Boosting team productivity and decision-making with GenAI
HP uses Databricks Mosaic AI to accelerate the effective delivery of business and customer value from big data
Cost savings compared with AWS Redshift
To build a GenAI assistant chatbot for data teams
HP, a global leader in computing and printing solutions, supports the functions of over 200 million printers worldwide. However, manual SQL queries and cross-referencing solutions slowed down service teams, often requiring assistance from data scientists for troubleshooting and real-time insights. These manual, time-consuming processes drove up costs and disrupted productivity. To address these challenges, HP migrated from their legacy data warehouse to the Databricks Data Intelligence Platform. With Databricks Mosaic AI, they’ve built an AI agent system that securely powers a knowledge base chatbot. They also use AI/BI Genie to simplify the creation of intelligent dashboards, making it easier for business leaders and support teams to get the information they need to make smarter decisions.
Slowed by costly and frustrating manual processes
Throughout their 80 years in business, HP has been at the forefront of innovation. Within their Big Data Platform and Solutions organization, this team provides data ingestion, platform support and customer data products for all HP business units. However, due to their large volume of data from PCs, printers and web and mobile apps, and the sheer number of partners and customers on the platform, there is a significant burden and bottleneck for nontechnical users to discover, access and gain insights from the data. As a result, making high-quality data available to thousands of data users on their data platform required manual interventions that significantly slowed productivity, added latency and contributed to overall costs.
HP’s data engineering teams, who were used to being regularly bombarded with questions about specific data models and issues, were overwhelmed with inquiries and troubleshooting requests. Whether users needed access to restricted data or wondered how to use platform features or new employee onboarding, the teams were constantly distracted by customer support requests. William Ma, Data Science Manager at HP, explained, “With all those inquiries coming in, our teams were spending 20–30% of their time trying to whip up SQL queries, dig into the data and cross-reference in different areas to figure out what was going on. With a five-person team, this equates to an additional full-time employee.”
Data teams were also required to help build usage and share cost and budget analysis dashboards used by HP’s leadership for strategic decision-making. To produce these insights, teams had to access and explore data manually. Additionally, data teams didn’t always have the bandwidth to support incoming requests, creating latency that impeded effective decision-making in real time. William stated, “When you need to move your data out of the warehouse to a workspace where you can apply AI tools, you typically need to make copies of the data. There are additional security and privacy governance concerns, such as DSRs, which means you must delete data when customers send in their DSR requests. All those complexities add up to tying up data teams and driving up costs.”
With the desire to move fast and reduce the load on their engineering team, HP looked to leverage generative AI (GenAI) to alleviate the data barriers to innovation and intelligent decision-making.
Data team enablement with Databricks
HP began using the Databricks Data Intelligence Platform on AWS while moving from their data center, leveraging a lakehouse architecture to unify data, analytics and AI. William elaborated, “Databricks has a data-centric approach that brings AI, analytics and the GenAI tools we need on a single platform on top of the lakehouse architecture. Serverless Databricks SQL allows us to provision ephemeral compute clusters on demand, and Unity Catalog is used for data management and governance, making it very easy to control fine-grained access for our 600+ Databricks users and enabling them to access information via a knowledge base chatbot. Unity Catalog also provides data lineage and auditing capabilities. We don’t have to reinvent the wheel and everything we need to build data and GenAI solutions comes with it. It’s all built in the Databricks Platform, and we just have to leverage its framework and tools.”
HP migrated from AWS Redshift to Databricks SQL for better performance/cost ratio, resource isolation, attribution and unified data cataloging, access control and governance. Using the Mosaic AI Playground, HP experimented with different large language models (LLMs) — ultimately selecting DBRX as the most appropriate and cost-effective model for their chatbot needs. With GenAI at the forefront of the data team’s ambitions, they quickly implemented their first use case to help onboard their data users. Information was scattered among internal wiki pages, SharePoint files and team support channels, making it difficult for partners to find answers and get started quickly. To disseminate and manage the knowledge more efficiently, HP implemented a retrieval augmented generation (RAG) solution with a Vector Search database back end for sourcing all relevant information.
Using Databricks Mosaic AI, an intern on the data team quickly developed a functional assistant chatbot that provided accurate answers in real time. William explained, “This solution has quite a few pieces: a web front end and a back-end agent that essentially parses the input from the user, searches and grabs relevant data, then sends it to the GenAI endpoint to generate answers. Another component is a web crawler that crawls and tokenizes the internal information on various sites and gets them populated into the Vector Search database. The college intern implemented the end-to-end solution in less than three weeks on Databricks Mosaic AI, which is amazingly fast compared with other teams who spent months building similar solutions on other platforms with experienced staff engineers.” The Vector Search database entries and answers generated also contain URLs for reference so users have the opportunity to validate or explore further.
Expanding GenAI for self-service insights
Since moving to the Databricks Data Intelligence Platform, HP has seen significant operational cost savings compared with their AWS Redshift warehouse, to the tune of 20–30% lower costs. In addition, the platform enabled their data teams to easily construct self-service insights for partners and business leadership. By implementing an AI agent system to power their GenAI chatbot, HP is forecasting significant productivity gains within data teams where practitioners are less frequently needed for manual partner support.
Continuing their use of GenAI solutions, HP is also exploring AI/BI Genie to add efficiency to service teams, building workspaces where teams can share established Genie workspaces utilizing pre-canned queries for frequently asked questions. This enables nontechnical users to query the data in natural language and receive reliable responses immediately. William explained, “AI/BI Genie can play a significant role in reducing some of the manual SQL support required from our data engineers today, by providing the ability to converse with the data in natural language and create data visualization easily and nicely. I’m quite amazed at how good Genie has become and how fast it evolves.”
Moving forward, HP is working to expand the use of GenAI solutions to their customers to improve the troubleshooting and query resolution process. As data teams work toward solving more challenges with GenAI, the Databricks Data Intelligence Platform will remain the foundation. William concluded, “I’ve been working with Databricks for eight years, and this platform keeps improving. It gives us high confidence as it continues to evolve and get better. This is a great collaborative partnership to continue building solutions to help us better support our teams, partners and customers.”