Trident Trust, a global leader in fund administration, faced significant challenges when it first ventured into the crypto space in 2017. The company had to collect its clients' crypto transactional data through mechanisms that were often cumbersome and unsustainable, such as manual input, files, and screenshots. Once the data was collected, Trident used spreadsheets to apply price and derive the trading activity value. This process was not only laborious but also required Trident to manually transform the data into a format that was easily consumable by traditional fund accounting systems. The lack of automation and the need for manual intervention made the process inefficient and prone to errors.

The case study revolves around a global shipping company, one of the largest in the world, with over 135 years of experience in the industry. The company manages around 600 vessels and employs 20,000 people on shore and at sea, through nine ship management centres worldwide. The company was aware of the potential damage cybercrime could inflict on its operations, finances, and reputation, especially after the NotPetya malware attack on Maersk in 2017. The company had some security controls in place, but these did not provide visibility across its complete hybrid cloud infrastructure, including Office 365. They also lacked confidence that attacks would be identified quickly enough to minimise potential damage and disruption. With a relatively small number of IT specialists for its size, the company sought a third party to help with day-to-day threat detection, enable a more proactive approach to cyber security, and mitigate future security risks. They also needed to ensure that appropriate controls and processes were in place to meet all its data protection obligations, including the ability to detect and report breaches in line with the GDPR.

Lumin Digital, a fintech company specializing in digital banking solutions, was seeking to build a modern, multi-tenant data platform to support advanced analytics, reporting, and enable various AI/ML use cases. The initial use cases identified were focused on improving abilities to monitor and detect risk involving user sessions and ACH transactions. The company's mission was to help financial organizations preserve and nurture their relationships with customers in today's evolving market. To meet their ambitious goals, Lumin needed a trusted partner to design and build the desired data platform, implement AI/ML use cases, and ensure that the platform was optimized for stable, cost-efficient performance.

TripActions, a corporate travel management organization, faced a significant challenge in enabling secure banking transactions without the need for third-party services. The company aimed to accept customer payments directly, track all banking transactions processed through the platform, and securely collect and store critical and client-sensitive data. To achieve these objectives and spur revenue growth by attracting new enterprise clients, TripActions needed to migrate its platform to a secure PCI-DSS-compliant infrastructure. However, the existing infrastructure had several network, user access, monitoring, alerting, and CI/CD issues that needed to be addressed. The company approached Provectus to upgrade their infrastructure as part of their preparation for PCI-DSS compliance certification.

BetterMe, a leading behavioral healthcare app publisher, faced a significant challenge during the pandemic. Despite being well-positioned to capitalize on the shift from gyms and group fitness classes to home workouts, the company found itself in catch-up mode. The behavioral changes of its users, such as the marked increase in time users were spending on the app, required rapid adaptation. The main challenges were the speed of experimenting and responding to user needs while maintaining a high satisfaction level. The company needed to balance velocity and satisfaction levels, which was a complex task. Additionally, BetterMe needed to understand how its new features were affecting user retention and revenue rates, and which releases were gaining traction and which were not.

Monday.com, a work operating system (Work OS) that allows organizations to manage every aspect of their work, faced significant challenges in implementing machine learning (ML) solutions. The company's data team, BigBrain, was responsible for the data and analytics platform and ML initiatives. However, as demand for ML solutions grew, the data scientists found themselves heavily reliant on engineers to bring models to production. This resulted in a high time to value, with models often waiting for deployment until a developer was available to set up the infrastructure. Furthermore, the data scientists were siloed and had a disconnected workflow between where the model was trained, deployed, and monitored, creating unnecessary complexity. Key pain points included excessively high time to value due to production bottlenecks, dependency on developers and engineers for deployment, missing critical MLOps capabilities, inability to consolidate distinct endpoints into a multi-model endpoint pattern, and disjointed workflow due to each data scientist working with different machine learning tools.

ChargeHub, a service that helps drivers locate over 120,000 public EV charging points across North America, faced a significant challenge in maintaining its always-on platform. The company aggregates real-time data from third-party providers and its community of drivers, which it then makes available to drivers through its mobile and web apps. ChargeHub's API is also featured in mapping products, in-car entertainment systems, and elsewhere. The company recently launched ChargeHub Passport, an app that allows drivers to use multiple charging networks. With thousands of consumers relying on ChargeHub and strict SLAs governing API availability to partners, there was no room for downtime or inaccurate data. The challenge for ChargeHub's agile engineering teams was to maintain high-quality standards without compromising productivity. They identified handover between teams as a particularly risky moment in their development lifecycle, with ad hoc methods of teaching and learning APIs damaging both productivity and code quality.

Lenovo's ThinkPad line of laptops is known for its aesthetics and durability. However, the challenge lies in ensuring that the laptops are not only portable but also durable. The laptops face demanding loads and forces even when they’re just being carried from place to place. It’s important for the covers to be stiff enough, with minimal deformation, so that the rear cover will protect the display, while the base cover protects the motherboard. Another challenge was to reduce the thickness of the keyboard assembly without losing the traditional keystroke feel.

The financial services team at Keurig Dr Pepper relied on 3rd party vendors, outsourcing their Cash Application and other A/R functions to control costs. This led to limitations such as restricted access and control. Moreover, outsourced contracts prevented shifting to other platforms or discontinuation of services before the contract period. A lack of metrics and statistics made tracking outsourced resource productivity challenging, causing inconsistencies across processes and creating complications for the A/R team. They thus decided to reevaluate whether they wanted to continue their contract or more operations in-company to have more power and visibility over their processes. Keurig Dr Pepper took a leap by deciding to move their cash application process in-house, but new challenges appeared. The biggest issue the teams faced was that they were strapped for time to cover all critical accounts. In the case of check images and remittances flowing in through disparate sources for e-payments, the team had to link every payment and remittance and code deductions manually. The A/R processes were utterly manual, and there were no worklists incorporated to help analysts prioritize work, leading to time-consuming, inefficient, and wasteful processes.

USAA, a fully integrated financial services organization, was facing the challenge of scaling its design process to meet the needs of its growing digital customer base. The company, which has been committed to digital growth since the launch of its first website in the 1990s, was looking for ways to bring exceptional service to all digital channels without compromising efficiency or empathy in the design process. The challenge was to maintain technical design consistency across all product teams and to develop a design-specific scorecard that could monitor engagement with compliance and legal, as well as the accessibility of the design program. Another challenge was to develop individual designers' skills and deploy them within the lines of business to work on specific projects.

Ubik, an outsourcing company that specializes in building tech teams, was facing a significant challenge in sourcing candidates quickly and efficiently. The company's CEO and founder, Mike Bysiek, who has a rich history of working with big names like Xerox, Johnson & Johnson, and American Express, realized the need for a tool that could expedite the process of finding suitable candidates. The challenge was not just about finding candidates, but finding the right ones that align with the business goals of the company. The traditional methods of sourcing were proving to be time-consuming and less efficient, which was a major hurdle in the smooth operation and growth of the company.

Allstate Insurance Company was facing challenges with its existing human resources applications. The extensive customization of the software had resulted in powerful functionality, but it was not user-friendly and introducing new functionality was time-consuming and often cost-prohibitive. The company wanted to enable the newest talent management solutions, at a more rapid pace, and keep operational costs low. The challenge was to deploy a new solution successfully and gain all the benefits of cloud-based solutions while ensuring that Allstate continued to retain a single integrated system of record.

GE Healthcare, a leading manufacturer and distributor of diagnostic imaging equipment, was seeking to improve patient outcomes by reducing workflow processing time through the sharing of medical image data across specialists and referring physicians. The company wanted to increase the value derived from device usage and data by enabling the leverage of cloud compute, storage, and access. The challenge was that up to 35 percent of patient cases were being misdiagnosed, partially due to a lack of access to images, data, and records. Additionally, better interoperability between systems could save healthcare ecosystems $30 billion per year, according to GE Healthcare.

TINE SA, a Norwegian cooperative owned by farmers, has been collaborating with Norway’s farmers for over 160 years to understand their challenges and help them drive efficiency, productivity, and high-quality dairy product development. As market demands increase, so does each farmer’s need to bring products to market more efficiently. TINE has a long history of developing decision-making tools for farmers and collecting data from different dairy products at the farm level. However, as TINE considered the future of dairy production at both the farm and national level, its data science team realized there would be changes to the breadth and depth of its data sources, types of data, and data analysis capabilities available to develop decision-making tools for farmers to use in production. TINE knew it would have to change its approach to data and technology by becoming more data driven as an organization to drive better predictability of milk production and other key data points related to a cow’s health and the quality of milk produced.

The 1000 Ton main forging press had a 75 HP motor and fed a trimming machine. The motor pulley combination was situated on top of the Press at a height of about 15 feet thereby reducing its access for routine maintenance. The company found difficulty ensuring constant uptime of the Press.

The cancer treatment and research center was facing a significant challenge in managing and accessing its vast volumes of patient data, which were primarily in paper format. The institution's unique approach to treating patients as a whole, rather than a series of isolated maladies, resulted in extensive patient medical records. The sheer volume of these paper records often made it difficult for clinicians and researchers to rapidly and reliably access the information they required. This situation was negatively impacting productivity and causing delays. Furthermore, the practice of 'checking out' medical records for research purposes often left clinical teams without immediate access to these records, especially in cases of unexpected patient visits. The center needed a more effective way to work with patient information that would enhance the delivery of prompt diagnosis and treatment, and accelerate the progress of vital research work.

Alvaz, a Brazilian agritech company, faced significant challenges due to its rapid growth. With each new crop cycle, the demand for their services increased, leading to a surge in the number of employees and the need for better process organization. In 2019, the company grew from 5 to 20 employees, making traditional methods of communication and process management insufficient. As Alvaz expanded into more countries and Brazilian states, the need for a reliable system to manage employee requests to the central office became evident. These requests were primarily equipment-related, including uniforms, safety equipment, and drone maintenance parts. By 2020, with around 30 employees, Alvaz was frequently missing deadlines and lacked visibility over the status of their many requests. The company needed a solution to manage their requests and improve the employee experience.

Jacobs Douwe Egberts (JDE), a global agribusiness group specializing in coffee and tea, was facing challenges in managing its commercial steering and financial management. The company, which operates in over 80 countries and generates annual sales of more than €5 billion, needed a tool that could consolidate the progress of negotiations in relation to the initial budget and analytically manage all customer investments by category. The tool also needed to support the S&OP process, manage product/customer mix, pilot pricing strategy, and ease the exchange of information between different services. On the financial control side, JDE required a solution that could value plans by segment, brand, and customer, steer profit up to full P&L (real + forecast), and facilitate budget comparisons and reporting.