The company, a leading global producer of passive electronic components and interconnects, was facing several challenges. It was operating on a legacy mainframe infrastructure that was over 30 years old. The mainframe IBM environment, which included Software AG’s Adabas, Natural and EntireX, was still functional but was not equipped to handle the demands of the modern manufacturing world. The company was facing rising operational costs and was struggling to prepare for the green energy revolution and faster production cycles. By 2013, the company was in a critical situation. Backup hardware had become impossible to find, older users supporting the legacy system were retiring, software updates were non-existent, and adding new functionality was off the table. This meant that in the event of a disaster, there was nobody the company could call.

The company, a leading marketing agency, was facing challenges due to the overwhelming volumes of data, the industry-wide revolution in digitalization, and a disruptive media and advertising environment. The company needed to deliver marketing messages to customers where they are, when they are receptive, and perfectly optimized for the right device or medium. They also needed to move beyond simple, post-campaign analysis to a set of analytic techniques that incorporated probability, statistics, algorithmic modeling, data mining, and machine learning. The volumes of data, computational resources, and technical knowhow required for this were tremendous.

Chevron, one of the top three integrated energy companies, wanted a centralized SAP® system with standardized process and master data design. The company was seeking to extract more value from its business processes. Additionally, Chevron identified the need for business process intelligence and a governance framework to ensure high quality and management of all material master data in SAP®. The challenge was to implement a system that could cover a wide range of processes, activities, and master data types across multiple countries.

The company, a global leader in consumer health products, was facing several challenges. They wanted to 'win differently' with a digital business platform, improve access to information enterprise-wide, reduce manual work and standardize processes, increase visibility across the global supply chain, and become more customer-centric. They were dealing with painful processes like manually collecting data from disconnected systems. They had no way to detect issues in delivery flow, and customer interactions were reactive rather than proactive. Costs increased due to shipment escalations. The disjointed Approval for Product Release (APR) process required manually consolidating paper specs into binders across all R&D functions. Every region had a different framework with no way to monitor or share information. There was no workflow support, no integration to other systems.

MPWiK S.A., a major water company in Poland, aimed to improve productivity across all operational areas while maintaining the quality of its core services. The company also sought to introduce a management system based on business processes that could be independently audited through formal certification, including ISO 9001 for quality management, ISO 14001 for environment management, and ISO 18001 for workplace safety. In 2010, MPWiK S.A. initiated a process transformation program to enhance productivity and transparency. Information technology was identified as a key factor in the success of this initiative, enabling the company to reorganize and introduce measurable and repeatable processes. This led to improvements in all core operational areas such as water production, network service and maintenance, and water leakage reduction.

As big, fast data proliferates, more and more data streams are being generated in real time from a myriad of data sources, such as Internet of Things (IoT) sensors, markets, mobile devices, internal transactional systems and clickstream analysis. Real-time insights must be derived from this data to give a competitive edge to agile organizations that want to act on these insights before they lose their value. To handle billions of these data streams, new software architectures and techniques are needed. These types of software are called “Big Data Streaming Analytics.” Companies need educated specialists to harness the power of big data. The Streaming Analytics Education Package (SAEP) is Software AG´s answer to the growing demand for data science experts. Universities and private companies share the responsibility of training the sought-after talent currently demanded by the market.

Suva, a Swiss insurance company, was facing the challenge of managing its complex and diverse business processes for which no single-source IT solution existed. The company had to manage its heterogeneous software and hardware systems, which were comprised of core prevention, insurance, and rehabilitation solutions as well as various interdisciplinary applications. Suva was also looking for a way to align its business strategy and IT, support corporate governance, and manage its IT architecture in a heterogeneous IT environment.

HealthCare.gov, the official website of U.S. Affordable Care Act, was launched in 2013 to assist millions of uninsured Americans. However, the site was over budget and unreliable, fraught with technical difficulties. Citizens had only from Nov. 1 to Jan. 31 to use the site compare insurance options and sign up for 2014. The unexpectedly high number of visitors began to overload the site’s infrastructure. The Centers for Medicare and Medicaid Services scrambled for answers from a variety of sources, including internal government experts, external programmers and industry partner, Software AG. The main challenge was to rapidly improve the performance of the website, assure a stable and scalable platform— especially during peak buying season, and increase site reliability so that no data’s lost.

The global player in the oil and gas industry was spending over $1 billion annually on technology to organize, store, and share information that drives the business. However, the enterprise IT infrastructure had become complicated due to piecemeal construction throughout the company’s business units and a tangled web of legacy systems from mergers and acquisitions dating back to the early 1980s. The company had an estimated 8,000 applications costing over $600 million to run annually. Despite the significant annual expenditure on new systems, there was no alignment to a multi-year road map. Application strategies were inconsistent across the business units, and there was no common architecture or strategy.

Fabick Cat, a tractor dealer specializing in the distribution of Caterpillar® equipment, faced a challenge when it acquired a second Caterpillar dealership. The newly acquired dealership had its own custom-written order processing system running on OpenVMS with RMS files, different from Fabick Cat's IBM® AS/400 based system. Fabick Cat wanted a solution to provide a data warehouse for both systems to ensure uniform reporting and analytics for both companies. The initial plan was to FTP files from the OpenVMS server and load them into SQL Server. However, the amount of data downloaded every day and the manual nature of this approach raised concerns.

The company, a global leader in consumer health products, was facing several challenges. They wanted to 'win differently' with a digital business platform, improve access to information across the enterprise, reduce manual work and standardize processes, increase visibility across the global supply chain, and become more customer-centric. They were dealing with painful processes like manually collecting data from disconnected systems. The company had no way to detect issues in delivery flow, and customer interactions were reactive rather than proactive. Costs increased due to shipment escalations. The Approval for Product Release (APR) process was disjointed and required manually consolidating paper specs into binders across all R&D functions. Every region had a different framework with no way to monitor or share information. There was no workflow support, no integration to other systems.

The company, a leading marketing agency, was facing challenges due to the overwhelming volumes of data, the industry-wide revolution in digitalization, and a disruptive media and advertising environment. The company needed to deliver marketing messages to customers where they are, when they are receptive, and perfectly optimized for the right device or medium. They also needed to move beyond simple, post-campaign analysis to a set of analytic techniques that incorporated probability, statistics, algorithmic modeling, data mining, and machine learning. The volumes of data, computational resources, and technical knowhow required for this were tremendous.

Medellin, the second largest city in Colombia, was facing a number of challenges in its quest to become a Smart Digital City. The city's citizens were demanding better public-private cooperation and communication, public services accessible and accountable through the web, and a government able to respond to requests quickly and without mistakes. The existing processes for citizens to interact with the local government were cumbersome and error-prone. A concerned citizen would submit a paper request in person—with no telling how long it would take to get a response, if ever. Without tracking or a coordinated workflow, some requests were lost in a sea of paper.

The company, a top loyalty program provider, was facing a challenge in meeting the aggressive end-to-end Service Level Agreement (SLA) of 500 milliseconds for every transaction, as demanded by large retail prospects. The company's data center took an average of 20 percent longer than that — 600 milliseconds — just to query upwards of 32GB of customer data in a central, disk-bound database. Furthermore, Java-related garbage collection pauses caused unexpected spikes in response times that would have resulted in financial penalties for failure to meet the SLA, and in unhappy customers. In order to win new business from larger retailers, the company realized it had to move its data into fast machine memory.

The company, a global industrial powerhouse, wanted to embrace the Internet of Things (IoT) to drive advances in high-end manufacturing. However, the early versions of its open IoT platform had limited integration capabilities and could only connect their own products. It was impossible to import application data into the IoT platform or to export results into applications. The company wanted to transform its platform into a driver of key digital services business by harnessing data from other manufacturers’ products.

The bank, founded in 2010, faced several challenges as it grew rapidly to over 1.2 million customers and 55 branches in just a few years. The high barrier to market entry, consumer stickiness, and extraordinary growth were some of the challenges. The bank also lacked a unified process management system and had to deal with looming GDPR regulations and manual documents. As a newcomer in the field, the bank sought to find a balance between well-worn protocols related to process planning, optimization, and efficiency, and the demands of savvy customers for legendary service and great products. The looming deadline for enforcement of the EU General Data Protection Regulation (GDPR) in early 2018 only added fuel to the fire.

The company, a global leader in medical technology, services, and solutions, was facing several challenges. They were in the process of transforming into an outcome-based healthcare solutions provider and needed to quickly integrate acquisitions to maximize operating efficiencies. The company was also looking to change how the world delivers patient care. The company's systems were previously locked down by point-to-point integrations, which was not flexible and technology-dependent. The company needed a more flexible, technology-agnostic solution for lower-cost internal and partner integrations that could be quickly configured, turned on, and changed.

BSA Group, a leading provider of asset management solutions in Australia, faced several challenges in their quest to optimize building management. They needed to connect disparate systems and sensors in buildings to collect and analyze new information and find efficiencies. They also needed to consolidate this information into a single view for customers and provide integration and connectivity between disparate and proprietary building systems and vendors. The goal was to reduce energy and maintenance costs for customers while addressing new revenue opportunities in the market.