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Endpoint Clinical, an Interactive Response Technology (IRT) provider, was faced with the challenge of maintaining data integrity at trial closeout. The company typically provided audit data to investigators through the trial sponsor, who acted as an intermediary. However, this process raised regulatory concerns around data integrity due to the potential conflict of interest of sponsors who have a vested interest in gaining market approval of the products being studied. Regulatory bodies preferred to restrict audit log data to more neutral parties to avoid any possibility of data being modified to support approval. However, completely excluding sponsors from the process was not a viable solution as they still needed to have oversight of the overall process. Endpoint Clinical was tasked with finding a way to strike a balance between providing sponsors the oversight they need without making regulators uncomfortable.

GBBN Architects, an architectural design firm with over 150 employees across five offices, found themselves at a crossroads in 2021. Their on-premises N5 storage device running their virtual environment was soon running out of support, as were their Panzura appliances. The expiration of hardware and software licenses presented an opportunity to explore new technologies and providers. GBBN was also grappling with the challenge of managing company data more effectively. The IT team often received support tickets about missing projects, and without data usage reports from individual staff members, diagnosing the issue was a time-consuming process. Secure file collaboration was another challenge. GBBN had to give project partners access to their network, which posed a risk of inadvertent ransomware attacks. Tracking whether a partner user was still employed with the firm was also a burden. Given these challenges, GBBN began researching its options, focusing on their primary data requirements of global file locking, stability, and security.

KAST Construction, a Florida-based general contractor, was facing inefficiencies in their Virtual Design and Construction (VDC) and project team operations. Despite seeing VDC as a potential tool to enhance project quality through improved collaboration, cost reduction, and safety enhancement, the company was struggling with data management inefficiencies that hindered productivity. The VDC team operated almost independently of the projects they supported, leading to a lot of back and forth with busy project managers. If the VDC team lacked necessary information such as the current budget or schedule, they had to wait for responses from the project manager, slowing down the process and resulting in the loss of scheduled days. Furthermore, project teams who needed the designs, schedules, and budgets from the VDC team often couldn't find the information, leading to wasted time and money.

KPS4Parents, a nonprofit child and family advocacy organization, was grappling with the challenge of managing the extensive documentation involved in the Individualized Educational Program (IEP) for special needs students. The process was document-intensive, with a single case generating over 1,000 records from various stakeholders including parents, attorneys, educators, and regulatory investigators. The traditional methods of sharing these documents, such as faxing, emailing, or delivering hard copies, were not only cumbersome but also unsecured. The shift towards digital uploads of evidence by administrative hearing offices and courts further complicated the situation. Many parents were overwhelmed by the sea of documentation and the multitude of ways those documents were shared. The challenge was to find a secure, user-friendly platform that could handle the volume of documents and facilitate easy sharing among all stakeholders.

Nimbus Therapeutics, a biotech company, was grappling with the challenge of managing increasing volumes of data from multiple experiments and projects. The company's reliance on manual data management processes was proving unsustainable. The team had to validate the data, upload it into the corporate database, reconcile different data formats, and troubleshoot any issues. Human error at any of these steps threatened data integrity. Inefficiency was another challenge as the team had to create a new file or manually update and rename an existing one each time a document was modified. Lack of visibility and accountability was a problem as file update notifications were sent through email, which could easily get lost in crowded inboxes. Stakeholders were dispersed across time zones, which often slowed the process. Content sat for hours or days before the data team started their workday.

Explorium, a company that integrates organizations' data with the world's most reliable sources for predictive modeling and informed business decisions, was facing a challenge. The company was seeking to minimize data latency and free its data engineers from the task of building ELT pipelines. Explorium's platform determines the characteristics of the data and identifies potential enrichments it can make. However, the company was struggling with loading the right data quickly, regardless of the technical challenges it faced on the back end. The company was using Amazon EMR to run its ELT pipelines but realized its data engineers were spending too much time building these pipelines. This was slowing down the release of new data products and the onboarding of new data sets to its platform.

Fastned, a pioneer in the fast-charging infrastructure for electric vehicles (EVs), faced a significant challenge as the EV industry grew exponentially. The company started with a small footprint of charging stations and expanded as the industry took root. However, as the size of Fastned’s data grew exponentially, the company needed to migrate away from its legacy system, Redshift, which was resource intensive to scale and expensive for democratizing insights across its teams. Fastned was also faced with the pressures of building more charging stations and ensuring a continued superior user experience, which became increasingly challenging to deliver with its existing tech stack. The company's data team quickly realized that with an increase in data collection points across its network, the legacy AWS Redshift data warehouse would not be able to meet its growing needs. And while Tableau was being leveraged to deliver insights, wide-scale analytics was hindered by high costs.

Finda, a data-driven lending platform in Korea, was facing challenges in managing its data environment due to spikes in data volumes and an increase in data users. The company's complex data environment was made up of different analysis systems used for various analysis demands, making it difficult to extract data insights and value for its customers. Frequent application outages due to scalability issues limited its ability to respond to sudden increases in users or operational activity. The company also struggled with data engineering activities such as table creation, modification, and deletion in the service database, which was used for back-end services. This absorbed valuable resources and impacted SLAs. The core issue was Finda’s legacy data warehouse, which was inefficient in managing storage and resulted in runaway operating costs. The system also required constant maintenance to synchronize the data catalog on both storage environments.

Freshworks, a provider of CRM and customer experience solutions, was facing challenges in improving the performance of its customer support organization due to a legacy Hadoop infrastructure and an assortment of data tools. With over 60,000 enterprise customers and multiple product lines, the company was struggling to maintain exceptional customer satisfaction due to the high volume and level of support required. The manual approach to managing help desk tickets was not sufficient to keep up with the demand. The company's internal enterprise data platform, powered by Hadoop, was composed of multiple data and analytics tools, which incurred massive IT overhead to manage upgrades and monitor performance. This environment created performance bottlenecks as data volumes increased, slowing down the customer support team’s ability to efficiently service customers.

Grammarly, a company that provides AI-powered communication assistance, was facing challenges with its legacy, homegrown analytics system. As the company grew, it became increasingly difficult to evaluate large data sets quickly and cost-effectively. The existing system was time-intensive to learn, making it challenging to onboard new hires. It also failed to meet the needs of essential business functions, particularly marketing, sales, and customer success. Analysts often had to resort to copying and pasting data from spreadsheets as the system couldn't effectively ingest the external data needed to answer critical business questions. Reporting was also a challenge as the system didn't support Tableau dashboards. Furthermore, Grammarly sought to unify its data warehouses to scale and improve data storage and query capabilities. The existing setup, with large Amazon EMR clusters running 24/7, was driving up costs. Data silos emerged as different business areas implemented analytics tools individually, and a single streaming workflow made collaboration among teams challenging.

Funding Societies | Modalku, a licensed digital peer-to-peer (P2P) lending platform in South East Asia, faced several regulatory and compliance requirements that factored into its data strategy. As data becomes an increasingly valuable asset in the FinTech world, the company needed to ensure high-quality data and meaningful management information to identify and monitor risks and understand the performance of various business functions. The challenge was to implement a solid understanding of data governance to equip the organization with better decision-making capabilities, uniform data across the organization, increased data literacy, and improved regulatory compliance.

Funding Societies | Modalku, a licensed digital peer-to-peer (P2P) lending platform in South East Asia, faced several challenges related to data governance due to its regulatory and compliance requirements. The company recognized the increasing value of data and its potential to provide significant competitive advantages in the FinTech world. However, without high-quality data and upward reporting of meaningful management information, the company was unable to identify and monitor risks or understand the performance of various business functions. The company faced daily operational and regulatory challenges, including understanding and classifying data, applying flexible governance and security policies, and integrating across different applications. The company also needed to ensure data was organized, accessible, and compliant.

Nasdaq, the world's second-largest exchange, has been a data-driven company for over five decades. Despite having a decade of experience operating in AWS and moving the bulk of its critical workloads to the cloud, Nasdaq faced significant challenges. The trading system data was complex in size and structure, with as many as 140 billion events processed per day in the U.S. alone. The data was optimized for operational performance, not for analytics, making it difficult to manage. Additionally, Nasdaq's process for preparing and presenting data was outdated, with their legacy ETL tools unable to keep up with the scaling types of data and demand. The rigidity of these tools did not align with Nasdaq's ambitions. The data team was overwhelmed with maintaining the technical landscape and struggled to support their business partners effectively. This led to the emergence of parallel teams, each with a unique approach to creating data solutions, causing inefficiencies and confusion.

When Postman's data team expanded, they faced a significant challenge in managing and understanding their data. The data was scattered across different locations, and often, the same data in different places contradicted each other. As the company grew, the data system, which was initially simple and manageable, became complex and difficult to navigate. The data was stored in tables, and the information about these tables was only known to the early members of the data team. This system was not scalable and could not keep up with the company's exponential growth. The company's goal was to democratize data, making it accessible and understandable to everyone in the company. However, the lack of consistency and context around the data made it difficult for everyone to understand and trust the data. The data team was constantly bombarded with questions about data location and usage, and the loss of any team member would mean the loss of crucial data knowledge.

Postman, an API collaboration platform, experienced rapid growth, with its valuation reaching $5.6 billion and its user base expanding to over 17 million people from 500,000 companies globally. However, the company's data team was not growing at the same pace. In April 2020, the data team consisted of only six or seven people. Over the next year, the team expanded by 4-5x to 25 people. This rapid growth presented challenges in terms of onboarding new hires, handling requests from the rest of the company, and planning their work. The data team was also grappling with the decision between a centralized and decentralized team structure, with the former leading to conflicting data systems and metrics. Additionally, the team faced difficulties in prioritizing work and allocating projects fairly among team members.

Snapcommerce, a tech-savvy organization that operates in the travel, fintech, and goods verticals, faced a challenge as it scaled its operations. The company's employees, who are active users of its data platform and assets, needed a reliable source-of-truth documentation in a user-friendly format to support their ongoing requirement for self-serve tools. The company was looking for a way to standardize and share data definitions across the organization. They also wanted a solution that eliminated the need for coding by business stakeholders and provided quick navigational capabilities. The challenge was to find a data catalog that met their specific criteria, including an easy-to-navigate interface, strong search capability, an automated crawler, clear definitions/glossary section, permission handling, a table preview and SQL component, and data lineage visualizations.

TechStyle, a fashion retailer with a portfolio of five brands, faced a significant challenge in early 2020. The company, which has built its business model around embedding data across its operations, decided to overhaul its common systems and roll out a new data warehouse. This was a daunting task due to legacy backends, a relatively new team, and a sudden shift to remote work due to the COVID-19 pandemic. TechStyle uses a 'hub-and-spoke analytics model', where each brand has its own embedded Analytics Team, and the Data Platforms Team creates and manages common data systems. However, the company had been struggling with making data discoverable and understandable to everyone, not just long-time team members. The documentation for their systems was often limited or non-existent, and the growth of data sources that weren’t owned by TechStyle’s central data team added to the confusion and complexity. The challenge was further compounded when the company had to shift to remote work, disrupting the informal information flow that worked naturally in the office.

Tide, a UK-based digital bank with nearly 500,000 small business customers, faced a significant challenge in improving their compliance with GDPR’s Right to Erasure, also known as the “Right to be forgotten”. The bank's data and legal teams needed to define personally identifiable information and propagate those definitions across their data estate. The process of compliance was difficult and time-consuming, involving manual effort to find and delete data that persisted in secondary systems. Complicating this challenge was a lack of shared definitions of personal data, with differing opinions across organizations from Legal to IT. As Tide's technology stack and architecture grew more complicated, new products and services were introduced, and customers increased over time, the compliance process took only more time and effort. Automating this process became a priority.

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.

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.

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.

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.

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.

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.

Barnhill Contracting Company, a commercial building and infrastructure service provider, faced significant challenges in managing and accessing project documents. With an average of 90 active projects on Procore, the company needed a unified system for document storage and access. Seven years ago, the company initiated a strategic project to bring control and consistency to their file systems. The existing system was scattered, with documents stored in different locations, making it difficult for executives and project teams to locate the right files. The company also faced issues with File Transfer Protocol (FTP), with teams preferring to transfer data through traditional means such as burning it onto a CD. The company needed a solution that would allow teams to collaborate on large files internally and with external parties, and securely access files via mobile devices without the need for email applications. The company initially used a project management tool that did not integrate with Egnyte and did not meet all its needs, leading to confusion and duplication of documents.

MullenLowe, a global marketing and communications firm, faced a significant challenge in securely sharing, editing, and producing large numbers of design files with a range of clients. The firm needed to ensure the confidentiality and protection of client intellectual property, allowing access only to authorized personnel. In their search for an enterprise-wide file solution, the IT team considered several options, including Box, Dropbox, Google Drive, and SharePoint. However, these platforms were either lacking in standard data protection and controls, too complex to manage, or posed a risk of exposing client data through public links. The firm needed a solution that would provide visibility into file usage, refined auditing on anomalous activity, permissions, and access across sites and users.

J.B. Hunt, a leading American transportation and logistics company, was facing significant challenges in their quest to become the most efficient freight transportation network in North America. Their primary focus was on connecting carriers with their ideal shipper, considering factors such as price, weight, and location. However, their legacy architecture, lack of AI capabilities, and inability to securely handle big data were causing significant roadblocks. Their data was locked in legacy enterprise data warehouse (EDW) platforms, and their systems struggled to process and store the massive data generated by hundreds of thousands of equipment pieces. They also lacked the necessary levels of data security and the ability to support data streams generated by IoT sensors on their trucks and carriages. The company knew it was time for a change.

Swedbank, the largest banking group in Sweden and the third largest in the Nordics, was faced with the challenge of increasing operational costs due to its on-premise data stack. The existing solution was not only expensive to maintain but also lacked the flexibility to adapt to new business requirements. It replicated data across siloed platforms, had limited capabilities to separate storage from compute, and lacked support for deep learning and AI capabilities. As part of its digital transformation journey, Swedbank sought to unify disparate data sources into a single data lake, migrate analytical capabilities to the cloud, uncover deeper insights at scale, and accelerate time to market of data use cases. The bank's strategic vision was to build a resilient, scalable infrastructure to enable the widespread availability of advanced analytics while streamlining the analytics process to achieve operational efficiency.