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callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EAUGMENTED ANALYTICS DEFINED\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EAugmented analytics is an approach to analytics that uses AI and automation to assist people in interpreting data, investigating questions and producing decision-ready insights with less manual effort.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text__0":{"id":"text-4d9f40d3e1","text":"\u003Cp\u003EA single analytical question can involve a surprising amount of work: finding and preparing the relevant data, choosing an analytical method, writing or adjusting a query, checking the result and explaining what the finding means. Augmented analytics applies \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/\"\u003EAI\u003C/a\u003E, \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/\"\u003Emachine learning\u003C/a\u003E (ML) and automation across that workflow, helping analysts reduce repetitive work while giving business users more direct ways to explore data, including through natural-language questions.\u003C/p\u003E\n\u003Cp\u003EHowever, AI puts more pressure on the data, governance and evaluation systems around \u003Ca href=\"https://www.snowflake.com/en/analytics/\"\u003Eanalytics\u003C/a\u003E because it takes on more of the choices previously handled by people. Data quality, business context, permissions and validation determine whether an AI-assisted result is reliable enough to inform a decision. That foundation is still a work in progress for many organizations. In research from \u003Ca href=\"https://www.technologyreview.com/2024/10/14/1105249/data-strategies-for-ai-leaders/\" target=\"_blank\"\u003EMIT Technology Review Insights and Snowflake\u003C/a\u003E, just 22% of more than 275 business leaders said their organizations have a data foundation for \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/generative-ai/\"\u003Egenerative AI\u003C/a\u003E that is “highly ready.”\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-augmented-analytics":{"id":"title-v2-429c9f27ec","additionalClasses":"anchor-title anchor-title--what-is-augmented-analytics","type":"heading2","lines":["What is augmented analytics?"],":type":"snowflake-site/components/title-v2"},"text_what-is-augmented-analytics_0":{"id":"text-00d8e2a416","text":"\u003Cp\u003EAugmented analytics uses AI, ML and natural-language technologies to assist with parts of the analytics workflow, including data preparation, querying, insight discovery, explanation, visualization and collaboration.\u003C/p\u003E\n\u003Cp\u003ESometimes the process starts with a person asking a question. In other cases, the system detects an anomaly, change or relationship that deserves attention. In either case, AI assists with the analytical work while people retain responsibility for interpreting consequential results and deciding what to do next.\u003C/p\u003E\n\u003Cp\u003EWhat distinguishes augmented analytics is the way those capabilities are connected across the analytical process. A natural-language question, for example, might trigger query generation, statistical analysis and an explanation of the result within the same experience. The goal is to reduce the manual transitions between analytical steps while preserving the evidence and context needed to evaluate the result. Human ownership is particularly important in enterprise analytics, where answers must inherit established business definitions and access controls.\u003C/p\u003E\n\u003Cp\u003E\u003Cem\u003ESee how semantic views give AI-powered analytics the business context needed to interpret questions and generate more reliable answers:\u003C/em\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"yt_what-is-augmented-analytics_0":{"id":"embed-9c09d1c1f0","youtubeVideoId":"6TUckfUdweE","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"title_how-augmented-analytics-turns-data-into-answers":{"id":"title-v2-8824a753c0","additionalClasses":"anchor-title anchor-title--how-augmented-analytics-turns-data-into-answers","type":"heading2","lines":["How augmented analytics turns data into answers"],":type":"snowflake-site/components/title-v2"},"text_how-augmented-analytics-turns-data-into-answers_0":{"id":"text-e2f45f692d","text":"\u003Cp\u003ETurning raw data into decision-ready insight with augmented analytics relies on a continuous loop that grounds the data in business context and refines itself with every decision.\u003C/p\u003E\n\u003Ch3\u003EContext\u003C/h3\u003E\n\u003Cp\u003EThe first step is context. The system needs to know what the relevant entities, metrics, relationships, permissions and freshness rules mean before it can interpret the request correctly. Without that layer, even a well-formed query can answer the wrong business question.\u003C/p\u003E\n\u003Ch3\u003EAnalysis\u003C/h3\u003E\n\u003Cp\u003ENext comes analysis. Depending on the request, the system might generate SQL, compare a result with a threshold, apply a statistical or ML method or combine evidence from structured and unstructured data. The method should follow the question — a descriptive comparison, anomaly investigation and forecast require different analytical logic.\u003C/p\u003E\n\u003Ch3\u003EExplanation\u003C/h3\u003E\n\u003Cp\u003EThe result then has to be explained in a form the decision-maker can use, such as a chart, table, narrative summary or combination of the three. For consequential analysis, the explanation should preserve enough source context that a reviewer can see where the result came from and how it was produced.\u003C/p\u003E\n\u003Ch3\u003EReview and action\u003C/h3\u003E\n\u003Cp\u003EFrom there, the workflow moves into review and action. Routine questions may need little intervention, while ambiguous, regulated or high-impact decisions call for closer scrutiny. The person responsible for the decision checks assumptions, resolves uncertainty and decides on an action.\u003C/p\u003E\n\u003Ch3\u003EFeedback\u003C/h3\u003E\n\u003Cp\u003EFinally, feedback closes the loop. Verified questions, corrected metric mappings, accepted or rejected insights and user behavior all create feedback that teams can use to improve future performance.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_how-augmented-analytics-turns-data-into-answers_0":{"id":"card-v2-91cf71c3a3","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/customers/all-customers/video/university-of-auckland/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Watch the customer story"},":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["Customer story: University of Auckland"],":type":"snowflake-site/components/title-v2"},"image":{"id":"image","height":"117","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--e4dda355-cd90-4f9c-bcfa-cd67b823e819/university-of-auckland.png?quality=85&preferwebp=true","alt":"University of Auckland logo","lazyEnabled":true,"isLcpImage":false,"width":"208",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003EThe University of Auckland uses Snowflake Cortex Analyst to give staff natural-language access to data, helping users get answers without navigating complex database terminology while maintaining a secure data foundation.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal"},"title_augmented-analytics-vs-traditional-bi":{"id":"title-v2-3804f67338","additionalClasses":"anchor-title anchor-title--augmented-analytics-vs-traditional-bi","type":"heading2","lines":["Augmented analytics vs. traditional BI"],":type":"snowflake-site/components/title-v2"},"text_augmented-analytics-vs-traditional-bi_0":{"id":"text-a512c973d7","text":"\u003Cp\u003ETraditional \u003Ca href=\"https://www.snowflake.com/en/analytics/business-intelligence/\"\u003Ebusiness intelligence\u003C/a\u003E (BI) typically gives users dashboards, reports and tools for exploring established metrics and recurring business questions. Augmented analytics builds on that foundation by using AI and automation to reduce some of the manual steps involved in querying, discovery and explanation.\u003C/p\u003E\n\u003Cp\u003EFor example, in a traditional BI environment, a sales leader might filter a report to investigate a decline in revenue. With augmented analytics, the same user could ask in natural language why revenue declined, have the system generate the appropriate query, identify contributing segments and return a narrative explanation for review.\u003C/p\u003E\n\u003Cp\u003EAugmented analytics combines AI, automation and analytical methods. These analytical methods include \u003Ca href=\"https://www.snowflake.com/en/analytics/ai-analytics/predictive-analytics/\"\u003Epredictive analytics\u003C/a\u003E, which estimates what’s likely to happen, and prescriptive analytics, which evaluates possible actions. ML supplies methods for identifying patterns or generating predictions, while generative AI supports tasks such as translating natural-language questions into SQL or summarizing analytical results. An augmented analytics system may use any of those capabilities as part of a broader analytical experience.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_why-augmented-analytics-needs-trusted-business-context":{"id":"title-v2-0e81998c4d","additionalClasses":"anchor-title anchor-title--why-augmented-analytics-needs-trusted-business-context","type":"heading2","lines":["Why augmented analytics needs trusted business context"],":type":"snowflake-site/components/title-v2"},"text_why-augmented-analytics-needs-trusted-business-context_0":{"id":"text-f58563321b","text":"\u003Cp\u003EConsider a seemingly straightforward question: “Which suppliers are responsible for the most late deliveries?” Before analyzing anything, the system has to resolve several questions. What qualifies as late: after the requested date, a promised date or an internally adjusted date? How should partial shipments count? Which supplier gets attribution when an order contains products from several vendors? Those choices determine the result just as much as the SQL does.\u003C/p\u003E\n\u003Ch3\u003EBusiness definitions give data a shared meaning\u003C/h3\u003E\n\u003Cp\u003EEnterprise data is rarely explained via column names alone. Metrics, dimensions and categories often depend on rules that live in documentation, BI models or subject matter expertise.\u003C/p\u003E\n\u003Cp\u003EFor augmented analytics, those definitions need to be available to the system during analysis. Otherwise, a natural-language request can map to a technically valid calculation that doesn’t match the way the organization actually measures the business.\u003C/p\u003E\n\u003Ch3\u003ERelationships and grain shape the answer\u003C/h3\u003E\n\u003Cp\u003EContext also includes how data fits together. An order table, shipment table and supplier table might each contain the information needed to investigate late deliveries, but joining them incorrectly could duplicate records or attribute a delay to the wrong supplier.\u003C/p\u003E\n\u003Cp\u003ETime grain creates similar problems. A monthly metric can’t always be meaningfully compared with a daily one, and some measures shouldn’t be summed across periods at all.\u003C/p\u003E\n\u003Cp\u003EBy representing those relationships explicitly, augmented analytics systems have more information for determining which data to use and how to combine it.\u003C/p\u003E\n\u003Ch3\u003EPermissions and freshness constrain what the system should use\u003C/h3\u003E\n\u003Cp\u003EThe relevant answer also depends on what a user is allowed to see. The system should identify the permissions of the user and expose only that data. For example, a procurement manager might have access to supplier performance data but not negotiated pricing or other restricted information.\u003C/p\u003E\n\u003Cp\u003EFreshness is another important factor. A question about today’s delivery delays, for example, shouldn’t rely on a table that refreshes once a week.\u003C/p\u003E\n\u003Cp\u003EGovernance therefore has to travel with the analysis. \u003Ca href=\"https://www.snowflake.com/en/data-governance/data-security/rbac/\"\u003ERole-based access controls\u003C/a\u003E must apply to the \u003Ca href=\"https://www.snowflake.com/en/data-governance/\"\u003Egoverned data\u003C/a\u003E and objects being queried, rather than requiring organizations to create a separate copy of the data for the AI interface.\u003C/p\u003E\n\u003Ch3\u003EVerified examples provide a reference point\u003C/h3\u003E\n\u003Cp\u003ESome questions recur often enough that teams already know what the correct analysis should look like. Those examples are useful context too. Natural-language questions can be paired with SQL that’s been confirmed as correct. When similar questions come up later, the system can use relevant verified queries as examples.\u003C/p\u003E\n\u003Cp\u003EContext reduces ambiguity, but it doesn’t remove the need for review. For consequential analysis, teams should still be able to inspect the generated SQL, source objects and assumptions behind the answer, particularly when an unusual question falls outside the patterns the system has seen before.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_why-augmented-analytics-needs-trusted-business-context_0":{"id":"text-2e1b38d5bd","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003ECOMMON PITFALL\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EA common mistake is treating technically correct SQL as proof of a correct answer. A query can run perfectly while using the wrong metric definition, join path, time grain or population.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_enterprise-augmented-analytics-use-cases":{"id":"title-v2-f388c1916b","additionalClasses":"anchor-title anchor-title--enterprise-augmented-analytics-use-cases","type":"heading2","lines":["Enterprise augmented analytics use cases"],":type":"snowflake-site/components/title-v2"},"text_enterprise-augmented-analytics-use-cases_0":{"id":"text-5b957f881b","text":"\u003Cp\u003EThe strongest augmented analytics use cases tend to share a few characteristics: the questions recur, the underlying definitions are clear and someone owns the decision that follows.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003ESelf-service business questions:\u003C/strong\u003E Recurring requests, such as revenue by region, product performance or customer retention, often consume analyst time even when the underlying analysis is well understood. Natural-language access gives business users a more direct route to those answers while the domain team retains ownership of the metrics.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EProactive anomaly and driver discovery:\u003C/strong\u003E Finance, operations, product and revenue teams often need to be alerted to meaningful changes shortly after they occur. An augmented workflow might flag an unexpected drop in conversions, for example, then identify the products, regions or customer segments associated with the shift and link the explanation back to supporting data.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EPredictive and scenario analysis:\u003C/strong\u003E Teams forecasting demand, churn, capacity or risk frequently move between historical analysis and estimates of what comes next. Augmented workflows give users a more accessible way to query those results and explore scenarios, while dedicated predictive analytics methods handle the underlying estimation.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003ENarrative and visual explanation:\u003C/strong\u003E A detailed analytical result isn’t always the format an executive, frontline employee or external audience needs. Generative AI supports first drafts of charts, summaries and explanations, provided the underlying metrics are defined consistently and teams review material intended for consequential or external use.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EEmbedded analytics:\u003C/strong\u003E Analytical questions increasingly appear inside operational applications and workflows. A user reviewing an account, for example, might ask why usage dropped without leaving the application. When the workflow moves from insight into action, organizations need explicit thresholds, approvals and an audit trail.\u003C/li\u003E\n\u003C/ul\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_risks-limits-and-the-role-of-human-judgment":{"id":"title-v2-e225a116e4","additionalClasses":"anchor-title anchor-title--risks-limits-and-the-role-of-human-judgment","type":"heading2","lines":["Risks, limits and the role of human judgment"],":type":"snowflake-site/components/title-v2"},"text_risks-limits-and-the-role-of-human-judgment_0":{"id":"text-74c51312d4","text":"\u003Cp\u003EAugmented analytics can reduce manual analytical work, but it’s susceptible to errors. Some originate in the data or analytical logic, while others arise because the system lacks context that a human user would recognize immediately. Generative AI introduces an additional concern: an incorrect result can be presented fluently and with confidence, making the error difficult to detect.\u003C/p\u003E\n\u003Ch3\u003EAnalytical errors can enter at multiple points\u003C/h3\u003E\n\u003Cp\u003EAn augmented analytics workflow can go wrong before the final answer is ever generated. A join might duplicate rows, or a stale table might be used for a question about a current situation. A non-additive metric could be aggregated incorrectly, or ambiguous language might map to the wrong population. Even with correct calculations, the interpretation can still overreach — a relationship between two variables doesn’t establish causation, for example.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/blog/engineering/optimize-enterprise-llm-queries\"\u003ERecent research from Snowflake AI Research and Brown University\u003C/a\u003E illustrates the problem. In AvalancheBench, systems were evaluated on end-to-end analytical correctness rather than whether the pipeline merely completed. In one ecommerce use case, even the strongest configuration of a leading coding agent correctly identified only 26% of the segments, events and relationships the benchmark expected it to find. Errors introduced earlier in the analysis propagated into the final result.\u003C/p\u003E\n\u003Cp\u003EBecause those errors can enter at different stages, reviewers need visibility into the analytical path: source data, generated SQL, metric definitions and relevant assumptions. Checking only the final chart or summary isn’t enough.\u003C/p\u003E\n\u003Ch3\u003EData doesn’t contain every piece of business context\u003C/h3\u003E\n\u003Cp\u003EAn \u003Ca href=\"https://www.snowflake.com/en/analytics/ai-analytics/\"\u003EAI analytics\u003C/a\u003E system works with the information available to it. For example, it may identify a sharp decrease in product usage without knowing that a planned customer migration occurred that week, or find an apparent performance difference among groups without recognizing a policy change that affected how the data was collected.\u003C/p\u003E\n\u003Cp\u003EHistorical data can also preserve historical inequities or patterns that an organization doesn’t want to reproduce. A statistically strong relationship isn’t automatically an appropriate basis for a business decision. This is why domain expertise remains essential. People bring knowledge of events, operational constraints and consequences that may never appear explicitly in the underlying data.\u003C/p\u003E\n\u003Ch3\u003EFluent answers can encourage too much confidence\u003C/h3\u003E\n\u003Cp\u003EAugmented analytics changes the way people encounter analysis. Instead of examining a query or building a chart themselves, users may receive a polished explanation immediately. Fluency and completeness can make that answer feel more authoritative than the evidence supports.\u003C/p\u003E\n\u003Cp\u003EHuman judgment has a specific role: determining whether the assumptions fit the situation, recognizing context the system doesn’t have and deciding how much evidence is necessary before acting. As augmented analytics handles more of the mechanics of analysis, those judgment calls must remain with the people accountable for the decision. Source references, generated queries, clarification requests and explicit review steps give users something concrete to inspect before relying on an answer.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_how-to-implement-augmented-analytics-responsibly":{"id":"title-v2-112d022e11","additionalClasses":"anchor-title anchor-title--how-to-implement-augmented-analytics-responsibly","type":"heading2","lines":["How to implement augmented analytics responsibly"],":type":"snowflake-site/components/title-v2"},"text_how-to-implement-augmented-analytics-responsibly_0":{"id":"text-673d185085","text":"\u003Cp\u003EThe safest way to introduce augmented analytics is to start small enough that teams can see exactly where it works and where it breaks. A bounded set of business questions gives them something concrete to test against: Are the right data sources being used? Do the answers match established metrics? Where do users need clarification or analyst review?\u003C/p\u003E\n\u003Cp\u003EOnce those answers are understood, teams can expand by domain, carrying forward the definitions, permissions, test cases and review practices that proved useful in the pilot.\u003C/p\u003E\n\u003Col\u003E\n\u003Cli\u003E\u003Cstrong\u003EChoose a repeatable decision or question:\u003C/strong\u003E Identify the intended user, the current delay, relevant source systems, the person who owns the decision and the consequences of a wrong answer. Questions that appear frequently and have measurable outcomes make strong candidates.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EPrepare the data and permissions:\u003C/strong\u003E Resolve freshness, quality, identity, lineage and access issues within the pilot scope. An AI interface still depends on the data underneath it.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EAssign owners to business definitions:\u003C/strong\u003E Domain teams should approve and maintain the metrics, relationships and other semantic context used in the workflow. Track changes over time so revisions to a definition trigger appropriate testing.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EBuild a representative test set:\u003C/strong\u003E Use questions people actually ask, paired with verified SQL and expected results. Include ambiguous wording, multi-table joins, time comparisons and known edge cases rather than testing only the easiest examples.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EPilot with domain users:\u003C/strong\u003E During early use, capture unanswered questions, corrections, clarification requests and escalation points. Those interactions reveal where the system interprets intent well and where a person still needs to intervene.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EMeasure quality as well as adoption:\u003C/strong\u003E Useful measures include answerability, SQL correctness, agreement with certified metrics, time to answer, clarification rate, review rate, adoption among intended users and analyst hours returned. Connect those operational measures to the outcome of the decision whenever possible.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EExpand by domain:\u003C/strong\u003E Once the test set, business definitions and ownership model hold up for one area, extend the approach to another. Changes to data structures, permissions, metrics or models should trigger another round of evaluation.\u003C/li\u003E\n\u003C/ol\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_how-to-implement-augmented-analytics-responsibly_0":{"id":"text-df05e1f106","additionalClasses":"callout callout--tip","text":"\u003Cp\u003E\u003Cstrong\u003EQUICK TIP\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EBuild your initial test set from questions people actually ask. Recurring analyst requests, dashboard usage and query history are often better starting points than synthetic examples because they expose the ambiguity and edge cases the system will encounter in production.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_how-snowflake-supports-augmented-analytics":{"id":"title-v2-fdac83474c","additionalClasses":"anchor-title anchor-title--how-snowflake-supports-augmented-analytics","type":"heading2","lines":["How Snowflake supports augmented analytics"],":type":"snowflake-site/components/title-v2"},"text_how-snowflake-supports-augmented-analytics_0":{"id":"text-903173b2af","text":"\u003Cp\u003ESnowflake supports analytics, including augmented analytics, through a shared foundation of governed data and business context. Semantic Views provide business-aware definitions for dimensions, metrics and relationships over governed data. \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst\"\u003ECortex Analyst\u003C/a\u003E uses that context to translate natural-language questions into SQL for structured-data analysis and returns the generated query alongside the answer, giving users and reviewers visibility into how the result was produced.\u003C/p\u003E\n\u003Cp\u003EFor knowledge workers working across a broader set of enterprise information, \u003Ca href=\"https://www.snowflake.com/en/product/snowflake-cowork/\"\u003ESnowflake CoWork\u003C/a\u003E provides an interface for working with structured and unstructured data, producing cited answers and, where configured, interacting with governed tools.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/product/features/cortex/\"\u003ECortex Agents\u003C/a\u003E support teams building managed agent workflows, while Cortex AI Functions analyze unstructured content such as text, images, audio and video inside Snowflake. For predictive workloads, \u003Ca href=\"https://www.snowflake.com/en/product/features/end-to-end-ml-workflows/\"\u003ESnowflake ML\u003C/a\u003E provides capabilities spanning feature management, model training, inference, \u003Ca href=\"https://www.snowflake.com/en/observability/\"\u003Eobservability\u003C/a\u003E, explainability and lineage.\u003C/p\u003E\n\u003Cp\u003EAcross those workflows, the architectural principle stays consistent: use the capabilities appropriate to the question while preserving governed access, shared business meaning and a path to verification.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_trust-has-to-keep-pace-with-automation":{"id":"title-v2-8481771166","additionalClasses":"anchor-title anchor-title--trust-has-to-keep-pace-with-automation","type":"heading2","lines":["Trust has to keep pace with automation"],":type":"snowflake-site/components/title-v2"},"text_trust-has-to-keep-pace-with-automation_0":{"id":"text-5ad9c7e276","text":"\u003Cp\u003EAugmented analytics gives teams new ways to reduce the manual work between a question and a decision, but faster analysis only helps when the result is grounded in trusted data, shared business context and appropriate review. As AI takes on more of the analytical process, the surrounding architecture has to carry more of the burden for consistency, access control and verification.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_trust-has-to-keep-pace-with-automation_0":{"id":"text-d01ba7faaf","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EKEY TAKEAWAY\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EAugmented analytics only pays off when speed is matched by trusted data, shared business context and human review — the surrounding architecture, not the AI alone, is what makes fast answers reliable.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular 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