HP 在扩展业务和发展合作伙伴生态系统方面遇到了无效的 BI 工具集。 BI 团队管理传统的 OLAP 多维数据集、定制的 .NET 用户界面和数百个离线报告。系统需要 24-48 小时来刷新数据，甚至更长时间来分析数据。新数据部署耗时三个月，使得数据在准备好使用时就已经过时了。 BI 团队是一个瓶颈，他们在数据分析请求上花费了太多时间，而不是专注于更具战略意义的计划。该团队将数据下载到离线仪表板和报告中，并在整个组织的 Excel 和 PowerPoint 文档中分发。最终用户需要一个自助服务解决方案。

平均而言，一个 AI 模型的开发需要一个 IT 团队 12 到 18 个月，这意味着数据科学家 80% 的时间都花在了查找、清理和重组数据上。 Accern 的无代码 AI 允许用户轻松部署和定制预训练的金融服务模型，帮助从大量非结构化数据（例如新闻文章或财务文件）中比以前更准确、更有效地提取洞察力。然而，在为客户提供数据的过程中，Accern 发现自己在为客户提供的定制方面受到限制。他们无法自助访问数据可视化，只能使用提供给他们的单一仪表板。

The lending services team at a Fortune 500 financial services company faced significant challenges in accurately pricing securities due to data silos and outdated systems. They managed thousands of stock transactions daily, requiring precise price estimates for each transaction. Traditionally, this involved mining through thousands of stock loan transactions and data from other institutions, which was time-consuming and inefficient. The team had access to real-time data from 18 different sources, but each source was accessed separately through proprietary applications, creating a bottleneck. With only one data analyst using a legacy BI tool, the process was slow and inflexible, limiting the traders' ability to make informed decisions and costing them revenue opportunities.

The pharmaceutical industry is facing significant challenges due to increased customer expectations, decreasing insurance coverage, and heightened regulations. These factors are creating obstacles for companies trying to bring successful products to market. Additionally, the explosion of data generated by new technologies presents both opportunities and challenges. In the drug discovery and development phase, which can take years, scientists need timely access to comprehensive information to determine the efficacy of a drug and decide whether it should proceed to production. However, accessing the right information was a manual and time-consuming process, as data was stored in siloed areas and could only be accessed via SQL. This led to the BI team being overwhelmed with requests, sometimes taking up to three months to fulfill.

Franchising is a highly competitive sector in the retail industry. A Fortune 500 electronics retailer, with over 20 years of establishment, has expanded to support a nationwide network of 800+ franchise stores. To maintain competitiveness amidst rapid growth, the retailer aimed to provide franchises with access to daily sales, marketing, and peer benchmarking data. However, the existing Microsoft Sharepoint analytics portal was outdated, difficult to use, and provided limited data views, leading to a poor user experience for store managers. The BI team faced challenges in maintaining 30+ data cubes, which was both costly and time-intensive. Despite these efforts, franchise owners lacked the necessary visibility and often relied on gut instincts for business decisions. The retailer needed a scalable, user-friendly analytics solution that would be less resource-intensive for internal teams.

For a global semiconductor manufacturer, the challenge was to streamline engineering operations to build higher-performing products, reduce operational costs, and outpace competitors. The engineering teams were using multiple BI tools like Cognos, Oracle OBIEE, and QlikView to gain visibility into product and operations data. However, these tools were too complex for engineers, leading to a heavy reliance on the BI team to set up data and produce reports. This dependency created a bottleneck, slowing down production across product lines as the overworked BI team struggled to deliver data quickly enough.

HP faced significant challenges in scaling its business intelligence (BI) and analytics capabilities. The company's partner ecosystem, which generates 80% of its revenue, required extensive data exchanges for product tracking and inventory management. However, HP's existing BI toolset was ineffective for scaling and managing this data. The BI team relied on a traditional collection of OLAP cubes, a custom-built .NET user interface, and numerous offline reports. This system was slow, taking 24–48 hours to refresh data and even longer for analysis. New data deployments took three months, rendering the data obsolete by the time it was ready for use. The BI team became a bottleneck, spending excessive time on data analysis requests instead of strategic initiatives. End users needed a self-service solution to access and analyze data efficiently.

At PCM, account executives manage 60-120 new loans per month. Getting visibility to the status of those loans at the individual account exec. level all the way up to the leadership team is critical to the success of the company. PCM had a vast amount of complex data about their mortgage services stored in a data warehouse that could only be accessed by a technical IT team. As a result, PCM’s 20 account executives had to spend 2-3 hours a day just to get daily updates on the status of the outstanding mortgages they manage. Additionally, they lacked a holistic view of each customer, and were missing out on opportunities to acquire new customers or provide additional products and services to existing ones. Even doing something as simple as reporting on daily KPIs for the CEO was a challenge.

At a Fortune 100 Mass Retailer, merchandise managers were struggling to analyze critical data due to the limitations of their legacy Business Intelligence (BI) tool, Tableau. The system was unable to handle the volume of data and frequent ad-hoc requests, leading to constant timeouts and a backlog for the BI team. As a result, merchandise managers had to spend hours manually building pivot tables in Excel to understand daily performance across product lines. This manual process limited their ability to manage all products effectively, causing them to miss opportunities to improve product margins and meet customer needs.

In early 2021, factory14's tech teams were building a data warehouse while brand management and operations teams struggled with over 40 Excel spreadsheets. This made it difficult to make quick business decisions or react swiftly to issues, and they were unable to restock products in a timely manner. The company needed a single source of truth to provide effective calibration and key insights across all brands, product categories, and markets. The existing system was cumbersome, and the need for a more unified and efficient solution was evident.

The UK travel industry is facing numerous challenges, including competition from Airbnb, the uncertainties of Brexit, and growing security threats. Travel Management Companies (TMCs) like Clarity are under pressure to rationalize legacy BI and analytics software to stay competitive. When Clarity merged with Portman Travel in 2017, they aimed to leverage both companies' market-leading technology, including BI and analytics software. However, integrating these systems posed a significant challenge. Darren Williams, Clarity's Head of Management Information (MI) and Data, wanted to provide joint customers with a visionary and competitive service. With multiple systems already in place, Darren was hesitant to introduce another analytics platform. However, Assimil8, Portman's technology partner, recommended ThoughtSpot and proposed a new system called Go2Insight to integrate Clarity's MI tools.

Tri-Vin Imports faced significant challenges with its existing cloud-based ERP system, which was unable to provide timely and accurate business intelligence (BI) reports. Despite the system's ability to collect data, the process of generating useful insights was cumbersome and time-consuming, often taking up to 16 hours a week for sales management to run customized reports. The data was frequently outdated by the time it was received, and the ERP's reporting capabilities were limited, failing to meet the company's needs for effective sales management. This inefficiency hindered Tri-Vin's ability to make informed decisions and optimize its sales processes.

To keep up with the constant barrage of IT and security tickets, Bedi and his team rely heavily on ServiceNow. They manage all of their tickets, end-user communications, change management, and CMDB initiatives through the ServiceNow platform, generating a tremendous amount of data in the process. However, without the right analytics capabilities, making the right operational decisions and improvements based on this data is challenging at best. To support the growing demands of the business, Bedi and his team needed more than ServiceNow’s canned reports and predefined drill paths. Snowflake had recently onboarded more than a thousand employees during the course of the pandemic. And Bedi knew he could either bring on additional resources to help manage the mounting number of devices, IT requests, and help tickets or he could find a solution to transform the way his team engaged with and acted on data.

Langs Building Supplies faced a significant challenge with their outdated ERP tool, which had been in use for nearly 20 years. The tool no longer met the company's current needs or values, prompting a complete reevaluation of their legacy reporting and BI tools. The company needed an analytics solution that was API-driven, AI-aware, and capable of providing quick insights to important business questions. The goal was to empower business users with a tool that could model and search live data intuitively, without relying on static reports or dashboards that only looked backward at data.

T-Mobile Netherlands faced challenges in maintaining its market leadership due to the constant battle for customer loyalty and the increasing complexity of customer data. The company had been using BI tools like Qlik and Tableau, but the recent growth and explosion of customer data made it difficult to keep up with the demand for insights. The existing solutions were taking too long to deliver essential customer data to frontline teams, leading to delays in problem-solving and dealmaking. The company needed a solution that would allow business users to access relevant data quickly and independently, without relying heavily on IT and data experts.