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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.