selecting Snowflake as their cloud-based data warehouse, they needed to find the ideal data integration solution. Aynaz Bagherynezhad, Data Team Lead, had used Fivetran in a previous role and when Snowflake recommended it as the best way to connect to data sources, it confirmed her own experience.

Code2College, a nonprofit organization aimed at helping minority and low-income students achieve tech/STEM careers, was facing challenges in managing and analyzing student data. The organization's data, including student attendance, grades, and teacher input, was kept in spreadsheets or gathered by word of mouth. This approach was inefficient and time-consuming, especially when specific data on a student's performance or an overall view of the student population was required. The organization used Salesforce for operations and Canvas as a learning management tool. However, extracting information from these platforms to answer a single question would require a day's work, which was untenable given the small size of the data team. The team wanted to centralize their data using Google's BigQuery data warehouse tool to streamline retrieval and expedite responses to student needs. However, the challenge was how to transfer data from platforms like Salesforce and Canvas into BigQuery.

Condé Nast, a global media leader with 37 brands reaching millions of consumers, was faced with the challenge of managing and monetizing trillions of data points generated from its digital assets. The company lacked a central mechanism for managing and maintaining data integration sources, making data not readily available to consumers downstream. The demand to integrate more sources globally continued to grow, and pulling data into the data lake with custom scripts was cost prohibitive. Each marketing technology platform had its own API, data structure and other properties that required its own custom script. Creating the connectors on the fly and managing them on an ongoing basis wasn’t scalable, posing a significant challenge to the company.

Coupa, a Business Spend Management (BSM) company, provides a cloud platform that digitizes and consolidates spending information across various sectors, creating actionable insights into spending behavior. However, Coupa faced challenges with its own data about its platform and customer usage. The data was siloed, impeding better insights and decision-making. The process of collecting this data and making it accessible to the relevant personnel was complex, costly, and resource-heavy. Coupa had invested in a data team to manage its data, with the goal of pulling data from various sources into a single place for creating actionable insights. However, the analytics strategy was immature and largely consisted of ad hoc procedures. If a UX designer wanted to know how customers were interacting with a particular feature, they’d have to request the engineering team to build a script from scratch, a process that could take weeks.

Chris Klaczynski, a Marketing Analytics Manager at Databricks, was tasked with supporting the primary marketing objectives of driving pipeline generation, growing the database, and improving ROI. However, as Databricks rapidly expanded, the need for centralized and documented data became more and more apparent. Data silos were appearing around the business, including on Chris’ marketing team, where data was stored in its own data warehouse. It was critical that Chris’ newly-built dashboards were supplied with trustworthy, timely data for marketing operations to keep running smoothly. However, without dedicated engineering resources, and in the face of a rapidly expanding marketing team, scaling with demand became next to impossible. Databricks faced a number of challenges with their traditional data warehouse, including issues with their Salesforce and Marketo pipelines, issues appending data natively to existing tables, and schema changes that were always breaking pipelines, resulting in outages and stale, untrustworthy data.

Daydream, an early-stage startup, provides financial insights to stakeholders across modern businesses. The company's business modeling and planning tool democratizes access to financial information by bringing together processes and data sources that are typically siloed. However, the Head of Engineering for Daydream, Shubham Sinha, faced a critical decision. The success of the startup hinged on its ability to move massive amounts of data from its customers’ cloud-based business systems, each with their own login credentials and access challenges, into the Daydream platform for analysis. The two options were to either ask its customers for login credentials to their business systems, posing a potential security risk, and use custom-built data pipelines to onboard data or to rely on Fivetran to broker the credential sharing exchange and onboard data using its pre-built data pipelines. Maintenance also posed a challenge as each cloud platform has its own APIs, processes, and data structures, many of them requiring custom integrations through scripting.

The Denver Broncos, a successful pro football team, faced a significant challenge in maintaining their data pipelines. The team's Senior Director of Ticket Strategy and Analytics, Clark Wray, and his lean team were spending excessive time on home-brewed data integrations. Whenever an original data source or API changed, it would disrupt the data connections they had built, often halting the flow of information for hours. If these issues were not addressed promptly, the business risked operating on inaccurate data. Additionally, the team was constantly adding new data sources to communicate and reach the next generation of fans. It was crucial for them to connect and centralize their email data in Dynamics 365, marketing automation data in Eloqua, and fan feedback in Qualtrics.

DOUGLAS, a leading premium beauty platform in Europe, was facing a significant challenge in its journey to become a 'Digital First' business. The company's existing infrastructure and processes, particularly around Business Intelligence (BI) and data analytics, were not up to the mark. The systems for collecting data were scattered, and there was an overreliance on spreadsheets and manual input, which were not scalable. This lack of a centralized, automated data collection and analysis system was hindering the company's growth and its ability to gain valuable insights from its data.

DPD Polska, a leader in the Polish courier market, was facing challenges with its existing data management system. The company was using a series of on-premises PostgreSQL and Microsoft SQL Server databases to track its trucks, parcels, and people. However, the array of custom SQL databases was preventing DPD from producing timely reports, meeting disaster recovery time objectives, testing new data and analytic products, scaling up its revenues, and increasing its customer base. For instance, one of DPD’s databases had three different usage contexts. The company was in need of a log-based replication solution that would not impact its source systems. The main pain points were the replication time lags, the risk of errors in manual data distribution, and the need for more flexibility, greater reliability, and higher operational scalability.

Engel & Völkers, a prestigious broker of premium residential property and commercial real estate, was facing a significant challenge in integrating various data sources. The company's data engineering team was inundated with requests from different departments for data integration. The process of creating custom solutions to respond to these requests was resource-intensive, leading to prioritization of tasks and inability to cater to the growing number of requests. The company was in dire need of a tool that could reduce the effort required to integrate new data sources and enable faster data integration, thereby promoting wider adoption of self-service analytics within the organization.

Frontify, a platform that helps companies grow their brands, faced a significant challenge in building a single source of truth for their data. The company needed to understand how people interacted with their platform to optimize user experience and resource allocation. However, their data analytics team was small, and their data infrastructure was unstable. They relied on custom Python scripts to pull data from business applications into a MySQL database, which often resulted in slow, incomplete data. Their BI tool was user-unfriendly and slow, causing reluctance among employees to use it. The data team was burdened with the task of updating reports and dashboards. To address these issues and become truly data-driven, Frontify needed a scalable and powerful data stack that could be accessible to everyone.

GroupM, a global media agency based in Oslo, was facing challenges in collecting and analyzing data for their clients. The agency, which serves over 200 clients and provides shared services for other agencies in the group, was using Supermetrics to pull marketing data directly into Google Sheets. However, this method was proving to be inefficient and problematic. Pipelines would occasionally fail due to hard-to-detect issues, and there were formatting problems with the spreadsheets as well as manual errors. Preparing data for analysis in Google BigQuery, GroupM’s data warehouse, was labor-intensive, and clients were demanding faster access to more insights. One client, with a broad business portfolio spanning retail and hotels, was looking for dashboards that could handle historical data analysis as well as day-to-day reports. GroupM was determined to find a more robust solution.

Hashtag You, a brand builder in the direct-to-consumer e-commerce sector, faced a significant challenge in leveraging and structuring data within its organization. As a data-driven company, the use of analytics in marketing, product and customer analytics, and operational analytics was crucial to its business model. Initially, Hashtag You implemented several piecemeal solutions through Google Sheets with self-created data pipelines. However, the company soon realized the need for a centralized and scalable approach to data ingestion. The challenge was not only to establish robust data pipelines but also to connect new data sources quickly and easily. The company needed a solution that would allow non-data specialists to make these connections. Furthermore, the company had to manage advertising across various platforms, combine marketing and webshop data, link with other data pipelines, and analyze campaign performance.

HOMER, an early learning company, was facing significant challenges with its data management. Despite the company's data-driven roots, the data and analytics architecture was considered to be at a foundational stage when Joe Nowicki joined as Vice President of Data and Insights in February 2021. The company's data team was spending a significant amount of time building ETL pipelines, a laborious and time-consuming process. Flattening and maintaining Stripe data was costing the team dozens of hours each month, preventing them from adding value to the wider organization. The legacy data practices had led to a feeling of distrust, with leaders unable to depend on dashboard views and unclear business intelligence. Access to timely data was critical but lacking. There had been multiple visions and revisions of the entire data infrastructure, leading to the selection of Databricks’ Delta Lake, to set HOMER up for future Machine Learning applications.

Houseware, a software development company with less than 20 employees, was facing significant challenges in providing a platform and toolkit for its customers to build internal data products. The company's goal was to go beyond the scope of general analytics and data visualization tools, delivering metrics such as ARR, NRR, customer churn, conversion rate, and other KPIs. However, they were struggling with a lack of data insight, reliability, and availability. Their marketing campaigns were inefficient, and they were unable to turn data into customer retention optimizations. The trust in data was decreasing due to errors. Users had to learn data analytics tools and database methods, such as table joins, and develop custom metrics from scratch. Data dashboards and analytics often broke down, took too long to produce results, or required too much custom programming. Poor APIs and data pipelines limited the types of analytics that developers could construct to meet customer needs. The tools produced insights without any actionable recommendations, and building data connectors required lots of programming time and effort.

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.