ThoughtSpot > Case Studies > HP Democratizes Access to AI-Driven Business Insights Using Snowflake and ThoughtSpot

HP Democratizes Access to AI-Driven Business Insights Using Snowflake and ThoughtSpot

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Technology Category
  • Cybersecurity & Privacy - Database Security
  • Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
  • Equipment & Machinery
Applicable Functions
  • Maintenance
  • Quality Assurance
Use Cases
  • Inventory Management
  • Time Sensitive Networking
The Customer

Hewlett-Packard

About The Customer
HP’s product and service portfolio includes personal systems, printers, and 3D printing solutions. Its vision is to create technology that makes life better for every person, every organization, and every community around the globe. HP's partner ecosystem generates 80% of its revenue. Its sales process involves data exchanges with thousands of partners for product tracking, sell-through inventory, and more. The company struggled with scaling its business and growing its partner ecosystem due to an ineffective BI toolset.
The Challenge
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.
The Solution
HP selected Snowflake for data warehousing and data sharing workloads and ThoughtSpot for search and AI-driven analytics. With ThoughtSpot, anyone can use simple natural language to create new data-driven insights or surface insights generated by others across the entire enterprise. By leveraging AI, ThoughtSpot goes beyond answering known questions, detecting trends, and identifying anomalies and patterns, to suggesting new questions users wouldn’t think to ask. HP ingests data into Snowflake and then uses Embrace to gain seamless access to that data from ThoughtSpot. HP began by ingesting compliance and supplies management data into Snowflake, which became immediately available in ThoughtSpot. This was a massive amount of data, as it tracked toner and ink units sold anywhere in the world. Next, the team ingested serial number data, which tracks products in use anywhere in the world. Snowflake and ThoughtSpot are now used for pricing and bid desk analytics, supplies management, partner risk management, customer performance management, and channel performance and profitability.
Operational Impact
  • The implementation of Snowflake and ThoughtSpot has resulted in speed, flexibility, and self-service for internal end users. Slow reports and bottlenecks for analytics requests are now a thing of the past. HP’s users now have access to a self-service analytics platform that’s fast and flexible and provides automated insights discovery. HP partners have also benefited from the new system. With Snowflake, HP accelerated the way it refreshes and processes data. Partners now submit data by the end of their business day and obtain data back from HP the following day. The result is time savings and a more efficient process for HP’s partners. HP is now looking forward to democratizing data access for everyone in the organization.
Quantitative Benefit
  • 350 users have been onboarded to the new system
  • Over a six-month period, 155,000 queries were generated
  • Data refresh and processing time significantly reduced

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