HP's partner ecosystem, which generates 80% of its revenue, involves data exchanges with thousands of partners for product tracking, sell-through inventory, and more. As business models evolved, more data and data sources needed to be connected to HP’s data platform. However, HP struggled with an ineffective BI toolset for scaling its business and growing its partner ecosystem. The BI team managed a traditional collection of OLAP cubes, a custom-built .NET user interface, and hundreds of offline reports. The system took 24–48 hours to refresh data and even longer to analyze that data. New data deployments took three months, making the data obsolete by the time it was ready for use. The BI team was a bottleneck, spending too much time on data analysis requests rather than focusing on more-strategic initiatives. The team downloaded data into offline dashboards and reports and distributed it in Excel and PowerPoint documents throughout the organization. End users needed a self-service solution.

Accern, a firm that believes in the power of data and AI, was facing a significant challenge. The development of an AI model typically takes an IT team 12 to 18 months, with 80% of a data scientist’s time spent on finding, cleaning, and reorganizing data. Accern's no-code AI allows users to deploy and customize pre-trained financial services models to extract insights from a vast amount of unstructured data more accurately and efficiently. However, Accern found themselves limited in the customization they could offer customers. They lacked self-service access to data visualizations and were restricted to the single dashboard provided to them. This limitation was hindering their mission to empower customers with data and was a barrier to their growth and customer satisfaction.