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callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EANALYTICS DEFINED\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EAnalytics is the systematic use of data to monitor performance, explain changes, estimate future outcomes and guide business decisions.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text__0":{"id":"text-219e3f79eb","text":"\u003Cp\u003EAnalytics has moved well beyond dashboards and periodic reports. Today, teams have access to predictive models, real-time analytics and conversational interfaces that let people ask questions of data using natural language.\u003C/p\u003E\n\u003Cp\u003EOrganizations are already putting these capabilities to work. In \u003Ca href=\"https://www.snowflake.com/en/lp/radical-roi-generative-ai-short-form/\"\u003E2026 Snowflake-sponsored Omdia research\u003C/a\u003E, 59% of data analytics teams said they were using \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/generative-ai/\"\u003Egenerative AI\u003C/a\u003E. At the same time, 96% of respondents said they still faced significant challenges putting generative AI into practice.\u003C/p\u003E\n\u003Cp\u003EThe disconnect between those two numbers reveals something important about modern analytics: New ways of working with data can make insights faster and more accessible, but they don’t remove the need for a strong data foundation. Reliable answers still depend on trustworthy data, consistent business definitions, the right level of freshness and an analytical approach suited to the decision being made.\u003C/p\u003E\n\u003Cp\u003EAs analytics continues to expand, the challenge is to connect tools to shared data, business context and \u003Ca href=\"https://www.snowflake.com/en/data-governance/\"\u003Edata governance\u003C/a\u003E so that different analytical workloads can produce insight people can actually use.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-analytics":{"id":"title-v2-da7419ff2a","additionalClasses":"anchor-title anchor-title--what-is-analytics","type":"heading2","lines":["What is analytics?"],":type":"snowflake-site/components/title-v2"},"text_what-is-analytics_0":{"id":"text-fcc5a548b7","text":"\u003Cp\u003EAnalytics uses data to measure performance, investigate changes, forecast outcomes and guide decisions. It encompasses the recurring work of preparing and modeling data, applying analytical methods and delivering results through reports, dashboards, applications and other interfaces.\u003C/p\u003E\n\u003Cp\u003EThe right analytical approach depends on the question being asked and how quickly the answer is needed. Descriptive and diagnostic analysis help teams understand past performance and investigate why it changed, while predictive and prescriptive methods help anticipate future outcomes and evaluate possible actions. Business intelligence (BI) gives users a recurring way to monitor and explore defined metrics, while real-time analytics supports decisions that depend on fresher data.\u003C/p\u003E\n\u003Cp\u003EIncreasingly, business questions are being explored through AI-assisted analytics interfaces. Natural-language querying, generated explanations and conversational follow-up can give users an easier, more flexible way to move through an analysis.\u003C/p\u003E\n\u003Cp\u003ETeams use various analytics approaches to track performance over time, investigate new questions as conditions change and apply the results to operational and strategic decisions.\u003C/p\u003E\n\u003Cp\u003E\u003Cem\u003EWatch a live demo of how Snowflake Interactive Analytics powers real-time analytics for APIs, apps and AI agents:\u003C/em\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"yt_what-is-analytics_0":{"id":"embed-465b2a20d0","youtubeVideoId":"VKnz7vMEBoY","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_analytics-vs-data-analysis-whats-the-difference":{"id":"title-v2-08a9555420","additionalClasses":"anchor-title anchor-title--analytics-vs-data-analysis-whats-the-difference","type":"heading2","lines":["Analytics vs. data analysis: What’s the difference?"],":type":"snowflake-site/components/title-v2"},"text_analytics-vs-data-analysis-whats-the-difference_0":{"id":"text-46950e83f6","text":"\u003Cp\u003EThe terms \u003Ci\u003Eanalytics\u003C/i\u003E and \u003Ci\u003Edata analysis\u003C/i\u003E overlap considerably in everyday use, and practitioners don’t always draw the boundary in the same place. Generally, however, data analysis refers to a specific activity, while analytics describes the broader capability that allows organizations to perform that work repeatedly and consistently.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EData analysis\u003C/b\u003E involves examining data to answer a particular question. An analyst might investigate why customer churn increased last quarter, compare conversion rates across campaigns or determine which products generated the largest margin decline.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EAnalytics\u003C/b\u003E includes the systems, processes and practices that make that work repeatable. It encompasses the \u003Ca href=\"https://www.snowflake.com/en/fundamentals/modernizing-data-pipelines/\"\u003Edata pipelines\u003C/a\u003E that supply information, the metric definitions that keep calculations consistent, the analytical methods used to investigate questions, the tools through which people access results and the governance required to maintain them over time.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EReporting overlaps with analytics, particularly in business intelligence, but typically focuses on presenting defined metrics and results. Analytics extends further into investigation, explanation, prediction and decision support. In practice, dashboards often include analytical functions, while analytics workflows frequently end in a report or visualization.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_the-four-types-of-analytics-descriptive-diagnostic-predictive-and-prescriptive":{"id":"title-v2-7ece895f59","additionalClasses":"anchor-title anchor-title--the-four-types-of-analytics-descriptive-diagnostic-predictive-and-prescriptive","type":"heading2","lines":["The four types of analytics: descriptive, diagnostic, predictive and prescriptive"],":type":"snowflake-site/components/title-v2"},"text_the-four-types-of-analytics-descriptive-diagnostic-predictive-and-prescriptive_0":{"id":"text-7c12833252","text":"\u003Cp\u003EOne common framework groups analytics into four types according to the question being asked.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003EDescriptive analytics\u003C/strong\u003E summarizes what has already happened. Revenue reports, website traffic trends, inventory levels and historical customer activity all fall into this category.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EDiagnostic analytics\u003C/strong\u003E investigates the factors associated with an observed result. After identifying a decline in sales, for example, an analyst might segment the data by region, product or customer cohort to understand where the change occurred and what contributed to it.\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/predictive-analytics/\"\u003E\u003Cstrong\u003EPredictive analytics\u003C/strong\u003E\u003C/a\u003E uses historical data and statistical or \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/\"\u003Emachine learning\u003C/a\u003E methods to estimate future outcomes, such as demand, churn risk or equipment failure.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EPrescriptive analytics\u003C/strong\u003E evaluates possible actions in light of objectives, constraints and expected outcomes. Optimization models, simulations and decision rules frequently support this work.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EAcross all four types, trustworthy data and stable business definitions provide the analytical foundation.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_the-four-types-of-analytics-descriptive-diagnostic-predictive-and-prescriptive_0":{"id":"card-v2-7c8dde8d50","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["Rinascente"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/customers/all-customers/case-study/la-rinascente/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Read the full case study"},"image":{"id":"image","height":"351","lazyEnabled":true,"alt":"Rinascente","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--655e98d4-be01-4190-81ad-f911846ab684/rinascente%25403x.png?preferwebp=true&quality=85","isLcpImage":false,"width":"624",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003EItalian department store Rinascente uses Snowflake to unify customer data across its loyalty program, communications and service channels, giving business teams a near real-time 360-degree view of how customers interact with the brand. Data tables refresh every 15 minutes, while the retailer’s heaviest customer reports that previously took an hour or more now run in minutes, cutting processing time by about 80% [as of Jan. 2025].\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal"},"title_how-an-analytics-workflow-actually-runs":{"id":"title-v2-23e75d011d","additionalClasses":"anchor-title anchor-title--how-an-analytics-workflow-actually-runs","type":"heading2","lines":["How an analytics workflow actually runs"],":type":"snowflake-site/components/title-v2"},"text_how-an-analytics-workflow-actually-runs_0":{"id":"text-be5aa28cd1","text":"\u003Cp\u003EAnalytics often starts before anyone opens a BI tool or writes a SQL query. The first task is deciding what questions need answers and how those answers will influence decisions.\u003C/p\u003E\n\u003Cp\u003EA practical workflow usually includes five connected stages:\u003C/p\u003E\n\u003Col\u003E\n\u003Cli\u003E\u003Cstrong\u003EDefine the objective:\u003C/strong\u003E Establish the question, decision or outcome the analysis needs to support. “Why are sales down?” might turn into an investigation of whether lower transaction volume, pricing changes or product mix explain a year-over-year decline in a particular market. The objective determines which data is relevant, how fresh the result needs to be and what level of uncertainty the decision will tolerate.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003ECollect the relevant data:\u003C/strong\u003E Depending on the question, the required information might include structured tables, semi-structured records or unstructured data such as documents and support transcripts. The source also shapes expectations around freshness, completeness and access.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EPrepare the data:\u003C/strong\u003E Data processing brings those sources into usable form through activities such as cleaning, transformation, joining and validation. At this stage, teams also need to understand whether fields contain the values analysts think they contain.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EAnalyze and interpret:\u003C/strong\u003E Analysts apply queries, statistical methods, visualizations, models or other analytical techniques, then interpret the result in its business context. A mathematically correct result is still easy to misread when definitions, populations or assumptions are unclear.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EConnect the result to a decision or action:\u003C/strong\u003E Some analyses inform an immediate operational choice; others influence planning, refine a hypothesis or rule out an option. Either way, the result needs a destination beyond the analysis itself.\u003C/li\u003E\n\u003C/ol\u003E\n\u003Cp\u003EReal analytical work tends to loop through these stages. An unexpected result sends an analyst back to inspect source data, revise a definition or ask a narrower question, and a new decision creates new data, which later feeds another round of analysis.\u003C/p\u003E\n\u003Cp\u003E“Ultimately, value shows up in decision velocity and organizational alignment,” says \u003Ca href=\"https://www.snowflake.com/en/blog/authors/josh-klahr/\"\u003EJosh Klahr\u003C/a\u003E, Snowflake’s Head of Product Management for AI-Powered BI. “The test is whether cross-functional teams spend less time debating whose metrics are right and more time making decisions from agreed business logic.”\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"quote_how-an-analytics-workflow-actually-runs_0":{"id":"quote-item-5273e22150","alignment":"left","layout":"simple","quoteSourceName":"Josh Klahr","quoteSourceTitle":"Head of Product Management for AI-Powered BI, Snowflake","showQuoteIcon":true,"quote":"Ultimately, value [in analytics] shows up in decision velocity and organizational alignment. The test is whether cross-functional teams spend less time debating whose metrics are right and more time making decisions from agreed business logic.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"title_why-analytics-depends-on-data-quality-and-a-shared-semantic-layer":{"id":"title-v2-d1d33c9010","additionalClasses":"anchor-title anchor-title--why-analytics-depends-on-data-quality-and-a-shared-semantic-layer","type":"heading2","lines":["Why analytics depends on data quality and a shared semantic layer"],":type":"snowflake-site/components/title-v2"},"text_why-analytics-depends-on-data-quality-and-a-shared-semantic-layer_0":{"id":"text-2e1cddfbab","text":"\u003Cp\u003EAn analytical result inherits the characteristics of the data underneath it. Missing records, stale values, inconsistent identifiers or incorrect field mappings all affect what the analysis represents, even when the query or model operates exactly as designed.\u003C/p\u003E\n\u003Cp\u003ETeams typically assess \u003Ca href=\"https://www.snowflake.com/en/fundamentals/a-practical-guide-to-data-quality/\"\u003Edata quality\u003C/a\u003E across several dimensions:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003EAccuracy:\u003C/strong\u003E Whether values correctly represent the underlying event or entity\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003ECompleteness:\u003C/strong\u003E Whether required values and records are present\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EConsistency:\u003C/strong\u003E Whether the same information agrees across systems and data sets\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EFreshness:\u003C/strong\u003E Whether the data is current enough for the decision it supports\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EValidity:\u003C/strong\u003E Whether values conform to expected formats, ranges and rules\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EUniqueness:\u003C/strong\u003E Whether duplicate records distort counts or other calculations\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EBefore defining rules for those dimensions, teams need to profile the current data and understand its actual distributions, null rates, formats and relationships. A freshness threshold that works for quarterly financial planning, for example, would be inappropriate for fraud detection.\u003C/p\u003E\n\u003Cp\u003EData quality alone doesn’t guarantee analytical consistency, however. Two teams might work from perfectly valid records but report different revenue figures if they define annual recurring revenue differently, for example. One dashboard might include expansion revenue immediately, another might wait until a contract start date, and a third might exclude a particular product category. Without an agreed definition, all three calculations could be technically correct while producing different answers to the same business question.\u003C/p\u003E\n\u003Cp\u003EA semantic layer addresses that problem. Rajhans Samdani, Principal Software Engineer at Snowflake, \u003Ca href=\"https://www.snowflake.com/en/blog/engineering/why-you-need-a-semantic-model/\"\u003Eexplains\u003C/a\u003E: “Semantic models are the shared interface that lets BI dashboards and AI agents operate on the same definitions.” Metric definitions, entity relationships and other semantic context give analysts, dashboards, applications and \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/\"\u003EAI systems\u003C/a\u003E a common interpretation of the underlying data.\u003C/p\u003E\n\u003Cp\u003EA semantic layer is especially crucial for natural-language interfaces. When definitions disagree, the system can reproduce misaligned answers at greater scale, and it can be more challenging to identify the problem. Semantic consistency is playing a larger operational role as more users interact with data through conversational and agentic systems.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_why-analytics-depends-on-data-quality-and-a-shared-semantic-layer_0":{"id":"text-f211c35c3f","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003ECOMMON PITFALL\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EA common mistake is assuming a technically correct calculation guarantees a useful answer. Two teams can apply valid logic to the same data and still produce conflicting results if they define terms differently.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_business-intelligence-reporting-and-dashboards-for-the-whole-business":{"id":"title-v2-3e8f8babaf","additionalClasses":"anchor-title anchor-title--business-intelligence-reporting-and-dashboards-for-the-whole-business","type":"heading2","lines":["Business intelligence: Reporting and dashboards for the whole business"],":type":"snowflake-site/components/title-v2"},"text_business-intelligence-reporting-and-dashboards-for-the-whole-business_0":{"id":"text-8c73a2dcf1","text":"\u003Cp\u003EBusiness intelligence sits within the broader analytics discipline, concentrating heavily on reporting, dashboards, visualization and access to recurring business metrics.\u003C/p\u003E\n\u003Cp\u003EA BI environment might give a finance team a monthly revenue dashboard, provide sales leaders with pipeline metrics and let operations teams explore inventory trends. Because those questions recur, teams generally define the underlying metrics and data models in advance, giving business users a consistent way to inspect performance without rebuilding each analysis from scratch.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/natural-language-processing/large-language-models/\"\u003ELarge language models\u003C/a\u003E (LLMs) and generative AI give users new ways to query and explore analytical data. Alongside filters and dashboards, users can interact with business data through natural-language questions, generated visualizations and conversational follow-up.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_real-time-analytics-acting-on-data-as-it-arrives":{"id":"title-v2-7b0c61259b","additionalClasses":"anchor-title anchor-title--real-time-analytics-acting-on-data-as-it-arrives","type":"heading2","lines":["Real-time analytics: Acting on data as it arrives"],":type":"snowflake-site/components/title-v2"},"text_real-time-analytics-acting-on-data-as-it-arrives_0":{"id":"text-59af0d08a7","text":"\u003Cp\u003EReal-time analytics is often framed around latency: milliseconds, seconds or minutes from an event occurring to its appearance in an analytical result. Whether or not a use case requires real-time analytics and what the appropriate latency target is, however, depends on the decision the result supports. If a five-minute-old answer leads to the same decision as a five-second-old one, reducing latency further provides little practical value.\u003C/p\u003E\n\u003Cp\u003ETighter freshness requirements generally demand continuously updated data, low-latency processing and analytical infrastructure designed to handle queries while new records are still being ingested.\u003C/p\u003E\n\u003Cp\u003ETo enable real-time analytics, the system has to manage both latency and concurrency: data needs to reach the analytical layer quickly, while many users or applications query continuously changing information without creating unacceptable delays.\u003C/p\u003E\n\u003Cp\u003EFor workloads whose decisions tolerate older data, batch processing remains simpler and often less expensive.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_ai-analytics-from-predefined-paths-to-interactive-analysis":{"id":"title-v2-55b6d02686","additionalClasses":"anchor-title anchor-title--ai-analytics-from-predefined-paths-to-interactive-analysis","type":"heading2","lines":["AI analytics: From predefined paths to interactive analysis"],":type":"snowflake-site/components/title-v2"},"text_ai-analytics-from-predefined-paths-to-interactive-analysis_0":{"id":"text-fdc007a3bc","text":"\u003Cp\u003ESelf-service analytics moved a lot of analytical work closer to business users by giving them governed dashboards, drag-and-drop exploration, filters and ad hoc querying. AI has significantly expanded self-service capabilities by letting users formulate questions in natural language and continue the analysis conversationally.\u003C/p\u003E\n\u003Cp\u003EGenerative AI supports much of the experience through text-to-SQL, generated explanations and conversational querying, while machine learning supports predictive and prescriptive methods that handle tasks such as forecasting outcomes and evaluating possible actions.\u003C/p\u003E\n\u003Cp\u003EAI can also assist analysts with tasks such as identifying patterns, generating explanations and summarizing results — capabilities often grouped under augmented analytics.\u003C/p\u003E\n\u003Cp\u003EAs more of the analytical path moves from predefined navigation to generated queries and multi-step workflows, semantic context and governance carry more of the burden of keeping results aligned with business definitions.\u003C/p\u003E\n\u003Cp\u003E“Introducing conversational AI changes the work from designing dashboards and reports to managing context,” says Klahr. “The system has to understand the user’s intent and apply the right business definitions and analytical rules to the question being asked.”\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_analytics-across-industries":{"id":"title-v2-d2f234f7f6","additionalClasses":"anchor-title anchor-title--analytics-across-industries","type":"heading2","lines":["Analytics across industries"],":type":"snowflake-site/components/title-v2"},"text_analytics-across-industries_0":{"id":"text-3c9503eb80","text":"\u003Cp\u003EIndustry changes the context around analytics more than the underlying analytical methods. The important differences usually show up in the data being analyzed, the freshness required for the decision and the governance or regulatory constraints surrounding its use.\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EIndustry\u003C/th\u003E\u003Cth\u003EHow analytics is applied\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cstrong\u003ERetail\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003ERetailers analyze demand, inventory, pricing, promotions, customer behavior and supply chain performance. Some decisions, such as replenishment planning, tolerate longer analytical windows; digital inventory availability and other operational scenarios require much fresher data.\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cstrong\u003EHealthcare\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003EHealthcare organizations analyze patient operations, resource utilization, research data and financial performance. Privacy, security and governance requirements shape who can access the underlying data and for which purposes.\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cstrong\u003EFinancial services\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003EFinancial institutions use analytics for risk, fraud, customer activity, portfolio performance and regulatory reporting. Transaction volume and time-sensitive decisions often place significant requirements on freshness and concurrency.\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cstrong\u003EManufacturing\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003EManufacturers connect production, equipment, supply chain, quality and financial data to monitor operations, forecast demand and identify conditions associated with downtime or defects.\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_analytics-tools-and-platforms":{"id":"title-v2-bf6d73770d","additionalClasses":"anchor-title anchor-title--analytics-tools-and-platforms","type":"heading2","lines":["Analytics tools and platforms"],":type":"snowflake-site/components/title-v2"},"text_analytics-tools-and-platforms_0":{"id":"text-d877798c41","text":"\u003Cp\u003EAn analytics environment needs several capabilities across the path from raw data to an analytical result. Data has to be ingested and transformed, stored and processed, interpreted through consistent business definitions and made available through interfaces suited to different users and workloads.\u003C/p\u003E\n\u003Cp\u003EFor analytics specifically, those capabilities commonly include:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003EStorage and compute:\u003C/strong\u003E Process the structured, semi-structured and unstructured data used for analysis.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EData ingestion and transformation:\u003C/strong\u003E Bring source data into the analytical environment and prepare it for use.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003ESemantic and metrics capabilities:\u003C/strong\u003E Define shared business concepts, measures and relationships.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EBI and visualization:\u003C/strong\u003E Support dashboards, recurring reporting and interactive exploration.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003ENotebooks and code environments:\u003C/strong\u003E Support statistical analysis, machine learning and more customized analytical workflows.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EConversational interfaces:\u003C/strong\u003E Let users ask analytical questions in natural language and continue an investigation through follow-up questions.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EIdeally, these capabilities will be located within the AI data platform. When the platform can support multiple analytics workloads against the same governed data, teams can reduce the copies, pipelines and business logic they would otherwise have to recreate as data moves between systems.\u003C/p\u003E\n\u003Cp\u003EA platform needs enough flexibility to support compute, latency and concurrency needs while preserving shared data, definitions and governance across them.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_getting-started-with-analytics":{"id":"title-v2-117ba76a90","additionalClasses":"anchor-title anchor-title--getting-started-with-analytics","type":"heading2","lines":["Getting started with analytics"],":type":"snowflake-site/components/title-v2"},"text_getting-started-with-analytics_0":{"id":"text-25b1d092fe","text":"\u003Cp\u003EBuilding an analytics capability is less about following a fixed maturity model than about making a few foundational decisions in the right order. Teams need to know which questions they’re supporting, how the underlying metrics are defined, what freshness and processing those decisions require, and how people will access the results.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003EStart with the decision and the measures behind it:\u003C/strong\u003E Identify the questions people need to answer, the metrics those questions depend on and how quickly the underlying information needs to reflect changes in the business. A recurring performance report, a demand forecast and an operational decision each impose different requirements on the analytical environment.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EDefine important metrics early:\u003C/strong\u003E If revenue, churn or active customer means something different from one team or analytical workflow to another, adding dashboards, models or conversational interfaces multiplies the reconciliation work later. Clear ownership also gives teams a way to manage changes to those definitions over time.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EMatch the data and processing requirements to the workload:\u003C/strong\u003E Scheduled updates may be sufficient for recurring BI, while operational analytics can require continuously processed data and tighter latency. Predictive workloads introduce additional requirements around historical coverage, feature quality and whether past data still represents current conditions.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EExpand analytical access according to how people work:\u003C/strong\u003E BI and governed self-service support recurring exploration for business users; notebooks and code environments serve more specialized analytical work; conversational interfaces give users another way to express questions in business language. These access patterns work best when they draw from shared governed data and business context rather than recreating them independently.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EBuild data literacy and governance alongside access:\u003C/strong\u003E Users need enough context to understand what a metric represents, which population it describes and where uncertainty enters the result. Governance provides the corresponding controls around access, ownership, definitions and traceability.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EOrganizations will assemble these capabilities in different orders. Keeping the data and business context shared across them reduces the amount of infrastructure and analytical logic that has to be rebuilt as new workloads are added.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_getting-started-with-analytics_0":{"id":"text-4aa8e0e621","additionalClasses":"callout callout--tip","text":"\u003Cp\u003E\u003Cstrong\u003EQUICK TIP\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EStart with the decision you need to support, then work backward to the metric, data freshness and analytical method it requires. This keeps technology choices tied to an actual business requirement.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_how-snowflake-supports-modern-analytics":{"id":"title-v2-33c60c9c92","additionalClasses":"anchor-title anchor-title--how-snowflake-supports-modern-analytics","type":"heading2","lines":["How Snowflake supports modern analytics"],":type":"snowflake-site/components/title-v2"},"text_how-snowflake-supports-modern-analytics_0":{"id":"text-b361d7b2a3","text":"\u003Cp\u003ESnowflake supports analytical workloads on a shared governed foundation, so teams can work across BI, real-time analytics, machine learning and AI-assisted querying without maintaining a separate data environment for each use case.\u003C/p\u003E\n\u003Cp\u003EFor BI and interactive analysis, Snowflake provides elastic compute and concurrency for many users working against the same underlying data. Real-time and operational analytics can work with continuously updated data, while data science and machine learning workflows use the same governed foundation for feature preparation, experimentation and model development.\u003C/p\u003E\n\u003Cp\u003ESemantic capabilities add shared business context across those workloads. By defining metrics, dimensions and relationships centrally, organizations can keep dashboards, applications and AI-assisted interfaces aligned on the same business definitions rather than recreating that logic independently.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/product/features/cortex/\"\u003ESnowflake Cortex AI\u003C/a\u003E extends that foundation into conversational and generative analytics, while \u003Ca href=\"https://www.snowflake.com/en/product/features/horizon/\"\u003ESnowflake Horizon\u003C/a\u003E applies governance, security, discovery and compliance controls across data, applications and AI. The result is an analytics environment in which different workloads can use the processing and interfaces they require while remaining connected to shared data and governance.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_where-analytics-goes-next":{"id":"title-v2-47ab71f85f","additionalClasses":"anchor-title anchor-title--where-analytics-goes-next","type":"heading2","lines":["Where analytics goes next"],":type":"snowflake-site/components/title-v2"},"text_where-analytics-goes-next_0":{"id":"text-9b7133dfbe","text":"\u003Cp\u003EBusiness users will increasingly expect to move between recurring metrics, ad hoc investigation, forecasts and conversational queries without having to understand which pipeline, model or tool sits underneath each result. This raises the standard for the data foundation: definitions have to travel with the data, governance has to follow the workload and freshness has to match the decision.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_where-analytics-goes-next_0":{"id":"text-856804412c","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EKEY TAKEAWAY\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EModern analytics works best when different workloads draw from a common governed data foundation rather than recreating pipelines, copies and business logic for each use case.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular 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