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.

A2A, an Italian utilities provider, faced the challenge of scaling its business to meet the needs of over 2.5 million customers while also striving to meet the United Nations’ 2030 sustainability goals. The company aimed to double its client base by 2030, in line with the liberalization of the Italian energy market. However, the increasing customer numbers, shifting expectations, and the uptake of new technology, such as smart electricity meters, made it difficult to predict infrastructure requirements. A2A's data warehouse would refresh every 24 hours, which was not sufficient for real-time customer service. The company needed a solution that could provide real-time data to respond to customer needs immediately, especially in critical situations such as power cuts.

Airwallex, a global payments platform, was facing challenges in expanding into new markets and complying with stringent regulations such as the Payment Card Industry Data Security Standard (PCI DSS). The company's existing cloud service was unable to support its growth plans and ensure compliance with these regulations. As a startup, Airwallex also needed to focus on the application layer of its offerings rather than dedicating time and resources to infrastructure. Furthermore, the company required high security, reliability, low latency, and manageable costs to build and retain trust with its customers. The company also needed to ensure low latency to provide data consistency and offer low rates to customers without applying a high spread to account for rate fluctuations due to the fast-moving nature of financial markets.

Anheuser-Busch InBev (AB InBev), a global corporation known for its popular beer brands like Budweiser, Corona, and Stella Artois, was facing challenges in optimizing its beer filtration process. The filtration process, which is crucial for achieving the best beer taste and meeting brand-required turbidity levels, involves many unpredictable variables. The existing technology could only handle basic logic, using meters to monitor and react to adverse conditions such as a change in pressure. AB InBev recognized the potential of machine learning (ML) and artificial intelligence (AI) in leveraging a larger dataset to better predict and prevent potential issues during filtration. However, the company needed a partnership and provider that could enable them to deploy ML quickly and effectively.

BBVA, a leading bank in Spain, was facing the challenge of digital transformation in the highly regulated banking industry. The bank was under pressure to compete with digital-native fintech companies while maintaining the trust of its 78.9 million customers. The banking industry, being an attractive target for cybercriminals, had traditionally kept their IT infrastructures on-premises to maintain control over data security and privacy. However, BBVA recognized the need to embrace cloud computing to gain agility and anticipate future banking needs. The bank also aimed to reduce its carbon footprint and become an environmentally sustainable business. The challenge was to navigate this transformation while maintaining stringent data security and privacy standards.

The process of diagnosing autism in children has been historically plagued by subjectivity and inconsistency. The existing assessments are lengthy, require in-clinic visits, and there is a growing shortage of specialists. Furthermore, disparities in diagnosis based on socioeconomic status, race, gender, and geography have been observed. The average age of diagnosis in the U.S. is 4.3 years, and parents and caregivers often spend up to 3 years seeking answers. Early diagnosis is crucial as it can greatly alter the course of development and may even erase the signs of autism altogether.

Devsisters, a global entertainment and gaming app developer, launched Cookie Run in 2013, which quickly became a success with 2.9 million daily active users in Korea and over 10 million downloads within 12 weeks of its launch. However, as the user base grew, Devsisters' on-premises platform began to experience performance issues and became complex to manage and costly to maintain. The company needed a new data analytics platform that data platform engineers and data scientists could use to analyze big data more quickly and seamlessly. The existing infrastructure was associated with high maintenance costs and frequent issues that required support from software engineers, leading to a diversion of resources from enhancements to troubleshooting. Devsisters attempted to solve this by building its own SQL-based data querying environment based on Spark Thrift Server, but the challenges persisted, including non-functional update and delete queries and slow performance.

Froedtert & the Medical College of Wisconsin (F&MCW), a regional health partnership, was facing challenges in managing and analyzing their vast data resources. The organization had a wealth of data from electronic medical records, financial spreadsheets, and HR data. However, the data was managed in silos using tools like SharePoint and Excel spreadsheets, leading to limited data sharing and collaboration between departments. There was also a lack of consensus on what discrete data metrics signified. Furthermore, there was a lack of data ownership, particularly among business and clinical leaders, who viewed data as a technical aspect and left its analysis to data governance and technical teams. The organization needed a solution that would enable better information analysis, foster data ownership, and improve data governance.

DXC Technology, a global technology company, was facing a significant challenge with its client's dependence on a legacy tool for continuous PCI compliance. The situation was further complicated by the need for manual checks and a lack of oversight into unauthorized changes in the IT environment. The existing implementation with a competitor was not meeting the client's needs, and the lack of transparency into what they were monitoring made achieving effective PCI DSS compliance difficult. This situation was consuming a significant amount of time from the IT teams, preventing them from focusing on more strategic initiatives. As a result, DXC Technology began to evaluate other solutions that could streamline and simplify the PCI compliance process.

ING-DiBa AG, a leading digital and universal bank in Germany, was grappling with the challenge of managing a large number of legacy data systems. These systems were creating inefficiencies in serving customers in the digital age, especially as more customers began interacting with banks through online platforms and mobile apps. A seamless cross-channel experience was crucial for customer engagement and satisfaction. However, the existing data infrastructure was not equipped to efficiently integrate and deliver all the data relevant to customers and business users through the right channels. This was hindering the bank's ability to quickly deliver on business requirements and was increasing manual and overall development efforts.

Lotus’s, a popular retail brand with over 2,500 outlets in Thailand and Malaysia, aimed to double its sales in two years by improving customer loyalty and adopting an omnichannel strategy. However, the company's rapid growth led to the accumulation of multiple disparate systems and data sources across new acquisitions, partners, and supply chain operators. This made it difficult for Lotus’s to access the necessary data to gain visibility into its business. The existing IT infrastructure and point-to-point custom code integration were labor-intensive and costly to connect existing systems, add new systems, and access data. Lotus’s needed a more flexible systems integration solution to connect front and back-end systems at an accelerated pace to support its sales goals and business growth.

Vecs Gardenia, a renowned skincare brand in Taiwan, was facing the challenge of maximizing onsite conversions and effectively identifying hesitant shoppers in an increasingly competitive skincare market. The brand wanted to deliver coupons with precision to increase revenue and conversion. The traditional methods of identifying hesitant shoppers were manual and prone to errors. The brand was also looking for ways to understand the behavior of hesitant shoppers to tailor its marketing measures based on different onsite behaviors.

Since 1992, LoyaltyOne has operated the AIR MILES Reward Program, generating $70 billion in revenue across their network. Despite having billions of data records from collectors and retail partners, AIR MILES struggled to foster stronger touch points with its customers due to a lagging legacy infrastructure. This outdated system prevented the company from gaining a holistic view of their customers, thereby hindering their ability to improve retention and customer lifetime value. AIR MILES wanted to leverage the petabytes of customer data generated by 558 million transactions per year to enhance the collector experience and enable their partners to better engage and drive revenue. However, their existing technology was unable to support this vision. The company's data scientists couldn't access data older than 5 years, and complicated queries limited what they could do with the data they did have access to. Pipeline development alone took 3 to 4 months, stifling their ability to deliver innovative solutions to market in a timely manner.

Akamai, a global content delivery network (CDN) provider, manages approximately 30% of the internet’s traffic through its 345,000 servers spread across more than 135 countries. In 2018, Akamai launched a web security analytics tool to provide its customers with a unified interface for assessing a wide range of streaming security events and perform real-time analysis. This tool ingests approximately 10GB of data related to security events per second, with data volumes increasing significantly during peak retail periods. The tool initially relied on an on-premises architecture running Apache Spark™ on Hadoop. However, Akamai faced challenges in meeting its strict service level agreements (SLAs) of 5 to 7 minutes from when an attack occurs until it is displayed in the tool. The company sought to improve ingestion and query speed to meet these SLAs and provide real-time data to its customers.

Aktify, a company specializing in conversational AI, aims to help its clients convert their customers through AI-driven SMS conversations. The company's goal is to make these AI agents as effective as possible, which requires drilling into massive volumes of data to find overlooked insights. However, Aktify faced challenges in managing its complex data dependencies, which hindered data democratization. The company needed a way to allow different teams to interact with data as per their needs. For instance, while a data scientist might need raw data, an executive team would prefer pre-aggregated data for quick decision-making. The company also sought to minimize the risk associated with data transformations and to make data management more simple, flexible, and cost-effective.

Redstone Federal Credit Union, a financial institution with 1,001-5,000 employees, was facing a significant challenge in leveraging data to gain more visibility and control over banking operations. The credit union aimed to deliver more value to its members through data-driven insights. However, fragmented data spread across a myriad of systems and applications was a major obstacle. This fragmentation was holding back the credit union's Business Intelligence (BI) and data science initiatives, preventing them from making the progress they desired. The data science team was heavily reliant on the IT team, which was slowing down their ability to innovate with data.

SeaLink, a company with 24 brands in its portfolio, was struggling with slow and inefficient data analysis and reporting due to outdated tools and a lack of support. The company relied heavily on accurate analysis and reporting to set prices for its various products. However, with numerous products and data sources, the process was slow, with one monthly report taking over 100 hours to compile. The company was using a legacy Business Intelligence (BI) system that was nearing its end of life and showing signs of failure. With no support available, the company was exposed to significant risk. Users found the tool difficult to use, making them dependent on IT for tasks. Analysts also lacked control over its configuration, making it even more challenging to implement logic.

One of the world's largest and most loved convenience stores had a strategic goal to grow from 10,000 stores in North America to over 20,000 by 2027. To achieve this aggressive expansion while increasing store operations satisfaction, they needed a complete reimagining of how they supported their franchisees. The challenge was amplified when they purchased 1,000 new stores in 2018 and needed a way to quickly onboard them. They required a single platform that could manage all types of service requests, allowing for greater efficiency and automation. They wanted to combine 15 distinct helpdesks, track 3rd party vendor quality and speed of service, streamline store reporting process for facilities and IT, and improve field services through work order and store asset analysis. Over time, they had lost visibility into their vendor management program, had no insight into vendor activity or performance, and were paying expensive vendor invoices without the ability to cross-check against services performed. They were also facing issues with their support centers being on different unconnected platforms, resulting in ticket re-routing costs of over $3 million a year.

EBSCO Information Services, a leading provider of digital research material, faced a significant challenge as its marketing department expanded. The company's digital assets were scattered across various platforms including Microsoft Sharepoint, network drives, local hard drives, email attachments, desktops, and Google Drive. This disorganized digital landscape hindered EBSCO's scaling, leading to outdated branding, lack of cohesive metadata, and inefficiencies when creating duplicates of lost assets and posting one asset in multiple systems. The lack of a standardized asset metadata and clear labeling or historic versioning recorded in their ecosystem often left EBSCO’s marketing and sales teams unable to identify the most recent approved versions of assets. The scattered assets also resulted in wasted time and money creating duplicates of lost assets and inefficiently posting and maintaining one asset in multiple systems.

Hickam Communities LLC (HC), owned and managed by Lendlease, at Joint Base Pearl Harbor-Hickam (JBPHH) Air Force Base in Hawaii, was facing the challenge of modernizing over 2,500 privatized military housing units with energy-efficient solutions. The project required a significant investment, which was to be generated without the need for congressional appropriations. The goal was to significantly increase energy efficiency, reduce carbon emissions, and enhance the comfort of military families residing in these units. The project was expected to generate $13 million in annual cost savings, which would be used to pay for the improvements, financing costs, and operations and maintenance services over a 25-year performance period.

Taylor Farms, North America’s largest producer of healthy fresh foods, was facing energy reliability challenges and escalating energy prices. The company was reliant on the regional power grid to power its 450,000 sq. ft. facility in San Juan Bautista, CA. The facility required a constant power supply to ensure uninterrupted production and delivery of fresh foods to customers. However, the regional power grid was strained and unreliable, often impacted by extreme weather events. This situation posed a significant risk to Taylor Farms' operations, potentially disrupting its commitment to providing fresh foods to customers. Furthermore, the company was keen on reducing its greenhouse gas emissions and establishing price and power predictability.

Engie Solutions' subsidiary, TEM, is tasked with ensuring the comfort and well-being of occupants in various buildings, including offices, schools, public buildings, and manufacturing plants, while keeping energy costs to a minimum. The quality of air and temperature can directly impact the behavior and productivity of occupants and employees. Poor air quality can lead to health issues such as fatigue, sleepiness, loss of concentration, and increased sick leave, thereby affecting business performance. Moreover, discomfort is often associated with excessive energy consumption. The recent federal law in Belgium has further complicated matters by regulating CO2 levels in enclosed spaces, requiring employers to ensure that the CO2 concentration in the workplace is less than 900 ppm.

Braserv Petróleo, a Colombian oil and gas support services company, experienced rapid growth from 2016 to 2018, expanding from four rigs to 15 across several high-production oil fields. With this expansion, the company faced the challenge of maintaining the reliability of their critical assets, which included their workover and well servicing unit, drawworks, hydraulic winch, traveling block, mud pumps, BOPs, and choke manifold. Any failure of these assets could result in high costs due to lost production time and emergency maintenance expenses, as well as potential risk of injury to personnel. About 20% of all Braserv assets were considered critical, necessitating a reliable solution to mitigate risk and avoid unplanned downtime. The company had previously attempted to use another maintenance management system, but it failed to meet their needs.

Silgan Plastic Food Containers (PFC), a global leader in high-barrier plastic packaging, faced several operational challenges. Despite being an industry leader, the company struggled with issues related to transparency and management of tasks. The documentation process was entirely paper-based, leading to a lack of clarity about who was responsible for what tasks and when they were completed. This lack of transparency affected operational results. Handing over responsibilities was also a challenge, especially when team members were absent. Missed tasks were difficult to address due to these issues. Additionally, Silgan was required to conduct one major audit and several smaller ones each year. However, the paper-based filing system made locating necessary documentation a time-consuming and difficult process.

Ameresco, a leading energy efficiency and renewable energy company, was looking to expand its renewable energy asset portfolio beyond North America. The company aimed to establish its first renewable generation asset outside of the United States and Canada. The challenge was to acquire, upgrade, and operate a wind power project in a new geographical location, County Kerry, Ireland. The project, Beale Hill Wind Farm, was to be developed exclusively by Ameresco without any form of subsidy from the Irish Government in the form of tariffs or consumer levies. The company also faced the challenge of selling the electricity generated at Beale Hill directly into the local utility network under a power purchase agreement.

MarketAxess, a fintech company with a digital trading platform used by over 1,800 financial institutions, needed to strengthen its ability to make data-driven decisions. The company's Credit and Market Risk team was responsible for providing accurate and fast reporting to support optimized decision-making across the business. However, they faced challenges with their existing ETL (Extract, Transform, Load) solutions, which were inadequate for quickly and easily consolidating data from a plethora of different sources for analysis. The team needed a solution that could simplify data consolidation and analysis, thereby reducing business risk and improving the efficiency and quality of critical business reporting.

Meat & Livestock Australia (MLA), a global leader in the production and export of livestock, was faced with the challenge of improving product quality, productivity, and global market share. They aimed to drive the adoption of best practices across the industry and improve data collection for enhanced decision making. The business challenge was integrating data across the value chain to drive decision making. This was necessary not only from a supply and demand perspective but also a quality perspective so that farmers could continuously improve and provide the right product at the right time to the right markets. Given that MLA supports 50,000 farms on over 5.2 million acres, they needed collaboration from solution and cloud partners with a deep understanding of the challenges within the meat and livestock industry and the expertise to develop a new internet of things (IoT) digital strategy for collecting data and making data-driven decisions across the value chain.

Migdal, a leading Israeli finance group, was facing the challenge of supporting thousands of users accessing their most critical business environment while providing microsecond-level response times. With over 2.4 million private and corporate customers, Migdal offers insurance, pensions, savings funds, and investments. The company was experiencing pressure on its storage and backup environment due to increasing online touchpoints and data volumes. The risk of downtime and the need for a first-class user experience led Migdal to seek improvements in infrastructure performance, scalability, and manageability. The company aimed to find a solution that would minimize application response times and ensure rapid recovery in case of a disaster.

A multinational telecommunications company, operating in 26 countries predominantly in Europe, Africa, and the Asia-Pacific region, was seeking to improve the speed and quality of its customer service interactions. The company provides IT and other solutions to corporate clients globally and wanted to enhance its interactions with its vast subscriber base. Traditionally, customer support has been a labor and cost-intensive service with an impersonal, mechanized interactive voice response (IVR). The company recognized that a more efficient and personalized customer service approach would give it a competitive edge.