AAA of Northern CA, NV, and UT recently reorganized its operations to create a for-profit Insurance division and a not-for-profit Auto division. While the Auto Club put in place its data center, integration platforms and applications, it shared IT resources with the Insurance division so as to keep its operational applications, like reporting tools, call centers working as usual. The company was looking for a way to create an abstraction layer that deployed quickly and continued to feed the operational applications while migration continued under the hood. From a strategic perspective, as they were building their IT infrastructure from the ground up, AAA wanted to infuse flexibility and agility into the architecture that will allow them to introduce future changes at the source, target or middleware layer without disrupting existing workflows.

Telefonica, one of the largest telecommunication operators in the world, was facing challenges in managing key performance metrics and reporting across multiple, complex IT and network management systems. Each of these systems had their own dashboards and reports, and it took a team of 20 people to retrieve and consolidate the information and produce reports. The time to produce a report was typically 1 week and changes in sources or management requirements made the task quite difficult. Moreover, there was a complete lack of real-time views of the key operational metrics across systems. In a highly dynamic environment where projects, priorities and problems were changing all the time, IT management was hampered in decision making regarding systems and applications and taking rapid countermeasures to prevent poor service, delayed resolution, or efficient actions to advance priority projects. The system they had was rigid, fragmented and unable to support the integrated ITIL operating and performance management needs of Telefonica's IT management.

The Phone House, a global telecommunications retailer, faced challenges in efficiently managing customer phone activations or migrations. The process required manual operations to synchronize data with service provider systems over the extranets, leading to errors and inefficiencies. There was no direct access to the telecommunication operators systems nor any standards or interfaces for automatic information exchange and synchronization. The company aimed to improve its B2B business model across multiple partners and create an automatic synchronization of their invoicing system with the operator´s activation systems for more agility, efficiency, and transparency.

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.

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 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.

Enverus’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. As a result, the developers were directly accessing the data sources and in other cases suffering from severe delivery delays. To solve this issue, one option was to use conventional ETL (extract, transform, and load), but that would take several weeks. A more timely way of meeting the needs of the product development team was needed.

City Furniture, an online retailer with a chain of physical stores in Florida, started out as a small family business that has grown to reach annual sales of $1 billion. While the company has been experiencing rapid growth, it encountered challenges with slow, inefficient data management technologies. The company’s extract, transform, and load (ETL) processes had high resource requirements and were slow to implement, so they impeded many important data initiatives. In addition, City Furniture’s operational and sales data systems were decentralized from the data in the transactional systems. This made it difficult to perform advanced analytics and understand operational issues such as scheduling orders, delivery delays, and returns. To grow, City Furniture needed advanced analytics to gain more visibility about deliveries, scheduling, and routing orders, and to better prepare the sales team to deliver orders. The company needed a centralized data source to provide accurate data to different teams such as sales, marketing, supply chain, and business operations, to help them make faster decisions.

The data landscape at Hastings Deering was complex with data being generated through multiple enterprise resource planning (ERP) systems, which needed to be integrated with several Caterpillar systems. Numerous data silos across the business had challenges with respect to consistency and accuracy, directly affecting trust in the data. Traditional data delivery methods and methodologies were delaying time-to-data and time-to-value. Additionally, Hastings Deering also began to see a shift in business requests, away from traditional data requests to data sharing requests to assist with quick insights. The company’s vision, and a key part of its data strategy, was to enable “data citizenship” through the establishment of a data and analytics center of practice.

Nedbank 的旧数据仓库面临挑战，该数据仓库每天加载 6 TB 的数据，给数据消费者带来了复杂性。

作为一家地理分散的大型金融服务组织，科法斯面临着监管和合规挑战，希望通过创新提高运营效率并刺激业务增长。然而，整合内部和外部来源的数据很复杂。

在 Estes Express Lines，数据存储在多个数据孤岛中，带来了重大挑战：IT 需要在将数据投入生产之前对数据进行物理移动；时间、金钱和资源花费在冗余的硬件和代码上；数据安全由不同团队分别负责；业务用户依赖 IT 来提供数据请求，这影响了敏捷性。

Gebrüder Weiss 是一家国际运输和物流公司，在整合不同来源的数据以进行背景分析和流程优化方面面临着挑战。

IDX 希望使其会员和订阅者能够更轻松地接收市场数据，同时更加注重实时提供可信数据。

Insiel 希望通过整合大量数据和信息并将其提供给相关利益相关者来提高其价值。然而，他们在整合不同来源、不同格式和安全协议的数据时遇到了困难。

VHA'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. VHA'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. VHA'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. VHA 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.

JAZZTEL, a leading telecommunications and data transmission carrier in Spain, was facing a challenge in differentiating themselves from their competitors by providing the highest possible level of service. Their contact center, equipped with a CTI solution, required the agents to consult and utilize many different systems (CRM, Incident Management Systems, Network Management Systems, Diagnostic Systems, etc.) causing a lot of complexity in completing tasks. The company decided to find a software tool that would integrate with the CTI and provide their agents all the information they needed during a call. They aimed to optimize their customer service, improve the quality of customer service, increase Call Center productivity, have a flexible and dynamic platform which could quickly adapt to changes in the business and IT infrastructure, reduce the number of applications that needed to be accessed by the agents, and reduce the number of requests directed toward the back office.

The National Institutes of Health (NIH) faced significant obstacles in reliably and efficiently moving large volumes of cancer genome data from The Cancer Genome Atlas (TCGA) to the International Cancer Genome Consortium (ICGC). This process involved transforming the TCGA data to meet ICGC format requirements and then periodically uploading the data into ICGC servers. The transformation was initially accomplished using PERL scripts, but NIH faced challenges with this process. It was not scalable, had high costs, and was inaccurate due to limited connectivity to data sources leading to redundant copies of data, slower processes and greater chance of errors.

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.

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.

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.

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.

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 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.

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.