Euroclear, an international financial markets infrastructure company, was grappling with the challenge of data duplication and the associated costs. The company, which managed nearly 33 trillion euros in client assets in 2020, was looking for ways to improve data quality and eliminate unnecessary data duplication. The team recognized that while some duplication was necessary for business continuity and regulatory compliance, unnecessary duplication resulted in expensive and avoidable reconciliation efforts. The challenge was not only to identify and eliminate unnecessary data duplication but also to quantify the cost of data, which included direct and indirect costs, variable and fixed costs, and operational and capital expenditures. This was a complex task as solid reporting or hard figures were not readily available for many of these costs.

The Office of the Secretary of Defense (OSD) and their Comptroller Office of the Undersecretary of Defense are responsible for US defense policy, planning, resource management and program evaluation. Their decisions impact a wide range of operations, including human resources, weapons acquisition, research, intelligence and fiscal policy across all of the US armed forces. Given the size of the organization, data is crucial to its decision-making processes. However, ensuring decision makers have access to the right data, can trust in its accuracy and understand its context is a complex task. Recognizing this challenge, Greg Little, Director, CFO Data Transformation Office, sought to create a centralized platform for data and analytics across the organization.

Orange Spain, the second largest telecommunications company in the country, was grappling with the issue of customer churn in a highly competitive market. The company's market share was under constant threat from other telecom players, particularly start-ups offering low-cost introductory deals. The company's approach to data governance was inefficient, with data being managed using spreadsheets. This made it difficult for data scientists to quickly access the right data and ensure its accuracy. They spent days searching for information and exchanging emails with different business teams to establish data validity. The company's business glossary was in a spreadsheet, making it difficult to maintain, search for information, and establish clarity around data ownership.

Engie, a leading European energy company, is on a mission to achieve carbon neutrality for its customers and operations. The company, operating across five continents and 70 countries, is facing the challenge of efficiently managing and utilizing vast amounts of data related to energy generation and distribution, pricing, weather, and customers. The data is critical for driving efficient renewable energy production. However, the data was siloed within separate business divisions, making it difficult to share and use effectively for strategic decision-making. Furthermore, the company was undergoing a major business transformation, changing from 25 country/regional operations to four enterprise-wide divisions. This transformation required a more agile and resilient approach to data management, especially in the face of global events like the climate crisis, war in Ukraine, and the COVID-19 pandemic.

Freddie Mac, a leading mortgage loan company, was faced with the challenge of managing a colossal amount of data collected over the years. The data ecosystem was multi-generational, with Cobolt Mainframes, relational and star schema base data warehouses, data marts, and system-to-system integrations. The company also implemented Hadoop’s analytics platform for big data needs. This complex data ecosystem created complications for data consumers, making it overwhelming to locate the required data. The challenge was to transition to a self-governed cloud native data lake, making all data available in a single place, while ensuring security, resiliency, scalability, reliability, and availability. The business was heavily dependent on the seamless provision of this data for innovation, analytics, and predictive analysis.

Froedtert & the Medical College of Wisconsin (F&MCW), a regional health partnership, was facing challenges in managing and analyzing their vast data resources. The organization had a wealth of data from electronic medical records, financial spreadsheets, and HR data. However, the data was managed in silos using tools like SharePoint and Excel spreadsheets, leading to limited data sharing and collaboration between departments. There was also a lack of consensus on what discrete data metrics signified. Furthermore, there was a lack of data ownership, particularly among business and clinical leaders, who viewed data as a technical aspect and left its analysis to data governance and technical teams. The organization needed a solution that would enable better information analysis, foster data ownership, and improve data governance.

ATB Financial, a financial services provider to over 770,000 residents of Alberta, Canada, was facing challenges with its data-driven decision-making process. The organization's technology architecture consisted of a variety of disparate systems that were difficult to navigate. The knowledge of how data flowed through these systems was tied up with individuals, creating a dependency on 'tribal knowledge'. This made it difficult for the organization to drive Data Intelligence in an automated manner. Furthermore, ATB Financial was looking to modernize its data infrastructure in 2019. The organization needed a cloud-based solution that could support agile development methodologies, known as DevOps, and automate its data pipelines.

Sub-Zero, a leading American-based manufacturer of premium kitchen equipment, was facing challenges in transforming into a more data-driven organization. The company, which started as a refrigeration company in the 1940s and expanded into cooking appliances and dishwashing equipment, needed a new, enterprise-encompassing data environment. This environment needed to have comprehensive data governance and incorporate powerful new data technologies, such as Microsoft’s Power BI and Snowflake on the Azure Cloud. The challenge was to win over business users, find enthusiastic data owners and stewards, enable them with requests, ideas and workshops, and bring data governance to the data governance group itself.

Wolters Kluwer, a 127-year-old company, embarked on a digital transformation journey with the goal of future-proofing their business. The company's Governance, Regulatory and Compliance division, CT Corporation, was tasked with managing the data portion of this transformation. The challenge was to transform an enterprise with many duplicative systems and data sources, future-proof their products and data, and govern the process. CT Corporation had a wealth of data but struggled to derive the necessary information and insights to understand and address customer needs. The company had previously considered data governance, but either the company wasn't ready or the tools available at the time weren't up to the task. Furthermore, the company wanted to move to cloud-based services as part of their digital transformation, which required a tool that could be hosted, maintained, and supported without incurring excessive costs.