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.

SpareBank 1 Forsikring, a part of the SpareBank 1 Alliance and the third largest supplier of pension products in Norway, faced a significant challenge due to increasing regulations in the financial services industry. These regulations increased the reporting obligations to various internal and external stakeholders at SpareBank 1 Forsikring. The bank needed to detect and report suspicious activities related to money laundering and terrorist financings, such as securities fraud and market manipulation. To meet these obligations, the bank required a new data platform that could efficiently integrate data from all kinds of heterogeneous data sources, deliver the data to consuming applications, provide the flexibility to add new data sources quickly, support a variety of different input/output formats and tools, shorten time-to-delivery of data integration projects, support modern data and analytics use cases, and ensure that the data infrastructure is secure, well-governed, and intuitive enough to promote a self-service data-sharing culture within the organization.

Suez, a leading French multinational corporation with operations primarily in water, electricity, natural gas supply, and waste management, faced a significant challenge in consolidating data from its various global entities. The company's highly decentralized structure made it difficult to create a global data analytics system, which was crucial for its operations. The inability to integrate customer data from its global business entities and homogenize this data using a unified semantic layer was a major obstacle. The company needed a data integration platform that could overcome the expensive and complex process of physically moving data across varying regulatory boundaries to a central data warehouse.

VTTI, a global energy storage services company, was faced with the challenge of managing and utilizing massive amounts of data generated from its operations. With over 10 million cubic meters of storage capacity located across five continents, the company loads or discharges around 11,000 vessels, trucks, and trains globally every year. This scale of work provides VTTI with unique insight into energy trading and product flows. However, the company needed a way to generate insights from this data and make it readily and effectively available to business stakeholders. The data was also crucial for monitoring pumps and pipes, as well as for engaging in predictive and prescriptive maintenance of the entire infrastructure. VTTI was in need of a solid analytical foundation that could serve use cases in the field of predictive and prescriptive analytics to better manage customer expectations and enable customers to better manage expectations with the rest of the value chain.

ABSA Group Limited (ABGL), a leading financial services group based in South Africa, was facing challenges with its data infrastructure. The infrastructure, which consisted of two data warehouses and a complex series of extract, transform, and load (ETL) processes, was not only costly to operate but also time-consuming in delivering data to business analysts. Recognizing the need for improvement, ABSA decided to modernize its data ecosystem and embrace a big data strategy. However, during the modernization process, ABSA encountered difficulties in ensuring the continuity of business operations. The company needed a data abstraction layer between the legacy systems and the new strategic platform to continue building out the new platforms without disrupting business processes.

Engie Mexico, a subsidiary of Engie, is a leading energy provider in Mexico, serving over 2.5 million residential and industrial customers. Despite its significant presence, the company faced a major challenge in managing its data infrastructure. The existing system was segregated and comprised of extract, transform, and load (ETL) processes, data warehouses, and Excel files. This fragmented approach resulted in no single source of truth, making it difficult for the company to track changes across various data sources. The lack of a unified data infrastructure hindered the company's ability to cater to a wide range of business requirements and build new business use cases. This situation led to the initiation of the Data Virtualization and Capability Integration (Da Vinci) project, Engie Mexico's first major data-centric implementation project.

Starhub, a leading telecommunications company based in Singapore, was facing challenges with its complex ecosystem of data and applications. The company was in dire need of better governance to manage its data effectively. As part of its broader digital transformation and IT modernization program, Starhub aimed to establish a standard semantic and data delivery layer. The goal was to streamline data management, reduce reliance on IT, and lower data infrastructure costs. The challenge was to find a solution that could deliver timely data across the organization through self-service, while also ensuring security and minimizing disruption to the business.

Sunbelt Rentals, a leading player in the equipment rental industry and the second largest rental provider in North America, was on a mission to provide an unrivaled Customer Experience (CX) for all kinds of rental customers. The company aimed to grow from the number two equipment rental company in North America to number one. To achieve this, Sunbelt Rentals needed a growth plan that could drive and support an enterprise-wide digital transformation, with data at the core of this journey. The company faced challenges in modernizing their data integration efforts and creating a single access point for the business and developer teams to access all of their data assets.

Fifth Third Bank, the largest money manager in the midwestern United States, was looking to transform into a data-driven company. The bank embarked on an enterprise data strategy initiative that leverages data virtualization and logical data fabric. The primary objectives of this initiative were to improve compliance with numerous regulations, exceed customer expectations, increase adaptability and automation, and accelerate the modernization of its core data infrastructure. However, the bank faced challenges in establishing a critical data abstraction layer that could limit the user impact of a platform migration, creating standardized, reusable data domains, and building a modern, distributed data mesh architecture for analytical data management.

Schroders, a UK-based investment management firm with over $939.2bn worth of assets under management, faced a significant challenge in integrating third-party data sources with internal data to create comprehensive environment, social, and governance (ESG) models. These models are crucial for understanding the companies in which Schroders invests, as the firm aims to ensure its investments are directed towards well-governed, socially, and environmentally compliant companies. The inability to effectively integrate these diverse data sources hindered the firm's ability to produce timely ESG views of its portfolios, which in turn impacted the ability of fund managers to manage ESG risk and make informed investment decisions.

MultiChoice Group, a leading entertainment company, was facing a significant challenge in managing and integrating its geographically distributed data. The company operates in various countries in Africa and maintains relationships with different service providers, each operating in different countries and providing data in a variety of formats. This diversity made it difficult for the data and analytics team to integrate and process the data using traditional methods. The team was spending over 80 hours per month on data preparation, and the data required for reporting was almost a month behind schedule. The company needed a data integration solution that could connect to all kinds of data sources, integrate the data through a single service layer, and enrich data on the fly.

Bültel International Fashion Group, a global men’s and women’s fashion company, faced significant challenges with its data infrastructure. Over the years, the company had grown at a pace that outstripped the capabilities of its data infrastructure, rendering it inefficient and insufficient to meet the demands of today’s transient markets. Data was often stored in legacy databases, in different formats, with no common naming conventions or referenceability. The only way to exchange data between the different data silos was by importing and exporting csv files, a highly inefficient process. Bültel needed to transform its data infrastructure, merging its data silos for a single point of truth, and enhance the data infrastructure’s capabilities to meet current and future data and analytics needs.

Nedbank Group, one of the four largest banks in South Africa, was facing a significant challenge with its data infrastructure. The bank, which serves 7.5 million customers, of which 2.3 million are digitally active, was loading six terabytes of data into its operational data store and data marts on a daily basis. This sheer volume of data was proving to be a challenge for the bank's IT infrastructure to process and manage. The complexities posed by traditional data models were also a hurdle for data consumers within the organization. The bank needed a solution that would simplify its data infrastructure, reduce data integration costs, and provide a single interface for accessing all types of data within the organization.

Coface, a global credit insurer, faced a multitude of regulatory and compliance challenges due to its large and geographically dispersed nature. The company aimed to enhance operational efficiency and stimulate business growth by introducing innovative business use cases. However, the primary requirement for dealing with these challenges was the ability to provide comprehensive data in the right context. Integrating this data from various internal and external sources, including on-premises and cloud systems, proved to be excessively complex. The company needed a solution that could simplify this process and make previously inaccessible data easily available, while also ensuring governance, compliance, and data security.

Estes Express Lines, a major transportation company operating across the United States, Canada, Mexico, Puerto Rico, and the Caribbean, was facing significant challenges due to the storage of data across numerous data silos. This led to a number of issues including the need for IT to physically move data before it could be put into production, spending time, money, and resources on redundant hardware and code, and handling data security separately by different teams. Furthermore, business users were dependent on IT to furnish their data requests, which negatively impacted the company's agility and efficiency.

Gebrüder Weiss, an international transport and logistics company, was grappling with the challenge of managing and integrating a vast amount of data generated from its operations. The data, which was crucial for process optimization and improving delivery times, was scattered across various on-premises and cloud environments. The heterogeneity of these data sources made it difficult to integrate the data for contextual analysis. The company's end users had to manually collect data in CSV files from different applications and collate them before performing the necessary analysis. This process was not only inefficient but also unsuitable for a business that required real-time data analysis.

Indonesia Stock Exchange (IDX), a Self-Regulatory Organization, was facing a challenge in delivering real-time, trusted market data to its members and subscribers. The organization was looking for a solution that could streamline the process of data delivery, making it more efficient and reliable. The challenge was to integrate real-time messaging and streaming data with other API data sources, and manage both internal and external data in a single place. The goal was to create a trusted and credible financial market infrastructure that is accessible to all stakeholders through innovative products and services.

Insiel S.p.A., the in-house ICT company of the Friuli Venezia Giulia region, has been gathering a large amount of historical data and information related to its region, public administration, local authorities, and public health since its founding in 1974. The company wanted to enhance the value of this data by integrating it and making it available to relevant stakeholders for services to citizens and businesses. However, the company faced challenges due to the vast amount of data sources with different data formats and different security protocols. Without an efficient tool to integrate these data sources, it was difficult for Insiel to achieve its goal. The company needed a data platform that would allow users to focus on data analysis and generating insights, without having to worry about the technicalities of the different data sources.