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CTI Solutions Group was facing a significant challenge in managing their reporting solution for customers. The company's clients were increasingly involved in monitoring various aspects of their businesses, including wholesale inventory, sales by style, and SKUs. CTI Solutions offered analytics via Excel and other tools in both HTML and downloadable formats. However, the company was struggling to keep up with programming and maintenance, despite having a team of 12 local full-time programmers and another 30 offshore. Six of these programmers were dedicated to maintaining task reporting for 93 countries throughout the world. The company realized that they needed a more automated, more uniform solution that could present information in a variety of ways, both online and downloadable. If they didn't move to a more agile reporting and data handling system, they would not be able to satisfy their clients' growing needs for timely information or hire the talent they needed to grow.

Bigmate, a company that primarily deals with the Internet of Things (IoT) or more specifically, the Internet of Assets (IoA), faced a significant challenge. The company's telematics platform provides real-time vehicle tracking, data analysis, and tools that scale vertically or horizontally. However, new and emerging telematics applications, such as vehicle engine monitoring, were driving the need for customer accessible dashboards and reporting. With data generated as frequently as once a second, delivering visualizations that bring clarity was a crucial requirement. The main objective was to find a product that would enable them to solve their customers' problems effectively.

Biota Technology, a leader in industrial genomics, identified a unique business opportunity to apply DNA sequencing and genomics, typically used in medical and scientific fields, to industrial use, specifically for subsurface exploration in oil and gas. The company recognized the potential benefits of their innovations for energy companies under pressure to improve capital efficiency and reduce operating costs. However, the complexity of genomes and biological material made Biota's vision challenging to implement and scale. The company needed to characterize fluid movement in oilfields by surveying subsurface microbes at different locations, depths, and times, and organizing them into DNA markers. Each Biota sample contained around 20,000 sequences describing around 100 DNA markers, resulting in hundreds of thousands of microbial data points that needed to be stored, categorized, and analyzed for informed business decisions.

A large tech company was using legacy analytics platforms to support over 500 internal clients with predictive modeling projects. These projects were aimed at improving customer acquisition and retention, identifying up-sell and cross-sell opportunities, increasing revenue, and understanding customer lifetime value. However, the legacy systems were primarily used for data extraction, processing, analysis, and reporting, and had several limitations. The global analytics team was heavily dependent on these systems, and the idea of migrating to a new platform, the TIBCO® Data Science platform, was met with reservations. The team decided to conduct a feasibility analysis by creating a customer lifetime value model for one of the business units, a project they had undertaken multiple times before.

The management of regulatory changes is a significant challenge for financial companies. The process is often costly, time-consuming, error-prone, and unpredictable. The volume of regulatory information published by state and federal governments is overwhelming, and financial companies are forced to invest heavily in manpower to keep up. Compliance.ai offers solutions to these inefficiencies, helping financial services companies manage and respond to the growing volume and pace of regulatory change. Their platform includes purpose-built machine learning models and an API for programmatic access to regulatory information. However, the challenge was managing access to the API and its offerings.

In the United States, roughly one in 20 patients admitted to a hospital develops an infection, with surgical site infections being the most common. These infections account for more than 30 percent of occurrences, leading to illness, prolonged hospitalization, and even death. The total cost of hospital-acquired infections is estimated at $10 billion per year. Surgeons at the University of Iowa Hospitals and Clinics wanted to know when patients were susceptible to surgical infections to make critical treatment decisions in the operating room. Dr. John Cromwell believed that predictive analytics could prevent a high percentage of surgical site infections and decrease healthcare costs. However, the division’s desktop analytics environment could not handle large distributed data volumes, posing a major roadblock.

While the introduction of high-throughput techniques and lab automation helped to save a lot of time and manual effort to screen compounds, organizations lacked an easy to handle tool to perform accurate analyses that didn’t require a highly skilled analyst. Moreover, to cope with the tremendous amount of data, the scientists used to evaluate results on a per batch basis, skipping over information from the entire dataset thus missing opportunities to make better decisions. A large volume of complex data has to be assessed throughout the workflow to achieve the goal of delivering high-quality compounds. When the assay is developed and optimized it’s not only the activity or inactivity of a given target that has to be considered, batch size, reagent preparation, temperature, and signal stability need to be evaluated for a robust assay. Moreover, the quality of the screening campaign needs to be supervised to eradicate systematic errors, and hits have to be carefully selected to minimize false positives and false negatives in order to concentrate on the most promising candidates and make the most out of the available resources. Many data evaluation applications have difficulty dealing with the sheer volume of data, let alone handling the data’s multivariate and multiparametric nature.

The result of these funding trends was that QHC no longer received a predictable amount of money from the government. Sixty percent of its funding is now based on demographics, referral patterns, and outcomes. While this is a positive step towards improving patient care, it requires the organization to identify opportunities that align with this new model. Initial estimates called for the company to find $10 million in savings from its $160 million budget. To meet these needs, QHC used TIBCO to build a data warehouse and deliver analytics via a user-friendly portal. More than 300 people throughout the organization, from nurses to executives, rely on this tool to measure fundamental processes and analyze critical patient data trends. TIBCO technology has helped the company measurably improve operations, meet financial requirements, and increase favorable outcomes in patient care.

The airline industry has evolved significantly, shifting from traditional sales agents and ticket offices to online and mobile platforms. Air France and KLM, after merging, faced the challenge of integrating multiple reservation systems, legacy systems, and commercial off-the-shelf systems. They needed an integration platform to connect these diverse systems, ensuring seamless service to passengers 24/7. The integration had to be fast, cost-effective, and future-proof to support new functionalities and personalized services.

The shipping industry is facing increased competition, shifting alliances, and growing customer demands for better insights and faster decision-making. Carriers are struggling to keep up with backend technology advancements and are unable to leverage big data analytics effectively. This results in higher operational costs, such as terminal handling fees and bunker costs, which hinder customer satisfaction. CargoSmart aimed to provide ocean carriers with advanced analytics for better visibility and real-time decision-making to address these challenges.

The primary goal of Con-way’s integration strategy was to provide the core business with real-time analytics and automated decision-making. The risk in their business was not having real-time visibility, which could lead to inefficiencies such as running empty trucks, resulting in no revenue but only cost. They needed to optimize drivers, trailers, routes, and miles traveled continuously as conditions changed. Additionally, during major weather events, they needed to ensure critical shipments were delivered and have real-time information on truck locations and available routes. Business continuity and real-time information were critical for their operations.

Over the last decade, the energy market in the Netherlands has undergone significant changes. Consumers gained the right to switch suppliers, the government unbundled retail from power generation functions, and Essent was acquired by RWE. Additionally, Essent expanded its services to the broader European market. The company faced the challenge of adapting quickly to customer needs to stay competitive. Legacy systems and the inability to adapt were major inhibitors. Essent needed fast performance and decoupled systems for optimal flexibility to meet these evolving demands.

ING Bank Turkey aimed to enhance customer service and market share by making banking accessible through mobile devices, the Internet, or branches. The bank faced challenges such as lack of transparency in service levels, inefficient processes, and user-unfriendly systems for both customers and employees. They needed to streamline processes, decentralize operations, and increase service and sales while differentiating their distribution with new third-party channels.

Lufthansa Cargo’s IT systems had evolved into a complex and heterogeneous structure centered around the host-based MOSAIK application, which dated back to the seventies. This system enabled access to product information, bookings, reservations, and shipment tracking, as well as the exchange of waybill data via electronic data interchange (EDI). However, developing add-ons and adaptations to this system was time-consuming and costly, which was necessary for the optimal support of Lufthansa Cargo’s time-definite services. These services responded to customers’ needs for the rapid shipment of specific types of goods within a precisely defined time frame. The company needed to reduce operating costs by simplifying the complexity of interfaces and maintenance, and to accelerate the time to market of its new products and services. Enterprise and business application integration were identified as key facilitators, connectors, and control systems to achieve these objectives.

Royal Caribbean strives to anticipate guest needs and optimize their experience throughout pre-cruise, cruise, and post-cruise interactions. To accomplish this faster, better, and at lower cost, the IT team realized it needed a higher level integration and a services-oriented architecture that would let them transform legacy applications into reusable and re-combinable software services. The company needed a cohesive enterprise (ship and shore) integration services foundation and governance to manage the service lifecycle and sharing across the organization.

As Katarina Khan, head of enterprise architecture, explains, “Over the last 20 years, the airline industry has evolved from a system of long-established, state-owned carriers to a dynamic free-market industry. There are a lot of low-cost airlines now, and for passengers, it’s become very much about a digital experience—self-service apps for booking, re-booking, and flight updates. “Back in 2001, our challenge was to move away from old point-to-point integrations and become more loosely coupled—to reuse integrations and have a very reliable, secure, high-performing infrastructure. We wanted to give the business a shorter time to market and make it easy to integrate with partners and communicate with our customers in a modern way. If we didn’t modernize, it would have been very difficult to deploy new solutions to meet customer demand.”

Over the years, Societe Generale’s Risk, HR, Accounting, Procurement, and other business units (BUs) had each built their own information systems to meet business needs. These systems had evolved into separate silos of technologies with no plan for reuse. The results included high development and maintenance costs and data discrepancies that ultimately impacted the ability to make informed decisions. To overcome these issues and industrialize integration between business units, Societe Generale’s IT management decided to implement a platform to manage and govern data exchanges: the Système d’Echange Groupe (SEG), or Group Exchange System.

Swisscom faced significant challenges due to intense competition from local Telco operators and over-the-top providers that use local network resources. Additionally, the company was undergoing a rapid transformation to digital business, which necessitated an organizational restructuring and IT platform consolidation. The primary challenge was not only integrating technologies and IT platforms but also integrating people from different divisions to create solutions for the entire company. Without successful integration, Swisscom risked slower product creation, increased costs, and reduced revenues.

The rapid pace of change in the telecom industry presents a major challenge to the wireless providers. Consumers are constantly demanding changes like new rate plans, value-added services, better connectivity, and popular mobile applications. Throughout the industry, companies fight to keep up with the expectations of customers. As applications on mobile devices become richer and begin to generate more data, T-Mobile strives to be at the forefront of change. Consumers expect to see updated data in their apps the moment they open the phones, which necessitates automatic updating of data and faster transmission speeds. The sheer volume of traffic that smart phones and mobile apps generate is going up exponentially. By adopting the right technologies, T-Mobile wants to provide exemplary service and connectivity to its customers.

At Western Union, customers are central to everything. The company aims to make financial transactions as fast and secure as possible, with no margin for error. Customers are increasingly embracing new technologies, especially in regions like Indonesia, India, and Africa, where adoption is faster than in some developed countries. This shift brings new requirements, such as the need to complete transactions on mobile devices. Western Union faced challenges in integrating its payments ecosystem with hundreds of banks, half a million locations, and various post offices, ATM networks, mobile devices, and online and retail locations.

BMO Financial Group faced the challenge of meeting customer expectations in real-time across various channels, including branches, call centers, and digital platforms. With the rise of digital marketing and social media, customers expected personalized and timely interactions. The bank needed to simplify and automate processes, provide real-time information, and create data visualizations to make efficient decisions on how best to serve customers.

Retail giant Marks and Spencer wanted to empower its business analysts to learn more about the business and use that knowledge for better decision-making. They aimed to improve the productivity of the organization, particularly focusing on IT. This included optimizing the number of people working on projects and improving the speed at which solutions could be delivered. M&S started looking for a solution that would supply self-service data access and empower employees to confidently use data to answer key business questions without IT support.

BroadReach faced a significant challenge in improving healthcare outcomes due to the fragmented nature of data across various clinics, hospitals, and NGOs. The delay in data reporting, especially for late-stage HIV and TB patients, was critical as it could take weeks to get a report, leading to severe health consequences. The primary challenge was to integrate and understand data from multiple sources quickly and effectively to make informed decisions that could save lives.

In the semiconductor industry, constant margin improvement is a common challenge, and no different for NXP Semiconductors. They have a complex sales model involving direct sales to customers and through distributor channels. Managing distributor incentives and ensuring no margin leakage across different regions was a significant challenge. The traditional, static BI environment was slow and highly dependent on IT, making it difficult to add new data columns or graphs to reports. Analysts spent more time pulling data from various sources rather than analyzing it, leading to delays in identifying and acting on outliers.

Though nonprofits want data, they don’t always have the ability to get it. “When we start a project to help a nonprofit, we sometimes see a data collection issue, and not collecting data can have implications on funding. Funders are the biggest group demanding data from non-governmental organizations (NGOs), nonprofits, and even government,” says Heeke. At the same time, funders aren’t always keen on the idea of their donations going towards analytics tools. “There’s a huge opportunity to use enterprise-level tools for social impact and finding ways to make this technology accessible to the non-profit community is critical.” “It’s really about the quality of data and the ability to make the data visible and useful to a wide audience. People forget that good ideas come from people on the ground, and not necessarily from the analyst in an office,” says Heeke.

digitalSTROM aimed to create an infrastructure for smart homes that integrates cloud-based services like weather and security. The platform needed to handle high performance and low latency, be open and secure, and integrate various technologies from multiple partners. It also had to prioritize events from IoT devices, ensuring fast responses for critical alerts like fire alarms while managing less urgent events efficiently.

Like many companies in the energy industry, the challenge for Cargill was the volume of its diverse data and the difficulty of processing it quickly to enable fast response to market events. Without a strategic partnership, Cargill would have had to develop these solutions internally, which would not be ideal. The company needed a way to manage and analyze large volumes of data efficiently to make timely and informed decisions.

In 2007, XL committed to becoming a leader in a challenging market, one with 98 percent prepaid subscriptions and a state-owned telco that held over 50 percent market share. One challenge was that functional IT systems were in discrete silos, which did not allow the company to respond and support the business effectively. XL required a platform that would provide reliability, scalability, and allow for agile delivery of functionality. Additionally, XL's marketing challenge was to predict customer desires to optimize sales and marketing, understand customer needs, and identify opportunities to provide value.

ZE clients were looking for self-service BI, and ZE needed to decide whether to build or partner for this solution. Building in-house would require more developers, a longer time to market, and a shift in focus. ZE's clients demanded fast data availability and scalability in terms of software, architecture, technology, support, pricing, capability, and growth. ZE aimed to empower clients with the best total solution to maintain and grow its market share.

As QSuper transitions to providing superannuation management for a broader range of customers, the demands of a more competitive market suggested the need for change. The company aimed to improve operational efficiencies by streamlining back-office functions and reducing costs. QSuper faced challenges with application silos created by various business functions running separate operations and point-to-point interfaces that resulted in a brittle integration infrastructure and disjointed communications. This complexity made it difficult to deliver solutions promptly, affecting customer experience and increasing costs due to inefficiencies in communications.