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Zalora, a leading online fashion retailer in Asia Pacific, was facing a significant challenge in its customer communication and marketing efforts. With the rise of mobile eCommerce, Zalora recognized the need for an effective SMS solution to implement marketing campaigns and provide reliable transaction, order, and delivery updates to its customers. However, the company's initial solution of using local SMS aggregators proved to be ineffective. Thousands of Zalora's SMS messages were not being delivered, leading to a negative disruption in the mobile shopping experience of its customers. This issue was not only affecting Zalora's customer satisfaction but also its marketing and transaction efforts.

Zenly, a location-sharing mobile app, was facing a critical business issue due to the inability to reach users effectively. The company had implemented several SMS vendors to complement their home-grown user verification process, but many users never received a PIN code. This inability to verify their phone number to access the app prevented users from inviting their friends, thus hindering the growth of the Zenly user base. The situation was causing an increase in engineering resources, poor message delivery, lack of analytics, a friction-riddled user experience, and low conversion rates. Zenly needed a solution that could provide a seamless user verification and app installation process, while also ensuring compliance with regional regulations and facilitating an expedited user response.

Coractive High-Tech, a Quebec City-based designer and manufacturer of innovative optical fibers and fiber lasers, was facing a significant challenge. The company, with 65 employees and an office in China, was struggling to manage and store their data effectively. The software they were using did not communicate with each other, leading to inefficiencies and difficulties in accessing reliable information quickly. This was particularly problematic as Coractive needed to meet the demands of its OEM customers in Asia, the USA, and Canada profitably. The company was also aiming to continue its profitable growth in international markets, a goal that was becoming increasingly challenging due to intensifying competition and dropping product prices.

Answers.com, a popular website for sharing knowledge, faced a significant challenge in managing the vast volume of image uploads. The engineering team was tasked with storing these images, applying various graphic modifications, and delivering them to end-users in an optimal and quick manner. These tasks were complex and time-consuming. As the company expanded, the business needs grew, requiring a robust, scalable system for their image capabilities. The existing system needed re-engineering to support the scale, and the team was in search of a better solution to facilitate their work with images. They were looking for a partner to help expedite the process while meeting their evolving image requirements. The rapid growth of content and images added to the site necessitated a solid, scalable solution that was also cost-effective.

ClickMechanic, an online marketplace for automobile mechanics based in London, was facing challenges with managing and optimizing their media assets. As an online business, it was crucial for them to ensure that images loaded quickly on their website. They wanted to serve their images as quickly as possible through a content delivery network (CDN), but were apprehensive about the complexities of setup and integration. The company needed a solution that could not only meet their current needs but also scale with their growing business. They were also looking for a solution that could handle a large number of photos, some of which were several megabytes in size, and display them efficiently on their website.

Keep.com, an innovative online commerce startup, was facing a significant challenge in managing image transformations for their website and mobile app. The application displays hundreds of thousands of product and brand images, user profile pictures, and user uploaded images. Each time a redesign took place, Keep needed new image sizes, which required reprocessing of all the images. This process was not only time-consuming from a server perspective but also caused delays in the development process due to long waiting times as all the images were being transformed. Engineering resources were wasted trying to set up new image transformations and getting jobs running efficiently. Despite these efforts, image processing remained a bottleneck, hindering the development of their website and business.

MyCreativeShop, a company specializing in product customization for print designs, faced a significant challenge in providing their customers with realistic previews of their designs. Customers found it difficult to visualize what their designs would look like once printed, as the company was only able to provide flat, static images of the finished designs. The company explored various solutions, including Photoshop alternatives and running ImageMagick on their server, but these options were not scalable and involved complex task management.

Sendify, a logistics company based in Sweden, was in the process of building two new websites to implement new brand guidelines and improve site structures. The company wanted to incorporate images on both websites to enhance the user experience. However, managing these image assets posed a significant challenge. Images were stored in various locations, including local files on computers and different folders on Google Drive. This disorganized storage system led to developers spending up to ten minutes searching for a single image. Additionally, any transformation to the images, even minor ones, required the involvement of a graphic designer. With hundreds of images and multiple transformations for each, this process was time-consuming and hindered the daily progress on the websites.

Thrillophilia, India's largest online tours and activities platform, faced a significant challenge in managing and delivering millions of images. The platform supports over 1 million images, with 250,000+ unique images distributed across 200,000 pages for the 10,000 tours and activities offered on the site. However, many of these images, uploaded by Thrillophilia’s suppliers, lacked visual appeal as they were taken by cell phones or low-end cameras. Additionally, with more than 1,000 new images being uploaded to the site each day, Thrillophilia developers found themselves having to focus on image transformation and management, instead of their core duties.

InZone Industries, a company with a nationwide presence, was facing a significant challenge in maintaining brand consistency across its email communications. The company's employees, spread across the country, were using different email signatures, each with varying messages, logos, and branding. This lack of uniformity was affecting the company's brand identity and professionalism. The problem was further exacerbated by the fact that the company was split evenly between using Microsoft and Google, making the management of signatures even more challenging. The company's IT Manager, Thomas Erasmus, was in search of a solution that could standardize the email signatures across the organization, regardless of the platform being used.

The case study revolves around the challenge of managing identity and access in a banking environment with over 6,000 systems. The bank needed to ensure that the right people had access to the right systems, a task that was critical given the sensitive nature of the data involved. The bank was already using Oracle Identity Manager (OIM), but transforming data into a format that OIM could understand required a large development team and a cumbersome process of getting scripts into production. The bank needed a solution that was repeatable, configurable, and could be implemented quickly. Another challenge was the over-reliance on one team member for validating user permissions, which led to business risk and a lack of transparency.

Anchor Yeast, a leading manufacturer of yeast, enzyme and speciality ingredients in Southern Africa, faced a significant challenge when one of its top customers, Pick n Pay, decided to implement a new Electronic Data Interchange (EDI) order process. The new process required suppliers to accept EDI-based purchase orders, a change that necessitated a significant shift in Anchor Yeast's existing order processing system. The company's call centre, responsible for processing purchase orders and creating sales orders, needed a solution that could seamlessly integrate the new EDI order process into their existing operations. The challenge was further complicated by the need to minimize errors, address potential price discrepancies, and maintain efficient communication with customers.

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.