数据源描述生成助手

Below is a concise overview of common user-behavior data sources and how they’re typically captured in a BI context.

• Web analytics: First-party clickstream from websites (page views, clicks, scrolls, referrers, UTMs) via tags/SDKs or server-side tagging (e.g., GA4, Adobe Analytics, Snowplow).
• Mobile app analytics: Screen views, taps, app lifecycle, installs/attribution via SDKs (e.g., Firebase, Segment, AppsFlyer, Adjust).
• Product telemetry: Feature usage and in-app events instrumented in clients/servers and streamed via logs or event buses (e.g., Kafka/Kinesis).
• Server/API logs: Authentication, endpoint usage, errors, latency from web servers, CDNs, gateways (e.g., NGINX, CloudFront, API Gateway).
• Marketing and ad platforms: Impressions, clicks, conversions, costs, audience data from ad networks/DSPs; email/SMS engagement (opens, clicks, bounces) from ESPs/marketing automation (e.g., Google Ads, Meta, Braze, Iterable).
• CRM/CDP: Profile attributes, consent status, lifecycle events, and unified identities (e.g., Salesforce, HubSpot, Segment, mParticle).
• Commerce and payments: Browse-to-purchase funnel events (view, add-to-cart, checkout, purchase), refunds, from e-commerce platforms and payment gateways.
• Search and recommendations: On-site search queries, results, clicks, recommendation exposures and engagements.
• Support and success: Tickets, chat transcripts, call center IVR events, CSAT/NPS from tools like Zendesk, Intercom, Salesforce Service Cloud.
• Experimentation platforms: Variant exposures and outcomes for A/B and multivariate tests (e.g., Optimizely, LaunchDarkly).
• Session replay/UX diagnostics: Heatmaps, session replays, frustration signals (e.g., FullStory, Hotjar), with strict masking/anonymization.
• Offline and in-store: POS interactions, loyalty scans, footfall counters, Wi‑Fi analytics, beacons; kiosk/app usage.
• IoT/connected devices: Device usage telemetry and status events.
• Social/community: Engagement on owned channels (comments, reactions, shares).
• Third-party/panel data: Modeled audience and benchmark datasets (use carefully and compliantly).

Integration notes:

• Collection: Client SDKs, tracking pixels, tag managers, server-side tagging, log shipping, event streaming.
• Storage: Land in a data lake/warehouse (e.g., BigQuery, Snowflake, Databricks) via ELT/ETL; maintain event schemas and versioning.
• Identity and privacy: Consent management, PII minimization, cross-device ID resolution, deduplication, and compliance with GDPR/CCPA.
• Quality and governance: Enforce event naming conventions, required properties, monitoring for drop-offs, and clear data lineage.

以下为主数据清单常见数据源的简要梳理，按来源类型与主数据域组织，便于在清单中标注“权威/参考/补充”关系。

• 内部权威源系统

  • 客户/合作伙伴：CRM、订单/计费系统、客服/工单、会员/营销平台、渠道/电商平台
  • 供应商：ERP采购、SRM、合同与合规系统、应付/付款系统
  • 产品/物料：ERP物料主数据、PLM/PDM、WMS/MES、定价与目录系统
  • 员工/组织：HRIS/HCM、身份与目录服务（IAM/AD）、组织架构系统
  • 财务与科目：总账/科目表（GL/COA）、预算与合并系统
  • 资产/地点：EAM/CMMS、门店/网点系统、GIS

• 外部权威与参考数据

  • 标准与编码：GS1/GTIN、ISO代码（国家/货币/语言等）、UN/LOCODE、行业分类与税则/海关编码
  • 主体与合规：企业工商/税务登记库、信用评级、制裁/黑名单/PEP清单
  • 地址与地理：地址标准化与地理编码服务、邮编库
  • 金融与市场：汇率与基准利率、银行识别码、节假日/交易日历

• 文件与集成渠道

  • 伙伴/供应商目录与价目：EDI/IDoc/cXML、CSV/Excel模板
  • 平台与物流API：电商、市场、物流承运商；ESB/MQ/iPaaS、SFTP数据落地

• 交易系统候选来源

  • POS、电商、计费系统中首次出现的新客户/产品/地点，作为主数据候选项进入准入流程

实施要点（用于清单补充）

• 为每个域与字段标注权威源与参考源，避免多头维护
• 记录接口方式、刷新频率、覆盖字段、数据粒度、数据所有者与质量规则
• 对外部源建立版本与溯源机制，确保可追踪与合规可审计