BioStorage Technologies, a subsidiary of Brooks Life Science Systems, aimed to become the leading innovator of comprehensive sample lifecycle management solutions. They wanted to integrate clinical trial data, research data, biorepository data, biobank data, and laboratory data scattered across various locations globally. The data types, sources, and locations posed a significant challenge for effective data management. Before the ISIDOR platform, end consumers of biological samples had to engage in long, convoluted processes to gain insights from sample data and make decisions for therapy, research, or academic purposes. BioStorage wanted to consolidate all the data in the cloud for easy access from anywhere around the world. They also aimed to capture a wide-view of the sample to deliver more value to clinical trials by providing insights to disparate blended data sets.

Biogen Idec, a global biotechnology company, sells its drugs globally in over 90 countries through multiple wholesalers and distributors. Senior executives monitor drug sales and market share using daily sales reports by drug and territory. Business analysts at Biogen were manually collecting sales data from numerous internal and external sources, including wholesalers and distributors worldwide, in various formats like Excel, PDF, XML file transfer etc. They had to monitor the file versions, normalize formats and currencies, and consolidate sales figures with internal forecast and budget databases. The combined data was output into Excel reports for review by management. They also archived the data to support historical reporting. The process was cumbersome, time consuming and error prone. As the operations grew, Biogen was concerned about how to automate the generation of daily sales reports and improve accuracy.

Asurion's new digital home premium support service required strong predictive analytics, IoT capabilities, and big data architecture support, to be able to provide customers with the right experience. But Asurion's on-premises legacy data architecture could not support its global expansion or premium support service. To exceed customer expectations, Asurion needed a next-generation data architecture that could enable the company to spin up additional infrastructure, services, and products in weeks instead of months. Asurion also faced strict restrictions on migrating data, and had to remain compliant with stringent governmental regulations. As a part of this effort, Asurion needed to centralize companywide security management around a single point of control.

Indiana University (IU) was facing challenges in improving decision making at all levels within the university due to the lack of availability of timely, relevant, and accurate information. Data and its corresponding business logic were stored across multiple, siloed systems, making it extremely time consuming to gather and combine the relevant information decision makers needed. In some cases, data activities would fail entirely as required data elements could not be found and no common definition of sources of record were kept. Furthermore, the university's data integration toolset, primarily built around ETL processing, required broad skillsets and scarce resources to deploy, maintain, and manage. As a result, the development time needed for information access was long-so long, in fact, that by the time data was retrieved, it was often less useful or even irrelevant for decisions. In addition to the noted challenges of data and development timeliness, data security and privacy were also at risk within the traditional university reporting approach, as row-level access controls were integral only within the enterprise data warehouse (EDW). Other data sources and reporting environment offshoots (shadow systems) outside the EDW often lacked this same, fine-grained access control, thereby increasing the possibility of a compromise.

Digital Realty, a global data center solution provider, was experiencing significant growth and acquiring new companies. With these acquisitions came new systems that needed to be integrated with Digital Realty's enterprise data platform. The company was using ETL tools for data integration, but as the market and customer base grew, these tools were no longer sufficient. They required a broad range of specialized knowledge and were resistant to change. Digital Realty needed a flexible, adaptable data platform that could handle the company's evolving needs. They wanted to develop trust in enterprise data among all stakeholders, spend less time on manual data integration and governance and more time on data analysis, improve enterprise-wide financial planning and analysis, and deliver market segmentation and a 360° customer view for better customer targeting and value creation.

Swiss Re's internal business users needed a 360° view of the relevant data. In the past, the company had difficulty creating true 360° views, because Swiss Re was relying on a traditional architecture that was not delivering all of the relevant information to the data warehouse. Swiss Re therefore planned to implement an agile data integration platform to complement its traditional data warehouse. This would enable real-time data integration and make business decision-making faster and easier. Furthermore, the consuming applications would access the relevant data via the OData standard and provide easy data access for end users, portals, and applications.

Logitech's rapidly growing product line completely changed the nature of business reporting at the company. Business users needed to find answers to problems relating to price violations on retail sites, text mining and sentiment analysis of Logitech's products on social media and gaming websites, demand forecasting, sales channel management, and other domains. They were also challenged by fragmented analytics caused by data being trapped across multiple on-premises systems such as ERP, POS, DRM and MDM. In addition, Logitech had recently acquired a string of companies that added business verticals but data from those new verticals never got captured in the final enterprise-wide reporting, so top management lacked a full picture of the overall business.

Seacoast Bank, a growing community bank, faced a challenge with its operational data residing in a hosted data warehouse environment. There were many data silos that existed outside of the hosted platform and adding new sources of data or enriching the hosted data was not possible. The bank wanted to enhance the reporting experience for the departmental users. In the past, business users had to request custom static reports from the IT team, for operational purposes. These ad-hoc, manual reports used to get created as PDF or Excel files, making the reporting process extremely inefficient and outdated. Seacoast wanted its business users to interact directly with the data in a self-service manner, so they could create any type of custom report, based on the company's changing needs.

Drillinginfo's business growth drove the need for the company to build next generation products to support key O&G market segments. These products include applications to support well production and oil field services workflows, geo services for map analysis, a Geology, Geophysical and Engineering (GG&E) platform for interpretations and visualization, as well as a soon to be released mineral interest analysis application. Rapid time-to-market for these products and applications was crucial and this implied that the Data Tech team needed to deliver a data platform that supported the internal application development team quicker than they had been doing in the past. Also, rapid delivery of data directly to the customers was needed as well. However, the Data Tech team was challenged with integrating the data across the data warehouse, other data sources and providing it to the data consumers quickly. The product development team's delivery timelines were routinely at risk due to data availability and data consistency issues.

Autodesk, a leader in 3D design, engineering, and entertainment software, decided to transform its revenue model from a conventional perpetual licensing to a more modern subscription-based licensing model to increase profits and propel growth. However, Autodesk's existing Business Intelligence (BI) system could not support this critical change to the revenue model. The transition impacted the finance department's ability to track subscriptions, renewals, and payments, and the BI system, which included an operational data warehouse, could not meet the demands of the business stakeholders, who increasingly required both high quality and timely data. Autodesk quickly decided that an evolution to an agile BI 2.0 architecture was necessary with a logical data warehouse at its core.

Vizient's Sales Operations team needed various types of reporting such as opportunity reports, leads, as well as member-related reports. They also needed to perform analysis using BI tools on opportunity-to-outcome, member-to-product and lead time. Vizient's analysts provide reports on saving opportunities and initiatives to their member organizations on a regular basis. They also needed to know where to invest their time and resources - recruit new members or expand more products within the existing customer base. Vizient's sales team uses salesforce.com to generate these reports containing vital business information. Thirty-five analyst users needed immediate access to salesforce.com for reporting purposes. Vizient wanted to minimize Salesforce license costs, as some of the user licenses were used strictly to view the data. A new and cost-effective solution was needed to support the business information needs, with the flexibility to make the data accessible and available to any analytics or business intelligence tool used by the relevant business units.

Reintegra, a part of the Santander Group and a leader in collection management in Spain, was facing a significant challenge due to the economic crisis and a fall in consumer spend. The financial impact of delayed collections was growing, imposing a financial cost of over 0.5% on a company's invoicing. In addition, such delays were the cause of 25% of bankruptcies. With over 50% of invoices paid late, efficient collections management and debt recovery became crucial for the company's well-being. Reintegra needed to automate their search process for information regarding debtors, especially when trying to locate untraceable contacts. This task was accelerated by identifying valid telephone numbers and enabling direct contact. However, before the implementation of Denodo Data Virtualization, this task was carried out manually by Reintegra agents, resulting in significant resource investment, both in time and money.

The asset management firm was struggling with a growing mass of data from over seven different systems and external data feeds. This exponential growth in data and the wide variety of disparate data sources caused problems with the accuracy and integrity of reporting, adversely impacting the quality of customer service. The data integration team found it challenging to bring together meaningful information from inconsistent data. Providing timely and accurate data to customers is a vital part of the asset management process. To solve this problem, the application architecture team focused on developing a data services platform centered on Data Virtualization.

The profiled company is a leading global retailer that decided to extend its use of the digital domain by tapping into the wealth of competitor product and pricing data available on the web. It planned to collect this information and use it to drive its product and pricing strategy. However, the company faced challenges in acquiring and using competitive information due to the expensive and manual effort expended in extracting the data and keeping it updated, the incomplete and error-prone nature of manual acquisition, and the non-scalable nature of the extraction process that could not support company expansion.

The profiled company is a leading source of information, insight, and analytics in critical areas that shape today’s business landscape such as energy, economics, etc. However, as the demand for timely and accurate information in the energy industry grew, this company saw the need to redesign its data infrastructure as its existing setup posed challenges for its clients to easily wean valuable information from its databases, populated from a wide array of sources. Previously, this company provided information services to its customers using a database subscription model. Through this model, customers had to subscribe to entire databases; buying more data than necessary. Additionally, the company’s data architecture needed to be more flexible in order for them to fully exploit new revenue opportunities, scale its business and provide meaningful and enriched data to its customers. Considering the large amounts of data they managed, this company needed data virtualization.

Ultra Mobile experienced rapid growth, which required a quick evolution of its supporting IT systems. The company implemented a Hadoop-based data warehousing platform to accommodate new application data. However, the platform couldn't produce consumable data structures at the pace that business users required. This required skilled subject matter experts (SME) to manually assemble and cultivate key pieces of information, but this was not sustainable. The company lacked a holistic view of the organization’s data, which was needed for making tough optimization decisions.

The healthcare service provider decided to replace its main patient information system with a completely new system. This was going to be an expensive project that could span a number of years. As part of this initiative, the company also decided to modernize its data infrastructure built around an enterprise data warehouse. It took the company’s IT operations team a significant amount of time to implement changes to this infrastructure, which impeded the company’s overall agility as well as its ability to test new functionality. In addition, it was a batch-oriented infrastructure that furnished nightly reports. The company needed a solution that would enable the new infrastructure to seamlessly support both the dimensionally modeled enterprise data warehouse and the new transactional system, enabling both to work in tandem.

The British wealth management firm was facing challenges in complying with rapidly changing EU and UK regulations related to the financial market. The company's data landscape was fractured with an on-premises legacy data warehouse, a substantial presence on Amazon Web Services (AWS) Cloud, and other apps in the cloud, such as Power BI, Salesforce, and Snowflake Cloud Data Warehouse. The company needed to prepare for the Markets in Financial Instruments Directive (MiFID) II, which calls for increased cost transparency, improved record keeping for transactions, and added protection for investors. The company also realized that many of the data sources were separated into silos, which meant that many reports required time-consuming data integration efforts, increasing development time.

The risk management department of the reinsurance giant struggled with an increasing number of regulatory requirements, which was reflected in longer lead times and lower customer satisfaction. The department needed a holistic view across all sub-areas of risk in the life insurance, property insurance, and investment lines of business. Over time, the company's traditional data warehouse architecture began to face challenges, particularly in handling the high frequency of new regulatory requirements. The limitations of the company's data warehouse were demonstrated by the long lead times required for every change. The company decided to move its complete data infrastructure, which had previously been hosted on-premises, to the cloud. The new architecture had to provide faster access to data, improved availability, and greater flexibility.

TAM had a fragmented data architecture, with data trapped in different business silos. The company relied primarily on extract, transform, and load (ETL) processes to integrate data from its enterprise data warehouse and transactional databases on SQL Server. This process was extremely slow, manual in nature, and difficult to govern. Moreover, the ETL sessions increased the overall maintenance cost of the data architecture as data was replicated across layers. In addition, multiple operational teams within TAM performed their own analytics and generated business reports. In the absence of an enterprise semantic layer, this led to multiple connections to different data sources, complicating the data architecture, and making maintenance even more difficult. Data latency was also a challenge, as business users did not always receive the most recent data. These issues resulted in multiple inaccurate definitions of core business metrics. There was no single version of the truth, and top management did not get the most accurate picture of the business. All of these challenges overburdened the IT team and slowed down the adoption of new BI solutions.

The company recently decided to migrate its on-premises data infrastructure to the cloud to enable a wider variety of more powerful, cloud-enabled applications while taking advantage of its flexibility, agility, and reduced TCO. However, in doing so, the business intelligence consultant at the company knew that the company would face several predictable challenges: Not all of the company's applications could be simply ported to the cloud, as some rely on authentication procedures that may have been developed for the on-premises environment. Also, because some data centers are farther away than others, geographically, they could introduce unexpected latency into the migration process. Besides, the company needed to move and transform terabytes of data, and the company's extract, transform, and load (ETL) processes would take a prohibitively long time to run. The energy company wanted the flexibility to change application providers during the migration, while also migrating to the cloud in a phased, controlled manner, due to the complexity of some of its applications. The company needed a way to accelerate the migration process while also gaining the flexibility to migrate at its own pace and make changes during the migration without impacting users.

The biopharma company was experiencing rapid growth and was looking for ways to leverage data to improve sales, patient engagement, and treatment. However, the company's extract, transform, and load (ETL) processes required a prohibitive amount of development time, especially at the transformation stage. This made it challenging and time-consuming for stakeholders to access data from the different sources, which included a Netezza data warehouse, a CRM in the cloud, google analytics, and other 3rd-party data sources. The company realized that it needed a real-time, 360-degree omnichannel view of patient data across the different sources. In addition, the company was interested in enabling its human resources department to gather data from SuccessFactors and run its own reports on diversity and resource allocation, but the data infrastructure could not support this. The company also felt challenged in its ability to easily manage the lifecycle of documents for regulatory reporting.

AXA XL's data management architecture was extremely complex, with multiple operational source systems and multiple stakeholders from different business groups using their own BI tools to access data. The company relied on ETL processes to integrate the data, leading to excessive replication. This resulted in latencies in data delivery and inconsistencies between different data sets, creating multiple versions of the truth. Outdated or unreliable figures were reported to stakeholders. There was also a lack of data access control, with no way to trace who accessed what data, or when. Without role-based access rules, anyone could access any data, regardless of whether or not they had the authority. This made the entire data management architecture extremely vulnerable to security breaches and exposed the company to the risk of falling out of GDPR compliance.

Prologis, a global real estate asset management company, was struggling with its data management system. The company's data was stored in a variety of languages and dispersed across different geographical locations. The existing system was an on-premises data warehouse comprised of 27 servers supporting a series of databases, integration servers, and reporting servers. The system was becoming outdated and inefficient, with ETL scripts needing to be rewritten and re-tested whenever there was a change in the source environment. Migrations were all-day events that required the entire team, and with every test, a complete regression test of the entire stack was needed to ensure that nothing broke. Prologis wanted to modernize its data infrastructure to include cloud capabilities and introduce efficiencies that would accelerate analytics, without causing undue downtime.

Festo, a leading supplier of automation technology and technical education, was looking to optimize operational efficiency, automate manufacturing processes, and deliver on-demand services to its business consumers. This included finding smarter ways to streamline how the company aggregates and analyzes data. Festo also needed its business users to become self-sufficient with reporting and analysis and reduce their reliance on IT for preparing and surfacing the data they need. In addition, Festo's business teams had launched strategic projects to maximize energy efficiency, and they needed to be able to provide instant visibility on energy usage directly to the shop floor teams. However, Festo was challenged in finding an agile and robust way to integrate the data from the existing silos, which included the data warehouse, machine data sources, and other sources, in a way that would reduce the reliance on IT by the business users while providing the quick turnaround and flexibility that the users were demanding.

GetSmarter, a digital education company, was experiencing rapid growth due to the popularity of its university accredited online courses. As the company's customer base and operations grew, so did its data repositories, which contained a variety of functional data covering marketing, finance, courses, students, and many other domains. With data spread across so many heterogeneous systems, business users could not perform a unified analysis of the enterprise data or achieve a single version of the truth. The company's reporting tools now needed to talk to many databases instead of just one. GetSmarter needed a way to accommodate the many-to-many reporting tool and avoid having to manage multiple database connections.

The Company, being one of the largest multinational companies, with offices, data centers, and fabrication facilities all over the world, developed a heterogeneous ecosystem of tools and technologies over time, giving rise to a complex, distributed data ecosystem. As SaaS applications became mainstream, SaaS adoption within The Company skyrocketed; data oriented nomenclature grew inconsistent across business units, and the office of the CIO was under tremendous pressure to deliver business-friendly, consistent information with the lowest total cost of ownership (TCO), as well as products and services with enterprise grade security and privacy, and the fastest time-to-market (TTM). The physical EDW fell short of its promise. The Company also wanted fast, post-M&A data integration, distribute new selfservice entitlements to downstream acquisition applications, and distribute directory identities to the acquisition directory. The Company also wanted to architect its enterprise data access layer for a single point of entry for HR and supplier data consumption, seamless support for data source migration, and scalable interaction among on-premises and cloud data sources. As its IT culture was not historically suited for reusable information, The Company experienced and egregious misuse of resource time and effort. As challenges became overwhelming, The Company searched for an agile data access solution.

The financial services company, after crossing the $50 billion threshold in assets due to the acquisition of a retail bank, became a systemically important financial institution. This subjected them to stringent regulatory oversight. To meet compliance requirements, the company needed a controlled data environment to enable intercompany data transfers with a complete understanding of lineage from source to destination. In the legacy architecture, consumers were pulling data from the upstream systems directly instead of going through the common data access layer. As a result, information that was modified along the way may not tie across to the various silos. The company also needed a smart data governance initiative to avoid the garbage-in-garbage-out problem.

The Company, a leading construction equipment manufacturer, was facing challenges due to sluggish sales and competition from low-cost alternatives. The Company's customers were demanding high returns on their investments with minimum downtime and maintenance. To meet these demands, The Company needed to optimize asset performance and reduce machinery part breakdown in the field. The Company had invested in modern tools and technologies for telematics and predictive analytics, and in field sensors and big data technology. However, the field equipment data needed to be analyzed constantly in real time against the backdrop of service life records, warranty data, and other information. Traditional data integration methods were proving to be slow and expensive. The Company needed an agile data integration and access layer that could easily integrate big data with other sources of enterprise or cloud data in real time.

ABN AMRO Verzekeringen, a joint venture of NN Group and ABN AMRO Bank, was struggling with its classic data warehouse architecture. The company had a growing need to become data-driven, but the existing data warehouse was increasingly unable to meet this demand. The company had to report to various stakeholders, including internal business units and the two organizations of the joint venture, as well as various regulators. The demand for reliable, frequent, and up-to-date data had significantly increased within the organization. The existing data warehouse made it difficult to combine information from different source systems and create up-to-date reports. The business units only received monthly updates on the progress of campaigns or the quality of services. The final push for change came at the end of 2018 when it became clear that support for the tooling and database of the current data warehouse would no longer be provided in the foreseeable future.