可信AI的崛起：探究AI如何改变商业智能格局并塑造分析学的未来

WHITEPAPER The Rise of Trusted AI Discover how AI is transforming the BIlandscape and shaping the future of analytics Table of Contents IntroductionChallenges with AI and Data AnalyticsBridging the Divide: A Semantic Graph to Solve AI's Data ChallengesAI + BI Integration: The Strategy AdvantageThe Strategy DifferenceElevate Your Analytics, Transform Your Business34691213 Introduction The rise of Artificial Intelligence (AI) is transforming the business intelligence landscape,reshaping how organizations gather, analyze, and act on their data. For decades, BusinessIntelligence (BI) has enabled data-driven decision-making, powering operationalimprovements, enhancing product quality, and increasing efficiency across sectors. But with At the heart of this transformation is Strategy One, a cloud-native platform designed toseamlessly integrate AI and BI, turning data into a trusted resource for innovation all on asingle unified platform. By leveraging AI’s natural language capabilities and sophisticatedanalytics, Strategy is pushing the boundaries of what’s possible—helping organizations This whitepaper explores the challenges and opportunities of merging AI with BI anddemonstrates how Strategy's cutting-edge technology is empowering organizations to driveactionable intelligence across their enterprise. In the following sections, we will explore howthe Strategy One platform can help your organization harness AI to deliver trusted, pervasive, Challenges with AI and Data Analytics By integrating AI seamlessly into your data experiences, you can supercharge insights acrossany application. However, AI by itself is not a complete analytics solution. AI relies on thequality of data being fed into it. Data inconsistencies and misinterpretations decrease the output Challenge 1: AI Does not Fix Data Quality and Data Silo Problems Even the most sophisticated AI can only query and understand the data it interacts with.Inconsistent or poor-quality data will return flawed results. This is particularly evident inorganizations where data analysis happens via spreadsheets or point-solution BI. The absenceof a Single Version of the Truth (SVOT) leads to non-standardized definitions and conflicting When data lacks a unified reference point it exists in silos. Implementing AI on top of data silosexacerbates the problem of data inconsistencies. There is no unified data source that AI is The solution to this is robust data management and alignment. Data preparation accounts for asignificant portion of the effort involved in crafting AI solutions. Industry experts suggest thatover 80% of the work dedicated to AI solutions is invested in data integration tasks. Without Challenge 2: AI and the Problem of Inaccurate Data Interpretation Current AI Large Language Models (LLMs), such as those offered by OpenAI, are engineeredto generate human-like text. They understand context and perform a wide variety of naturallanguage processing tasks. However, these models are not specifically designed for reliabledata calculations. They are trained on textual datasets, so their ability to perform numerical Consider the following illustrative example where we provided GPT with a straightforwarddataset in CSV format, consisting of Subcategory, Quarter, Revenue, and Profit (50 rows total).We then asked the AI model to give us the total Profit for each Subcategory, a task requiring a GPT Prompt Request Expected Results Can you help analyze the data below, list out the total profitfor each subcategory: “Category”,“Subcategory”,“Quarter”,“Profit”,“Cost”"Electronics","Audio Equipment",“2024 Q4”,“24557”,“136934”"Electronics","Audio Equipment”,“2024 Q3”,“6539”,“32785”"Electronics",“Audio Equipment”,“2024 Q1”,“6440”,“27290”"Electronics”,"Audio Equipment”,“2023 Q4”,“6412”,“41322”"Electronics”,"Audio Equipment”,“2024 Q2”,“5522”,“27144”"Electronics”,"Audio Equipment”,“2023 Q3”,“3899”,“20094”"Electronics”,"Audio Equipment”,“2023 Q1”,“1481”,“6359”'Electronics”,“Audio Equipment",“2023 Q2”,“1477”,“ 6143”'Electronics”,"Audio Equipment”,“2022 Q4”,“872”,“6674”"Electronics”,“Cameras”,“2024 Q4”,“61506”,“326765” The implications of these limitations are significant in the field of analytics where accuracy ofresults is crucial. For example, in the preparation and analysis of financial statements, every Bridging the Divide: A Semantic Graph to Solve AI's Data silos and the associated data inconsistencies, as well as limitations of AI in data analysis,can be solved with a Semantic Graph, a technology layer that provides centralized and reusable A Semantic Graph acts as an interpretive layer, translating source data into meaningfuland unified business concepts and relationships. It standardizes an organization’s Not only does it help to bolster data integrity, but it also serves as a vital component for AI The Role of Prompt Engineering and Semantic Graph Using AI to directly interpret and aggregate data can be unreliable. However, employing AI prompt