数据分析必备指南

TABLE OF CONTENTSIntroduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3The Power of the Cloud Data and AI Platform. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6Leveraging Cloud Data and AI Platforms for Advanced Analytics and Collaboration. . . . . . . . . . . . . . . . . . . . . . . . . . . .12Transitioning to the Cloud and Scaling for the Future. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .13Faster Analytics and a More Intelligent Future with the Snowflake AI Data Cloud. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15 INTRODUCTIONOrganizations today have an unprecedentedamount of data filled with business insights.Analytics is critical for organizationsto staycompetitive, relevant and ready for theinnovation that the age of AI is bringing.Those who fail to embrace analytics risk fallingbehind competitors, missing crucial marketopportunities, and making uninformed decisionsthat could prove costly. As AI continues to transformindustries, analytics serves as the foundation forunderstanding and effectively leveraging thesetechnological advancements.In the age of data and AI, analytics is no longera luxury but a necessity.This guide will explore the benefits of analytics,the underlying architecture required to unlock itstrue value, data migrations, how to ensure yourorganization is ready for the future, and so muchmore. But first, let’s define a few terms to ensurewe’re all on the same page. WHY ANALYTICS MATTERThe benefits of analytics are far-reaching and transformativefor organizations. It helps them make more informed decisionsbased on data rather than intuition, leading to better outcomesand reduced risk.Analytics enables deeper customer understanding, allowingcompanies to personalize their offerings and improve customersatisfaction. Operational efficiency improves as analyticsidentifies bottlenecks and opportunities for optimization,while real-time monitoring of key performance indicatorsensures organizations can quickly respond to changingconditions.It also gives organizations a competitive advantagethrough market insights and trend identification. It supportsinnovation by uncovering new opportunities and patternsthat might otherwise go unnoticed.Cost savings emerge through optimized resource allocationand waste reduction, while compliance monitoring becomesmore efficient and effective. Perhaps most importantly,analytics enables strategic planning by providing the insightsnecessary for setting and achieving long-term goals, ensuringorganizations remain competitive and resilient in an increasinglydata-driven world. DEFINITIONAnalytics, at its core, is the systematic computational analysisof data or statistics that enables organizations to discover,interpret and communicate meaningful patterns in their data.While the specific applications are vast, analytical endeavorstypically fall into these primary categories:•Descriptive analyticsexplains what has already happened,providing insights into past events and performance•Predictive analyticsforecasts future trends and outcomes,enabling proactive decision-making•Prescriptive analyticsrecommends specific actionsto optimize results, guiding organizations towarddesired objectivesThese different types of analytics work together to provide acomprehensive view of an organization’s operations, customerbehavior and market position. However, to truly harness thesebenefits, organizations require an underlying architecturecapable of efficiently storing and managing their data. 2. Data LakeA data lake is a centralized repository that stores raw,unprocessed data in its native format, without any predefinedschema or structure. A data lake is designed to store largeamounts of data, including structured, semi-structured andunstructured data, from various sources.Analytics plays a crucial role in extracting insights from thedata lake, but the relationship is more complex than with adata warehouse:•Data exploration:Analytics tools are used to explore anddiscover patterns, relationships and insights from the rawdata in a data lake.•Data preparation:Analytics tools help prepare the data foranalysis by cleaning, transforming and structuring the data.•Data analysis:Analytics tools, such as machine learning,natural language processing and data visualization, are usedto analyze the data and extract insights.•Data refining:Analytics helps refine the data by identifyingdata quality issues, handling missing values and creatingdata pipelines.A data lake offers a flexible and scalable platform for analytics,allowing the analysis of large amount