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Superior Energy Services, a Houston-based company serving the drilling, completion, and production-related needs of oil and gas companies worldwide, faced a significant challenge in improving its working capital by reducing days sales outstanding (DSO). The company had grown by acquiring hundreds of smaller firms, each operating autonomously with their own unique ERP systems. This resulted in a decentralized IT infrastructure with over 100 different product and service lines. The lack of a unified system led to inefficiencies, particularly in invoicing. Individual units within Superior were invoicing the same customers independently, which was both inefficient and time-consuming. This fragmented approach also increased the overall number of days it took for Superior to collect payment for its services, negatively impacting the DSO key performance indicator (KPI). The challenge was to streamline this process without imposing a centralized system or specific mandates on the individual units.

TAIFUN Software, a Hannover-based company that has been developing commercial software for over 30 years, faced a significant challenge. The company needed a high-performance database with a small footprint for its ERP system, specifically designed for firms operating in the German and Austrian building trades. The database needed to be user-friendly and require minimal management, as most of the companies TAIFUN was targeting did not have access to an IT team or a skilled database manager. Many databases that were capable of providing a touch-free experience typically sacrificed performance for ease of use. Conversely, those that could provide the high performance TAIFUN wanted often came at the cost of ease of use, requiring ongoing management for optimal performance.

The Office of the Revenue Commissioners in the Republic of Ireland (Revenue) faced a significant challenge in improving the performance of their mission-critical database systems without compromising application availability or service delivery. The agency needed a highly reliable and available database platform to support uninterrupted operation of its mission-critical computer systems. The Revenue had to ensure that it could efficiently manage tax revenues, enforce import and export controls, administer various European Union schemes, and provide collection services for several other Irish government departments. The challenge was to maintain a system that could handle the influx of information and transactions as a large percentage of Revenue’s customer base moved to mandatory e-filing and e-paying.

Tsubakimoto Chain Co., a global machinery manufacturer based in Japan, was faced with the challenge of creating a registration and identification database capable of tracking and sorting up to 10,000 items per hour on their high-speed conveyor belt sorting systems. The company needed a solution that could meet their millisecond-level response times for database reads and writes. The challenge was not only to find a database that could handle the high volume of items but also to ensure that it could maintain its speed and efficiency over time. The company also needed a solution that could seamlessly integrate with their existing systems and processes.

Unit4 C-Logic, a Bruges-based company, was in search of a high-performance accounting solution that was easy to manage and designed around Belgian accounting rules. The company wanted an embeddable, performance-oriented database that could operate optimally without ongoing administration. The challenge was to find a database that could deliver high performance, extensibility, and scalability without requiring the active involvement of a database administrator (DBA). This was crucial as their target customers were small-to-mid-sized accounting firms, which typically do not have sophisticated (and costly) database administration skills. The company needed a database that could reliably deliver a high-performance experience without the ongoing attention or cost of a DBA.

The University of the West of England (UWE) was faced with the challenge of upgrading its student record system and underlying Ingres from Ingres 10 to Actian X, and migrating the entire system to the CentOS 8 implementation of Linux. This was a daunting task as the student record system was the only mission-critical application still running on the university’s aging Solaris UNIX systems. The challenge was further compounded by the global pandemic, which made it impossible for the teams to meet in person to plan or execute this critical project. The goal was to upgrade the database from Ingres 10.2 to Actian X 11.1 and to migrate the entire student record system from Solaris UNIX to the Linux platform, without interrupting access to the system.

Unruly Media, a London-based advertising technology firm, was faced with the challenge of delivering actionable, real-time insights to its clients to help them make decisions about expanding the social footprint of their critical video campaigns. The company needed to provide insights on where the video is running, who is viewing it, who has tagged it, and who is sharing it. The challenge was to provide these insights in real time, not days or weeks later. Unruly Media needed to tame the unruly stream of real-time data and enable clients to make use of it.

Deck, a trail-blazing company, provides predictive tools to help political candidates identify the right people for their advertising and direct contact campaigns. However, moving away from traditional methods of political campaigning, such as telephone surveys, posed a significant challenge. The company needed to ensure that their predictive models, which are based on machine learning, are updated with the most comprehensive and up-to-date data. One of the most critical sources of data for Deck is news. Therefore, the company needed constant access to high quality, timely, and relevant news data at both national and local levels. The challenge was to find a reliable source that could provide such extensive coverage of news data.

Okapi, a leader in innovative risk assessment for the commercial real estate (CRE) industry, faced a challenge in accessing real-time, relevant news content from a multitude of diverse data sources. These sources, which included HR platforms, labor statistics, location-based data, market-specific data, and financial reporting information, were crucial to Okapi's service but were often difficult to access and irregularly updated. Okapi's machine learning engine relied on this data to create accurate risk analysis reports for their CRE clients, providing insights for portfolio management, tenant risk, and asset acquisition. To enhance the effectiveness of their data insight tools, Okapi sought to incorporate real-time news data into their risk-prediction algorithm, which they believed would provide up-to-date data and a 'peek into the future'.

The Panama Leaks, the largest leak of its kind, revealed how the world's rich and famous were moving and hiding money across the globe. The leak consisted of over 11 million documents showing how money was laundered through offshore accounts and entities. The documents implicated a wide range of individuals, from world leaders to soccer stars. However, the sheer volume of data presented a significant challenge. The documents, dating back to the 70s, included emails, contracts, transcripts, photos, and even passports. The challenge was to mine this massive data set for interesting data points such as people mentioned, organizations, locations, and topics discussed.

Revolut, a fintech company, identified a need to enhance their customers' investment experience by providing them with real-time access to news stories and events that could impact their investment portfolios. The challenge was that news is crucial to trading as it provides signals that influence investment decisions. However, news moves quickly and investors who find out important news first have a considerable advantage over those slower to the source. The scale of global daily news makes it extremely difficult to identify high-impact news amongst the overwhelming noise. Revolut wanted to find a way to deliver relevant high impact news to their customers about any specific trading company through the app in real time, based on their customers’ portfolio preferences. This would not only provide a great user experience for investors, but also keep them engaged within the app, increasing the chances of them trading through Revolut.

Ryanair, one of the largest airlines in Europe, faced a significant PR crisis when they had to abruptly cancel around 1,900 flights due to an internal administrative issue. This situation presented one of the biggest PR challenges the company had ever faced. The challenge was to manage the negative press and social media backlash effectively. The company needed to understand the extent of the negative coverage, the effectiveness of their PR strategies, and the impact of the crisis on their brand image. To answer these questions, they needed to analyze nearly 600 news stories about the cancellations and 30,000 Tweets mentioning Ryanair over the week following the announcement.

The WannaCry malware attack was one of the most significant worldwide cyber attacks in history. The attack began on May 12th and within a few days, it had infected over 213,000 machines in 70 countries, paralyzing computer systems in hospitals, factories, and transport networks as well as personal devices. The ransomware virus encrypted all data on the infected computers, with users only able to decrypt their data after paying a ransom to the hackers. The attack was enabled by tools that exploit security vulnerabilities in Windows called DoublePulsar and EternalBlue. These tools were originally discovered by the National Security Agency (NSA) in the US, but were leaked by a hacker group called The Shadow Brokers in early April 2017. The challenge was to understand the media coverage of WannaCry before the news of the attack broke and afterwards, as details of the attack began to surface.

BukuKas, a financial management app, was founded in December 2019 with the aim of empowering small business owners in Indonesia to digitize their operations, run their businesses more effectively, and increase their degree of financial inclusion. However, the onset of the COVID-19 pandemic posed a significant challenge to the company. Many of their merchants were severely hit by the pandemic, with some experiencing a revenue drop of over 60% or even shutting down. The challenge was to leverage this situation as an opportunity for growth and to help their merchants manage their expenses and cash flows more effectively during these difficult times.

The challenge was to re-engage existing customers and accelerate app adoption and usage for EazyDiner, India's largest restaurant and table booking app. The company wanted to leverage the power of referrals to bring in valuable, engaged users with minimal financial investment. The success of the referral program hinged on the ability to seamlessly link one platform to another, allowing users to easily share their referral link via various platforms like social media, email, or text. Furthermore, the referred user needed to be directed to the most appropriate destination, either directly to the app if it was already installed on their device or to the relevant app store for download if not.

GoMechanic, India's first technology-enabled automobile service network, faced a significant challenge in maintaining a high lifetime value (LTV) to customer acquisition cost (CAC) ratio. The company operates in a market where customers have multiple options to cross-check service quality and pricing, leading to a complex purchase flow and high drop-offs. This resulted in significant cart abandonment that needed to be tracked and fixed. Additionally, the nature of the automobile service industry meant that customers planned their car service far in advance, leading to a long duration between the first and second service. This made it difficult to justify the CAC for users making only a single purchase.

HotelTonight, a major player in the hotel-booking space, was facing challenges in their mobile marketing strategies. The company was struggling with Google's app install ad format, which provided a limited and unspecialized user onboarding experience. This was particularly problematic for a mobile-only company like HotelTonight, operating in the highly competitive hospitality vertical. The challenge was to find a way to customize their Search Engine Marketing (SEM) campaigns and improve the user experience based on whether or not the users had previously installed the HotelTonight app. The goal was to direct users either directly into the app or to the App Store if needed, and then take them directly to the content within the app that was originally advertised to them, thereby increasing conversion rates.

ShopBack, a leading rewards and discovery platform in the Asia-Pacific region, was looking to enhance its growth strategy through a sophisticated referral program. The challenge was to design a user interface and experience that would make the referral process hassle-free and encourage users to refer more people. The referral program needed to be designed in a way that both the referrer and the referred user would receive extra cash each time a successful referral instance occurred. The company also needed a system to track the success of the referral program and reward the referrer when the referred user made a purchase with the stipulated minimum spend.

Airlines Reporting Corporation (ARC), a key intermediary in the travel industry, had accumulated a massive amount of data over its 50 years of operation. This data was stored in on-premises servers and was largely siloed by the company’s seven business domains. The company wanted to bring new products to market faster by making data more accessible across the business. However, ARC's existing product development processes relied heavily on the tribal knowledge of domain experts, which created bottlenecks and risked loss of expertise when employees left the company. In 2018, ARC decided to embark on a digital transformation initiative, which included modernizing business processes and migrating all their data to an AWS S3 Data Lake and Snowflake Data Cloud. The goal was to make data more accessible across domains and reduce reliance on individual expertise during product development. To ensure that employees could find the data they needed, ARC sought to implement a data catalog solution for their migrated cloud data.

Crocs, Inc., a global leader in innovative casual footwear, has been experiencing accelerated sales growth and setting high revenue targets. This growth led to an increased focus on data and analytics across all areas of the enterprise, including finance, digital, operations, supply chain, and more. However, managing disparate data sets across different systems became a challenge. To manage this data proliferation, Crocs invested in cloud-based infrastructure and technology, including a Snowflake data warehouse on the Microsoft Azure cloud platform and expanded their use of the Microsoft Power BI platform. Despite these advancements, Crocs recognized the need for better data governance to provide valuable insights and facilitate decision-making. An internal audit team identified several opportunities where data governance would provide value, leading to the creation of a data governance team.

Fifth Third Bank was facing a challenge in sharing and managing data across the organization. The bank needed a platform that could enable teams across the organization to use data to drive innovation and deliver superior customer engagements and outcomes. The lack of a centralized system for cataloging, tagging, governing, and sharing data was creating inefficiencies and slowing down the speed of operations. Furthermore, the bank needed to improve the security of its data management. The challenge was to find a solution that could provide a single source for data that could be easily accessed by data scientists, analysts, and engineers.

FLSmidth, a multinational engineering company based in Denmark, faced significant challenges in managing its data assets due to its size, history, and the complexity of its data environment. The company, with nearly 12,000 employees worldwide, has been growing for over 130 years, with numerous acquisitions adding to its data complexity. Each acquisition brought in new systems and data assets, which were not readily available to everyone who might need them. Additionally, valuable tribal knowledge often got lost when employees left the company. These complexities posed significant roadblocks to achieving the company's top-line goals of creating a data-driven enterprise and turning data assets into revenue-generating resources.

MercadoLibre, the largest online commerce and payment ecosystem in Latin America, faced the challenge of democratizing information across the company. The company wanted to provide its employees with easy access to data and enable them to run queries independently, without the need for constant IT involvement. However, this posed a risk of losing data governance, as analysts were given complete independence to use tools like Tableau. The company needed a solution that would allow controlled freedom, ensuring that employees could access the right information and data, while maintaining data governance.

Munich Re, a global reinsurance company, was facing challenges in managing and utilizing its vast data resources. The company deals with complex risks, including natural catastrophes, satellite launches, and large building projects, which generate a significant amount of data. However, the company was struggling with data collaboration and transparency across its various departments and global locations. Different teams were working on similar data analytics projects without knowledge of each other's work. This lack of coordination and collaboration was hindering the company's ability to innovate and develop new products based on shared insights. Furthermore, the company was grappling with the challenge of efficiently managing its data lake, which contained thousands of data sources and tables. The difficulty lay in finding specific information and extracting it from the data lake.

Parexel, a global provider of biopharmaceutical services, faced significant challenges in leveraging big data to transform their operations. The company had been approaching the issue as a technology problem, underestimating the complexity of the clinical development process and the importance of domain expertise. The business was focused on understanding the value and impact of treatments in the real world, which required the use of real-world data and healthcare data. However, the company's data governance was fragmented, and they had to build a capability in real-world data that was adjacent to their own business data. Additionally, the company was funding initiatives to advance their business operations and analytics capabilities, but the gap between their corporate IT function and the business was wide, with IT drifting away from the business.

Riot Games, the company behind the globally popular game League of Legends, was struggling to manage and derive value from the vast amounts of data generated by its over 150 million registered users. The company had a growing Databricks lakehouse on their AWS S3 cloud storage service, but finding valuable information within this data lake was proving to be a challenge. The analysts at Riot Games needed the right data to derive insights using their Tableau business intelligence platform, but the engineering teams were constantly bogged down with questions about the data. The engineers were repeatedly answering the same questions, which was taking up valuable time that could have been spent on more meaningful projects. The company needed a solution that would allow analysts to self-serve and self-educate, reducing the burden on the engineering teams.

T-Mobile, one of the largest wireless carriers in the US with over 116 million subscribers, was facing a significant challenge in managing and securing its vast data landscape. The company's growth, including its 2020 merger with Sprint, meant it was handling more customer data than ever before within an increasingly complex infrastructure. This complexity made it a target for cyber attacks, as evidenced by a data breach in 2021 that directly impacted its share price. This incident catalyzed T-Mobile's efforts to redefine its data management at scale. The company sought an enterprise-wide data transformation to secure its data, maintain regulatory compliance, save time and reduce costs, and accurately predict customer behavior and intention. The challenge was to scan an estimated 5,000 apps and 22,000 databases continuously, with 8 petabytes of data.

Brainly, a rapidly growing organization with a distributed model, faced a significant challenge in data governance. With independent teams each owning their data silos, the discoverability of data became a major issue. The remote setting further complicated the situation as it was difficult for teams to find and access the data they needed. The requirements for addressing this challenge included having metadata of all data assets in one place, making data assets discoverable, enabling collaboration and trust, reducing dependencies between business, analysts, and engineers, and showing where the data comes from.

Chargebee, a leading technology solution for recurring revenue management, faced a significant challenge in early 2021 when its growth led to an increase in data requests. The company's Data Engineering team was responsible for processing these requests, both from internal colleagues and customers. However, the volume of requests was so high that internal data requests were often pushed to the back of the queue, leading to missed SLAs and dissatisfaction among stakeholders. The team attempted to address this issue by hiring new colleagues, but the transactional nature of their Data Engineering function made hiring difficult. They also tried to automate and standardize the process by creating dashboards and workflows, but these were often too bespoke to service with a single view of data. As a result, the data request volumes continued to grow, with the team receiving 350 requests per quarter, 80 of which were repetitive requests. Struggling to meet their SLAs and with growing escalations to subject matter experts, Chargebee needed to find a new way to meet their colleagues’ and customers’ expectations.

Delhivery, India’s leading fulfillment platform for digital commerce, handles a massive amount of data, over 1.2 TB per day, from its vast network of IoT devices. The company fulfills a million packages a day, 365 days a year, through its extensive network of automated sort centers, fulfillment centers, hubs, direct delivery centers, partner centers, vehicles, and team members. With nearly 60,000 data events and messages per second, data discovery and organization became a significant challenge. The data is organized and processed by hundreds of microservices, which means that ownership over the data is distributed across different teams. As the company grew, the scale and complexity of its data grew even faster. Teams started building their own microservices, motivated by a desire to make data-driven decisions. However, finding and understanding the data became a significant issue. The onboarding process for new team members grew from 1-2 months to 3-4 months due to the growing complexity of the data. By 2019, Delhivery realized it desperately needed a data cataloguing solution.