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This leading logistics real estate provider and business builder transformed its data lake with an intuitive interface that lets users ask open-ended questions and receive instant answers. GLP operates an enterprise data lake (EDL) with downstream reporting tools and needed to enhance workforce productivity and minimize the data management team's workload when creating new reports and conducting analyses. GLP is a leading logistics real estate provider and business builder specializing in logistics, real estate, data centers, infrastructure, renewable energy, finance and related technologies. With an extensive network of smart warehouses, GLP serves a myriad of logistics service providers, manufacturers, retailers and  [[http://www.zhongying.club/sergioresch405/8417352/wiki/Written-in-Rust-with-12-Dependencies|MedicGLP Online]] e-commerce giants. Through careful incubation of new business ventures and efficient management of its assets, GLP maintains an inventive path toward growth and value creation. The firm is always on the lookout for  [[https://bbclinic-kr.com:443/nose/nation/bbs/board.php?bo_table=E05_4&wr_id=1113036|MedicGLP Online]] novel, tech-driven strategies to enhance its operations and  [[https://ewuswiki.club:443/index.php?title=Resistant_Starch_Diet|weight management supplement]] provide superior service to its partners and clients.



"We put digital technology, data and innovation at the center of how we run our business," explains Miao Song, Chief Information Officer of GLP. GLP operated a robust Enterprise Data Lake (EDL), but the output data wasn't particularly user-friendly. This posed a notable challenge for the data management team,  [[https://orionadviisoo.biz/desmondyork388/medic-glp-website8208/wiki/How-to-Control-your-Hunger-Hormones-to-Lose-Weight-Fast%2C-according-to-Experts|MedicGLP Online]] who frequently found themselves [[https://www.groundreport.com/?s=assisting|assisting]] other users in extracting crucial information regarding the company's managed assets and its operational regions. GLP needed a swift, effective solution to alleviate the growing strain on resources and ensure timely, accurate data retrieval for all users. At the recommendation of members of its Microsoft® Azure® partner team, GLP reached out to the generative AI experts at Rackspace Technology®. "We chose Rackspace because they are very practical," explains Song. "They also have data experts who can really help and work with us directly on a day-to-day basis. "Rackspace has a different approach compared to traditional consulting firms.



GLP for its envisioned AI-driven data analytics solution, the Rackspace AI team embarked on a series of discovery sessions to meticulously analyze GLP’s needs and identify the most pressing use case. Amid a fast-approaching visit by a global team to Singapore, a demo was created to showcase the potential of addressing GLP's core issue: the transformation of its daily reports through AI-enhanced data-querying capabilities. "We did a few rounds of our sprints. And from sprint to sprint, it was getting better and better. The use case becomes much more usable and real for our organization", explains Song. "That was a key differentiator for Rackspace. I think the company has a different approach compared to traditional consulting firms. Nathan Cheng, Data Analytics and Machine Learning Systems Engineer at Rackspace Technology, explained the wish list's two main themes: a user-friendly product interface and an intelligent system capable of deep-diving into data dimensions to answer complex business inquiries.



This wish list evolved into concrete test cases, marking the roadmap for the project's technical progression. Building a centralized hub for open-ended data insights was an ambitious endeavor that began with a focus on fund management and could be extended to ESG metrics and beyond. The goal was to create a dynamic interface where users could easily access their conversation histories, particularly when reviewing the fund management KPIs via the app. The interface design focused on a cohesive user experience and easy conversation-based functionality. Interactions needed to be logged, as documentation was crucial for identifying and analyzing any existing logic gaps, which would, in turn, inform iterative improvements to the system. Then conversations turned toward complex queries, like comparing the occupancy rate metrics of properties across different quarters. Such requests required the system to sift through multiple data dimensions, pinpoint the relevant data, and identify the factors that drove changes in the metrics.



Transitioning to the technical execution, Srinivas Chakravarthy, a Senior Director of Business Development at Rackspace, highlighted the innovative approach of crafting on-the-fly SQL queries based on users' inquiries. Initially working with CSP files, the goal was to transition into a production-ready phase where data lakes could be utilized for real-time data access and analysis. The process entailed running an SQL query, interpreting the data and furnishing the user with insightful responses. Achieving this required a precise SQL phrasing, aligning the technology to comprehend and respond to the contextual nuances of each inquiry. Rackspace engaged with various GLP teams to create a deep understanding of the types of inquiries employees made. This led to quick and effective calibration of the AI and allowed GLP to deploy a generative AI chatbot atop its EDL that could generate answers to business-related questions. The chatbot, with its intuitive user interface, showcases common questions and guides users on how to formulate and pose accurate prompts.