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Early work with a data set often exposes a missing variable, an unexpected segment or a problem with the original question. Analysts then return to the source data, revise the question and investigate again.\u003C/p\u003E\r\n\u003Cp\u003EThroughout that process, the analysis inherits the condition of the data underneath it. If the inputs misrepresent the business, the result will too.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ci\u003EDiscover the latest AI-powered BI and interactive \u003Ca href=\"https://www.snowflake.com/en/analytics/\"\u003Eanalytics\u003C/a\u003E capabilities, with a demo that shows you how to quickly get to deep insights:\u003C/i\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"yt_what-is-data-analysis_0":{"id":"embed-5dbeb2b17f","youtubeVideoId":"560rRpKij6U","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_data-analysis-vs-data-science":{"id":"title-v2-3a04f40f70","additionalClasses":"anchor-title anchor-title--data-analysis-vs-data-science","type":"heading2","lines":["Data analysis vs. data science"],":type":"snowflake-site/components/title-v2"},"text_data-analysis-vs-data-science_0":{"id":"text-b9b6b4e12c","text":"\u003Cp\u003E\u003Cstrong\u003EData analysis\u003C/strong\u003E begins with a defined question and follows the evidence toward a conclusion that can inform a decision.\u003C/p\u003E\n\u003Cp\u003E\u003Cstrong\u003EData science\u003C/strong\u003E often extends into experimentation and \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/\"\u003Emachine learning\u003C/a\u003E. A data scientist working with customer retention data, for instance, might develop a model that estimates which customers are likely to churn and test which variables improve its performance.\u003C/p\u003E\n\u003Cp\u003EThe exact division of work varies among organizations, especially on smaller teams.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_the-data-analysis-process-step-by-step":{"id":"title-v2-23d10cbd91","additionalClasses":"anchor-title anchor-title--the-data-analysis-process-step-by-step","type":"heading2","lines":["The data analysis process, step by step"],":type":"snowflake-site/components/title-v2"},"text_the-data-analysis-process-step-by-step_0":{"id":"text-a478117b0c","text":"\u003Cp\u003EMost data analysis follows six broad steps:\u003C/p\u003E\r\n\u003Col\u003E\r\n\u003Cli\u003E\u003Cb\u003EDefine the question\u003C/b\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ECollect the relevant data\u003C/b\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EClean and prepare the data\u003C/b\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EAnalyze the data\u003C/b\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EInterpret the results\u003C/b\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ECommunicate the findings\u003C/b\u003E\u003C/li\u003E\r\n\u003C/ol\u003E\r\n\u003Cp\u003EIn practice, analysts move back and forth among these steps. A finding in step five might expose missing data from step two, for example, while preparation might reveal that the original question needs to be narrowed.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_the-data-analysis-process-step-by-step_0":{"id":"text-cb93c133cb","additionalClasses":"callout callout--tip","text":"\u003Cp\u003E\u003Cb\u003EQUICK TIP\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003EBefore collecting data, identify the decision the analysis is intended to inform and what evidence would change that decision. That gives you a clearer basis for choosing the data, time period and level of detail the analysis actually requires.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text_the-data-analysis-process-step-by-step_1":{"id":"text-aa3474a60f","text":"\u003Ch3\u003E1. Define the question\u003C/h3\u003E\n\u003Cp\u003EA well-framed question gives the analysis a clear scope and purpose. For example, “Why did renewal rates fall among midmarket customers last quarter?” identifies a population, an outcome and a period to investigate.\u003C/p\u003E\n\u003Cp\u003EThe question should also connect cleanly to a decision. If a team is deciding whether to redesign an onboarding flow, for example, it needs to know which outcome the redesign is intended to affect and what evidence would support making the change.\u003C/p\u003E\n\u003Cp\u003EWithout that connection, an analysis might generate interesting patterns indefinitely without defining which result would warrant taking action or investigating further.\u003C/p\u003E\n\u003Ch3\u003E2. Collect the data\u003C/h3\u003E\n\u003Cp\u003EOnce the question is well defined, analysts identify the data needed to answer it. A retention analysis, for example, might combine subscription records, product usage events and customer attributes from a CRM system.\u003C/p\u003E\n\u003Cp\u003EAnalysts also need to know where the data originated, what period it covers, how frequently it refreshes and which transformations have already been applied. These details are especially relevant when several systems contain similar information since various systems might each contain a field with the same label but use different rules to populate it.\u003C/p\u003E\n\u003Ch3\u003E3. Clean and prepare the data\u003C/h3\u003E\n\u003Cp\u003EPreparation often accounts for a large share of the work. Analysts resolve missing values, standardize formats, investigate duplicate records, join data sets and aggregate observations to the appropriate level. A surprising amount of analytical judgment is required for these tasks.\u003C/p\u003E\n\u003Cp\u003ESuppose a customer table contains three records for the same organization. Which one represents the customer? The newest record might be correct, or the records might need to be merged. Treating all three as separate customers changes counts, averages and any downstream metric based on customer volume.\u003C/p\u003E\n\u003Cp\u003ENull values create a similar problem. A blank value might mean zero, unknown, not applicable or simply missing. Filling it incorrectly changes the analysis.\u003C/p\u003E\n\u003Cp\u003EPreparation is where assumptions about what the records represent get translated into the data set analysts use, so data cleaning and data wrangling directly shape the accuracy of results.\u003C/p\u003E\n\u003Ch3\u003E4. Analyze the data\u003C/h3\u003E\n\u003Cp\u003EWith the data prepared, analysts choose analysis methods that fit the question, including aggregation, exploratory data analysis, regression, cohort analysis, controlled experiments and other statistical techniques.\u003C/p\u003E\n\u003Cp\u003EExploration often starts simply, as analysts look at distributions, medians, percentiles and visualizations that expose patterns that weren’t obvious in the aggregate view.\u003C/p\u003E\n\u003Cp\u003EIf average order value rises 6%, for example, a segment-level view might show that most customers are spending the same amount while a small group of large accounts is driving the entire increase.\u003C/p\u003E\n\u003Ch3\u003E5. Interpret the results\u003C/h3\u003E\n\u003Cp\u003EInterpretation asks what the observed result actually supports. Analysts must remember that a relationship between two variables doesn’t establish that one caused the other, and an apparent trend sometimes disappears when the data is separated by customer segment, geography or time period.\u003C/p\u003E\n\u003Cp\u003EAlternative explanations need attention as well. If churn rises after a pricing change, an analyst might check whether a product outage occurred during the same period, whether the increase is concentrated among customers affected by the new price or whether the definition of churn changed upstream. This investigation often sends the analyst back through earlier steps with a narrower question.\u003C/p\u003E\n\u003Ch3\u003E6. Communicate the findings\u003C/h3\u003E\n\u003Cp\u003EThe final result of an analysis has to give its audience enough information to act on it. For an executive, that might mean a recommendation supported by a few carefully selected measures. A line-of-business leader may need more detail on which customers, products or processes are driving the result and what operational response it suggests. A fellow analyst might need the methodology, assumptions and source data required to reproduce the result.\u003C/p\u003E\n\u003Cp\u003EUncertainty shouldn’t be downplayed. If the evidence points in one direction but leaves an important confounding variable unresolved, the audience should know that before making a decision.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_the-data-analysis-process-step-by-step_0":{"id":"card-v2-c03f042407","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["Customer story: Under Armour"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/customers/all-customers/case-study/under-armour/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Read the full case study"},"image":{"id":"image","height":"351","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--8e0db12c-1ac0-4b9a-9702-ade0c8fe0199/underarmour%25403x.png?preferwebp=true&quality=85","alt":"Under Armour logo","lazyEnabled":true,"isLcpImage":false,"width":"624",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003EUnder Armour uses Snowflake to give teams a consistent foundation for analyzing data across inventory, supply chain, demand planning, sales and other functions. Product margin reporting that previously required a week of manual work and ran monthly is now automated and delivered daily; the company has since extended that governed data foundation to conversational analysis with Snowflake CoWork.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal"},"title_the-four-types-of-data-analysis":{"id":"title-v2-909ac41f35","additionalClasses":"anchor-title anchor-title--the-four-types-of-data-analysis","type":"heading2","lines":["The four types of data analysis"],":type":"snowflake-site/components/title-v2"},"text_the-four-types-of-data-analysis_0":{"id":"text-1f15c1546f","text":"\u003Cp\u003EFour categories of analysis cover most of the questions organizations ask of their data.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EDescriptive analysis\u003C/b\u003E examines what happened. Revenue by quarter, average delivery time and website traffic by channel are familiar examples.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EDiagnostic analysis\u003C/b\u003E investigates why an outcome occurred. If revenue declined, diagnostic work might compare regions, products, customer segments or sales channels to identify where the change originated.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EPredictive analysis\u003C/b\u003E estimates what’s likely to happen next, often using historical patterns and statistical or machine learning models. Demand forecasts and churn predictions fall into this category.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EPrescriptive analysis\u003C/b\u003E evaluates possible actions based on expected outcomes and constraints. A logistics system, for example, might use demand forecasts, inventory levels and transportation costs to recommend how stock should be allocated.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EThese types of analysis aren’t usually sequential. Organizations often use a mix, depending on the decision or problem at hand. For example, a retailer might monitor yesterday’s sales descriptively, investigate an unexpected decline in one region diagnostically and forecast next month’s demand predictively.\u003C/p\u003E\r\n\u003Cp\u003EThe quality of the data underneath each analysis carries forward. If customer classifications are wrong in descriptive reporting, a predictive model trained on those classifications inherits the same problem. When the output starts influencing future actions, the consequences can extend into multiple decisions.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_common-data-analysis-methods":{"id":"title-v2-19809e943b","additionalClasses":"anchor-title anchor-title--common-data-analysis-methods","type":"heading2","lines":["Common data analysis methods"],":type":"snowflake-site/components/title-v2"},"text_common-data-analysis-methods_0":{"id":"text-2feee69aeb","text":"\u003Cp\u003EThe choice of method typically starts with the question, then narrows based on the data available, the assumptions each technique requires and how the result will be used. The most common data analysis methods include A/B testing, regression, cluster analysis, cohort analysis, time-series analysis and sentiment analysis.\u003C/p\u003E\r\n\u003Ch3\u003EComparison\u003C/h3\u003E\r\n\u003Cp\u003EA/B testing compares outcomes between groups under controlled conditions. Product and marketing teams often use it to test changes to interfaces, pricing, messaging or customer experiences. Sample size, random assignment and corrections for multiple comparisons affect how much confidence analysts can place in the observed difference.\u003C/p\u003E\r\n\u003Ch3\u003ERelationship\u003C/h3\u003E\r\n\u003Cp\u003ERegression examines how variables are related. \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/models/linear-regression/\"\u003ELinear regression\u003C/a\u003E provides a relatively simple starting point, while other regression approaches accommodate different types of outcomes and relationships. An analyst might use regression to study the relationship between pricing and demand, or between product usage and retention.\u003C/p\u003E\r\n\u003Ch3\u003EGrouping\u003C/h3\u003E\r\n\u003Cp\u003ECluster analysis identifies observations that share similar characteristics. Cohort analysis follows groups that share a common starting point or attribute, such as customers acquired in the same month. Looking at retention by acquisition cohort, for example, can reveal changes that an overall retention average hides.\u003C/p\u003E\r\n\u003Ch3\u003EChange over time\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/time-series-analysis/\"\u003ETime-series analysis\u003C/a\u003E examines observations recorded across a sequence of time periods. It’s widely used for forecasting, trend detection and capacity planning, with factors such as seasonality affecting how analysts interpret the pattern.\u003C/p\u003E\r\n\u003Ch3\u003EMeaning in text\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/what-is-sentiment-analysis/\"\u003ESentiment analysis\u003C/a\u003E and related text-analysis methods extract patterns from unstructured language, including customer reviews, support conversations and survey responses.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_common-data-analysis-methods_0":{"id":"text-687353ec57","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003ECOMMON PITFALL\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EA common mistake is using a familiar method by default without checking whether its assumptions fit the data. For example, a technique that assumes independent observations or a linear relationship can produce misleading results when those conditions don’t hold.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_how-data-quality-determines-analysis-accuracy":{"id":"title-v2-9187b218b0","additionalClasses":"anchor-title anchor-title--how-data-quality-determines-analysis-accuracy","type":"heading2","lines":["How data quality determines analysis accuracy"],":type":"snowflake-site/components/title-v2"},"text_how-data-quality-determines-analysis-accuracy_0":{"id":"text-754ae8e9db","text":"\u003Cp\u003EAn analysis can be methodologically sound and still produce the wrong conclusion if the underlying data is incomplete, stale or interpreted incorrectly. Before trusting a result, analysts need to know that the records represent the right entities and events, reflect the relevant time period and can be traced back to their source.\u003C/p\u003E\n\u003Ch3\u003ECheck whether the data reflects what actually happened\u003C/h3\u003E\n\u003Cp\u003EAccuracy starts with whether the records represent the underlying entities or events correctly. An incorrectly mapped customer ID can assign activity to the wrong account, for example, and an upstream schema change can alter the meaning of a field.\u003C/p\u003E\n\u003Cp\u003ECompleteness and consistency affect accuracy as well. Missing records can skew a comparison, while conflicting values across source systems leave the analyst guessing which version to use.\u003C/p\u003E\n\u003Ch3\u003EMatch data freshness to the question\u003C/h3\u003E\n\u003Cp\u003EFreshness describes whether the data is current enough for the decision it’s intended to support, and the appropriate threshold varies widely by use case. Fraud detection might depend on events occurring within the past few seconds, while a weekly workforce report requires updates only within the week’s timeframe. Defining the expected refresh interval gives analysts a concrete way to judge whether a data set is stale.\u003C/p\u003E\n\u003Ch3\u003EUse lineage to understand where a result came from\u003C/h3\u003E\n\u003Cp\u003ELineage shows how data moves from its source through transformations and into downstream models, dashboards and reports. This traceability makes reconciliation more systematic. When two analyses disagree, analysts can follow the paths backward to see whether they started from different sources, applied different transformations or diverged at a particular field.\u003C/p\u003E\n\u003Ch3\u003ECheck downstream impact before data changes\u003C/h3\u003E\n\u003Cp\u003ELineage also supports impact analysis in the other direction. Before changing an upstream table or column, a data team can identify the models, dashboards and reports that depend on it.\u003C/p\u003E\n\u003Cp\u003ETable-level lineage provides a broad view of affected objects, while column-level lineage shows where a specific field flows. That distinction is important when a schema change leaves a table available but alters the meaning or format of one column used downstream.\u003C/p\u003E\n\u003Cp\u003EBefore sharing an analysis with stakeholders, an analyst should be able to answer three basic questions: Where did the data come from? How current is it? Who else depends on it?\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_the-semantic-layer-one-definition-of-every-metric":{"id":"title-v2-fbeb5e4e97","additionalClasses":"anchor-title anchor-title--the-semantic-layer-one-definition-of-every-metric","type":"heading2","lines":["The semantic layer: one definition of every metric"],":type":"snowflake-site/components/title-v2"},"text_the-semantic-layer-one-definition-of-every-metric_0":{"id":"text-bd18f2bbe9","text":"\u003Cp\u003EPicture a quarterly review where sales is reporting $14.2 million in revenue while finance reports $13.7 million.\u003C/p\u003E\r\n\u003Cp\u003ENeither team necessarily made a mathematical error. One calculation might exclude refunds while the other includes them. One might use the order date while the other uses the date revenue is recognized. Both queries run correctly, yet the organization now has two values attached to the same business term.\u003C/p\u003E\r\n\u003Cp\u003EA semantic layer gives those terms a shared definition. It stores business logic for metrics, dimensions and relationships — such as what revenue means, which customers count as active, how churn is calculated and how business entities relate to one another — so analysts and downstream tools use the same rules.\u003C/p\u003E\r\n\u003Cp\u003EHeadless BI extends the layer beyond a single reporting interface. Notebooks, dashboards, applications and AI interfaces can all work from the same business logic when governed metrics and dimensions are available independently of the frontend.\u003C/p\u003E\r\n\u003Cp\u003EAI-generated SQL relies on context to translate a business question into the correct query logic. The model must know which tables and fields correspond to the concepts in the question, how those tables relate, what grain the query should use and which metric definitions or filters apply. A physical schema provides some of that information, but it rarely captures business meaning. Semantic context supplies those definitions and relationships explicitly, giving the model a more constrained set of choices when it generates the query.\u003C/p\u003E\r\n\u003Cp\u003EThis context can make a measurable difference when AI has to reason across data sources. \u003Ca href=\"https://www.snowflake.com/en/blog/agent-context-layer-trustworthy-data-agents/\"\u003EIn one Snowflake internal experiment\u003C/a\u003E involving questions that required multiple semantic views, adding explicit information about join keys, table grain and cardinality improved final-answer accuracy by 20% while also reducing the number of tool calls and overall latency.\u003Csup\u003E1\u003C/sup\u003E\u003C/p\u003E\r\n\u003Cp\u003EAnalytics engineers increasingly participate in maintaining semantic models, usually alongside subject-matter experts who own the business definition. For example, finance determines what revenue means, while the data team translates that definition into reusable logic and keeps downstream analysis aligned with it.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_how-ai-is-changing-data-analysis":{"id":"title-v2-58eceeddcd","additionalClasses":"anchor-title anchor-title--how-ai-is-changing-data-analysis","type":"heading2","lines":["How AI is changing data analysis"],":type":"snowflake-site/components/title-v2"},"text_how-ai-is-changing-data-analysis_0":{"id":"text-dbc4d82099","text":"\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/generative-ai/\"\u003EGenerative AI\u003C/a\u003E is reducing some of the manual work between asking a question and querying the data. Natural-language interfaces, generated SQL and automated summaries give non-technical users a direct way to explore data, while shifting more of the analyst’s attention toward framing questions, maintaining business context and validating results.\u003C/p\u003E\r\n\u003Ch3\u003ENatural-language queries broaden access to analysis\u003C/h3\u003E\r\n\u003Cp\u003EBusiness users can increasingly ask questions in plain language rather than translating every request into SQL or waiting for a new dashboard. \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/natural-language-processing/\"\u003ENatural language\u003C/a\u003E interfaces make exploratory analysis more accessible, although the system still has to map the user’s language to the correct tables, relationships, filters and metrics.\u003C/p\u003E\r\n\u003Ch3\u003EAI speeds up query generation and follow-up analysis\u003C/h3\u003E\r\n\u003Cp\u003EFor analysts, generated SQL can reduce the time spent writing and revising routine queries. AI systems can also summarize results, surface patterns and support follow-up questions as an investigation develops.\u003C/p\u003E\r\n\u003Cp\u003EHuman judgment remains essential, since the analyst must determine whether the query actually represents the business question. A technically valid query can use the wrong grain, join tables incorrectly or apply a metric definition that doesn’t match the organization’s standard.\u003C/p\u003E\r\n\u003Ch3\u003EAnalysts spend more time validating and framing the work\u003C/h3\u003E\r\n\u003Cp\u003EAs AI handles more of the mechanical query work, analysts can devote more attention to defining the question, maintaining business logic and checking machine-produced output.\u003C/p\u003E\r\n\u003Cp\u003EThe review process includes familiar analytical checks: Does the query preserve the right grain? Do the filters match the intended population? Does the result reconcile with an authoritative source? Are there alternative explanations for the pattern?\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_data-analysis-at-enterprise-scale-on-snowflake":{"id":"title-v2-b45715777a","additionalClasses":"anchor-title anchor-title--data-analysis-at-enterprise-scale-on-snowflake","type":"heading2","lines":["Data analysis at enterprise scale on Snowflake"],":type":"snowflake-site/components/title-v2"},"text_data-analysis-at-enterprise-scale-on-snowflake_0":{"id":"text-76400f0783","text":"\u003Cp\u003EAt enterprise scale, analysts often work across data that’s passed through multiple sources, transformations and business processes. Snowflake provides a common environment for analyzing that data while keeping governance, business context and AI-assisted workflows connected to the underlying data.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/product/features/horizon/\"\u003ESnowflake Horizon Catalog\u003C/a\u003E provides governance across the data used for analysis, including data quality monitoring and lineage. With table- and column-level lineage, teams can trace analytical results back through their sources and transformations or identify which downstream assets depend on an upstream field.\u003C/p\u003E\n\u003Cp\u003EFor shared business context, Snowflake Horizon Context brings metrics, definitions, relationships and other metadata into a governed context layer for AI, \u003Ca href=\"https://www.snowflake.com/en/analytics/business-intelligence/\"\u003Ebusiness intelligence\u003C/a\u003E (BI) and applications. Semantic Views store business concepts such as metrics, dimensions and entity relationships directly in Snowflake, while Horizon Context can also incorporate context from BI systems and other sources. The goal is to reduce the number of places where teams independently recreate the logic behind the same metric.\u003C/p\u003E\n\u003Cp\u003EAI-assisted analysis builds on that context. \u003Ca href=\"https://www.snowflake.com/en/developers/guides/getting-started-with-cortex-agents/\"\u003ECortex Agents\u003C/a\u003E can answer natural-language questions over structured data using semantic views, while also working across unstructured sources and supporting multistep analytical tasks.\u003C/p\u003E\n\u003Cp\u003EFor business users, \u003Ca href=\"https://www.snowflake.com/en/product/snowflake-cowork/\"\u003ESnowflake CoWork\u003C/a\u003E provides a conversational interface for exploring enterprise data, asking follow-up questions and working with the resulting charts, tables and reports. CoWork, formerly Snowflake Intelligence, can also perform deeper research across enterprise information and preserve analytical outputs as reusable artifacts.\u003C/p\u003E\n\u003Cp\u003EAnalysts working directly in SQL can also use Cortex AI Functions to analyze text, images, audio and other unstructured or multimodal content alongside structured data. That extends familiar analytical workflows to information that previously required a separate \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-processing/\"\u003Edata processing\u003C/a\u003E pipeline.\u003C/p\u003E\n\u003Cp\u003ETogether, these capabilities connect governed data, traceable lineage, shared business definitions and AI-assisted analysis grounded in the same underlying context.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_from-data-to-a-defensible-decision":{"id":"title-v2-6cfd3e1f52","additionalClasses":"anchor-title anchor-title--from-data-to-a-defensible-decision","type":"heading2","lines":["From data to a defensible decision"],":type":"snowflake-site/components/title-v2"},"text_from-data-to-a-defensible-decision_0":{"id":"text-dd7b4dc995","text":"\u003Cp\u003EData analysis helps organizations move from a question to evidence they can use. 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