What Is Unstructured Data Discovery?什么是非结构化数据发现？
For the full topic map and the neighboring methods that support this workflow, continue with the unstructured data processing and document intelligence guide.
如需查看完整主题结构以及支撑本流程的相邻方法，请继续阅读非结构化数据处理与文档智能指南。
Unstructured data discovery is the controlled process of identifying, inventorying, sampling, classifying and reconciling content across approved repositories. It covers documents, messages, images, audio and video, and it records not only what was found but also what could not be accessed, parsed, classified or verified.
非结构化数据发现是一项受控流程，用于在获批存储库中识别、盘点、抽样、分类并对账内容。它覆盖文档、消息、图像、音频和视频，不仅记录发现了什么，也记录哪些对象无法访问、解析、分类或验证。
A useful discovery result is more than a file list. It connects a stable source identity to location, version, owner, access context, format, timestamps, inspection status, quality warnings and any evidence-backed labels. The deliverables normally include a source map, asset inventory, coverage report, exception queue and prioritized risk or utility backlog.
有用的发现结果不只是一份文件列表。它把稳定来源身份与位置、版本、所有者、访问语境、格式、时间戳、检查状态、质量警告及有证据支撑的标签连接起来。常见交付物包括来源地图、资产清单、覆盖率报告、异常队列，以及按优先级排列的风险或价值待办。
Quick answer: define the decision and authorized scope; enumerate sources with metadata first; resolve stable identities, versions and duplicates; inspect representative content; evaluate parsing and classification; confirm ownership and access; reconcile source totals against every scan outcome; then monitor changes and retest blind spots.
快速回答：先定义决策与获批范围；优先用元数据列举来源；解析稳定身份、版本和重复项；检查代表性内容；评估解析与分类；确认所有者和访问权限；把来源总数与每种扫描结果对账；最后持续监控变化并重新测试盲区。
A Repeatable Unstructured Data Discovery Workflow可重复执行的非结构化数据发现工作流
- Define the question and authorized boundary定义问题与获批边界
State the decision, repositories, identities, exclusions, time range, copy rules and evidence needed for action.
明确决策、存储库、身份、排除项、时间范围、复制规则与采取行动所需证据。
- Enumerate metadata before content先列举元数据，再检查内容
Collect object IDs, paths, sizes, versions, timestamps, owners, permissions and content types through authoritative source interfaces.
通过权威来源接口收集对象ID、路径、大小、版本、时间戳、所有者、权限与内容类型。
- Resolve identity, versions and duplicates解析身份、版本与重复项
Assign stable IDs, preserve source identifiers, separate versions from copies and mark exact or probable duplicates without deleting anything.
分配稳定ID，保留来源标识，区分版本与副本，并标记完全或可能重复项，但不执行删除。
- Select representative content safely安全选择代表性内容
Stratify samples by repository, format, age, size, access class, owner and language; add difficult and high-impact cases deliberately.
按存储库、格式、年代、大小、访问级别、所有者和语言分层抽样，并有意加入困难与高影响案例。
- Inspect extraction and classification quality检查提取与分类质量
Test parsing, OCR, transcription or frame sampling separately from labels; keep source coordinates, versions, warnings and uncertainty.
把解析、OCR、转录或帧采样与标签分开测试，并保留来源坐标、版本、警告与不确定性。
- Confirm ownership, rights and business context确认所有权、权利与业务语境
Route ambiguous assets to stewards; do not infer an accountable owner from folder names or recent access alone.
把归属不明资产交给数据管理员，不要仅凭文件夹名称或近期访问推断责任所有者。
- Reconcile coverage and exceptions对账覆盖率与异常
Balance source totals against every terminal outcome, preserve reason codes and rerun failed slices after remediation.
把来源总数与每种最终结果对账，保留原因代码，并在修复后重新运行失败切片。
- Publish controlled outputs and monitor change发布受控输出并监控变化
Release only reviewed inventory and findings, assign action owners, then detect new, changed, moved, deleted and newly inaccessible objects.
只发布已复核清单与发现结果，分配行动负责人，再检测新增、变更、移动、删除与新近不可访问对象。
Test Format Detection Separately From Content Extraction把格式检测与内容提取分开测试
Recognizing an extension or media type does not prove that a tool can extract useful content. The Apache Tika supported-formats documentation explicitly notes that it can detect a wider range of formats than those from which it can extract metadata or text. Discovery reporting should therefore separate detected, readable, parsed, partially parsed, unsupported, encrypted and failed objects.
识别扩展名或媒体类型并不能证明工具可提取有用内容。Apache Tika支持格式文档明确指出，它能检测的格式范围比可提取元数据或文本的格式更广。因此，发现报告应分别统计已检测、可读取、已解析、部分解析、不受支持、已加密与失败对象。
| Modality模态 | Inspect检查内容 | Typical blind spot典型盲区 |
|---|---|---|
| Documents and messages文档与消息 | Reading order, tables, attachments, comments, headers and embedded objects阅读顺序、表格、附件、批注、邮件头与嵌入对象 | Scans, encrypted files and nested archives扫描件、加密文件与嵌套归档 |
| Images图像 | OCR language, layout, orientation, handwriting and confidence by regionOCR语言、版面、方向、手写与区域置信度 | Text detected but coordinates or characters are wrong检测到文字但坐标或字符错误 |
| Audio音频 | Codec, channels, timestamps, speakers, language and low-confidence spans编码、声道、时间戳、说话人、语言与低置信片段 | Silent, noisy or mixed-language segments静音、噪声或混合语言片段 |
| Video视频 | Duration, scene changes, frame cadence, captions and audio alignment时长、场景变化、帧采样频率、字幕与音画对齐 | Events between sampled frames or inaccessible tracks采样帧之间的事件或不可访问轨道 |
How to Measure Unstructured Data Discovery Coverage如何衡量非结构化数据发现覆盖率
Coverage is a reconciliation problem, not a single percentage. Obtain an authoritative source count or bounded listing for the same point in time, then balance it against mutually exclusive outcomes. If source totals change during a long scan, record the snapshot or event boundary used. Report both object counts and relevant bytes or duration; either measure alone can hide a material gap.
覆盖率是对账问题，不是一个单独百分比。应获取同一时间点的权威来源数量或有边界列表，再与互斥结果对账。如果长时间扫描期间来源总数发生变化，应记录使用的快照或事件边界。同时报告对象数与相关字节数或时长；任一单独指标都可能隐藏重要缺口。
- Terminal outcomes: parsed, metadata-only, unsupported, encrypted, permission denied, corrupt, oversized, intentionally excluded or transient failure.最终结果：已解析、仅元数据、不支持、加密、权限拒绝、损坏、超限、故意排除或瞬时失败。
- Slices: repository, connector, format, age, size, owner, access class, region and language.切片：存储库、连接器、格式、年代、大小、所有者、访问级别、区域与语言。
- Canaries: known authorized files placed or selected to prove enumeration, access and inspection paths.金丝雀：已知获授权文件，用于证明列举、访问与检查路径。
- Remediation loop: fix identity, permission, connector or parser causes; rerun affected slices; retain before-and-after evidence.修复循环：修复身份、权限、连接器或解析器原因；重跑受影响切片；保留前后证据。
AWS documents this boundary clearly for a vendor-specific example: Amazon Macie sensitive data discovery distinguishes broad automated discovery from targeted jobs and documents sampling, supported formats, encryption and permission prerequisites. Those constraints are not universal, but they illustrate why a “scan completed” state needs coverage detail.
AWS的特定厂商示例清楚说明了这一边界：Amazon Macie敏感数据发现区分广泛自动发现与定向任务，并记录抽样、支持格式、加密和权限前提。这些限制并非通用要求，但它们说明为什么“扫描完成”状态仍需要覆盖率细节。
Example: Discovering Contract Files Before an AI Pilot示例：AI试点前发现合同文件
Hypothetical example: a procurement team wants to test contract-question answering across an approved collaboration site, a file share and an object-storage prefix. The numbers below are illustrative, not product benchmarks. The team snapshots source listings, records 10,000 expected objects, and runs metadata enumeration. It finds 9,940 objects; 40 are denied and 20 are missing from the connector listing. Those 60 items remain explicit coverage exceptions rather than disappearing from the denominator.
假设示例：某采购团队希望在获批协作站点、文件共享与对象存储前缀中测试合同问答。以下数字仅为示例，并非产品基准。团队对来源列表建立快照，记录10,000个预期对象并运行元数据列举，发现9,940个对象；其中40个被拒绝访问，另有20个未出现在连接器列表中。这60项继续作为明确覆盖异常，而不是从分母中消失。
The team then stratifies a reviewed sample by repository, format, age and access class. It discovers that scanned amendments require OCR, nested attachments are missed by one parser and some copies have different access groups. Exact duplicates are flagged but not deleted. Contract owners review ambiguous records; sensitive-data labels retain page coordinates and rule versions.
随后，团队按存储库、格式、年代与访问级别对已复核样本分层。结果发现扫描版修订需要OCR，一个解析器遗漏嵌套附件，部分副本还具有不同访问组。完全重复项被标记但不删除。合同所有者复核归属不明记录；敏感数据标签保留页码坐标与规则版本。
Only after source totals reconcile, difficult formats pass thresholds and access tests succeed does the team publish an approved subset for downstream analysis. The pilot decision is supported by an inventory, exceptions, sample quality results and owners—not by a single “scan complete” message.
只有在来源总数对账、困难格式达到阈值且访问测试成功后，团队才发布获批子集供下游分析使用。试点决策由清单、异常、样本质量结果与责任人共同支撑，而不是依赖一条“扫描完成”消息。
Use InfiniSynapse After Discovery Controls Are Complete在发现控制完成后使用InfiniSynapse
Prepare an approved, supported set of documents, audio, video or structured context; retain source identifiers and evidence coordinates; remove prohibited content; and confirm access. InfiniSynapse's public site describes multi-source analysis across databases and multimodal analysis across documents, audio and video. That makes it a relevant downstream analysis workspace—not the enterprise discovery control itself.
先准备获批且受支持的文档、音频、视频或结构化语境，保留来源标识与证据坐标，移除禁止内容并确认访问权限。InfiniSynapse官网描述了跨数据库的多源分析，以及跨文档、音频和视频的多模态分析。因此，它适合作为下游分析工作空间，而不是企业数据发现控制本身。
Before opening the tool, prepare supported inputs that have passed scope, rights, extraction and quality checks. Use InfiniSynapse to explore those approved inputs across sources and modalities. Keep crawling, inventory, ownership, classification, permissions, OCR, transcription and lifecycle enforcement in the responsible discovery and governance systems.
打开工具前，准备已通过范围、权利、提取与质量检查的受支持输入。使用InfiniSynapse跨来源与模态探索这些获批输入；企业爬取、清单、所有权、分类、权限、OCR、转录与生命周期执行仍应保留在负责的数据发现和治理系统中。
Analyze approved data with InfiniSynapse使用InfiniSynapse分析获批数据See the public InfiniSynapse capability description and verify current supported sources, formats, deployment and controls for your intended workload before use.
使用前请查看InfiniSynapse公开能力说明，并针对预期工作负载验证当前支持的来源、格式、部署与控制。
Unstructured Data Discovery FAQ非结构化数据发现常见问题
What is unstructured data discovery?
什么是非结构化数据发现？
Unstructured data discovery is the controlled process of identifying, inventorying, sampling, classifying and reconciling documents, messages, images, audio and video across approved repositories. It produces an evidence-backed map of what exists, where it resides, who can access it, how reliably it can be inspected and which gaps require action.
非结构化数据发现是一项受控流程，用于在获批存储库中识别、盘点、抽样、分类并对账文档、消息、图像、音频与视频。它生成有证据支撑的数据地图，说明有哪些数据、位于何处、谁可访问、能否可靠检查，以及哪些缺口需要处理。
How is unstructured data discovery different from enterprise search?
非结构化数据发现与企业搜索有何不同？
Enterprise search retrieves items from a known and indexed scope. Discovery must also find unknown repositories, enumerate assets that cannot be parsed, record inaccessible or excluded objects, reconcile source totals and expose coverage gaps. Search may be a downstream capability, but a useful search result does not prove that the data estate was discovered completely.
企业搜索从已知且已建立索引的范围中检索项目。数据发现还必须找到未知存储库，列出无法解析的资产，记录不可访问或被排除的对象，与来源总数对账并暴露覆盖缺口。搜索可以是下游能力，但有用的搜索结果不能证明数据资产已经被完整发现。
Does unstructured data discovery require copying every file?
非结构化数据发现是否必须复制所有文件？
No. A metadata-first scan can enumerate objects and access context in place, followed by approved sampling or targeted content inspection. Some tools copy content or derivatives for processing; others work in place. The decision depends on source load, residency, permissions, retention, repeatability and the evidence required.
不必。元数据优先扫描可以原位列举对象及访问语境，随后再进行获批抽样或定向内容检查。有些工具会复制内容或派生物进行处理，另一些则原位工作。选择取决于来源负载、数据驻留、权限、保留、可重复性与所需证据。
How do you validate discovery coverage?
如何验证非结构化数据发现的覆盖率？
Reconcile source-system totals against discovered, authorized, read, parsed, classified, excluded and failed counts. Break results down by repository, format, age, owner and access class; preserve reason codes for every gap; test known canary files; and repeat the scan after access or parser fixes.
把来源系统总数与已发现、已授权、已读取、已解析、已分类、已排除和失败数量对账。按存储库、格式、年代、所有者与访问级别拆分结果，为每个缺口保留原因代码，测试已知金丝雀文件，并在修复权限或解析器后重新扫描。
What should an unstructured data inventory contain?
非结构化数据清单应包含什么？
At minimum, record a stable source identifier, repository and location, version or checksum, format, size, timestamps, owner, access class, retention state, discovery time, parser or classifier version, inspection status, quality warnings, duplicate relationship and source coordinates for any findings. Unknown values should stay explicitly unknown.
至少记录稳定来源标识、存储库与位置、版本或校验值、格式、大小、时间戳、所有者、访问级别、保留状态、发现时间、解析器或分类器版本、检查状态、质量警告、重复关系，以及每项发现的来源坐标。未知值应明确保留为未知。
How should teams evaluate unstructured data discovery tools?
团队应如何评估非结构化数据发现工具？
Use a versioned pilot corpus that represents normal, difficult, restricted and prohibited cases. Compare connector scope, metadata fidelity, format support, access propagation, evidence coordinates, classification quality, false positives and negatives, coverage reporting, incremental scans, auditability, export, recovery and workload cost.
使用版本化试点语料，覆盖正常、困难、受限和禁止案例。比较连接范围、元数据保真度、格式支持、权限传播、证据坐标、分类质量、误报漏报、覆盖报告、增量扫描、可审计性、导出、恢复与工作负载成本。
How often should unstructured data discovery run?
应多久执行一次非结构化数据发现？
Frequency should follow source change rate and decision risk. A one-time baseline is useful for planning, but operating discovery usually needs incremental monitoring for new, changed, moved, deleted and newly inaccessible objects, plus scheduled reconciliation to detect missed events and connector drift.
频率应由来源变化速度和决策风险决定。一次性基线有助于规划，但运营中的发现通常需要增量监控新增、变更、移动、删除和新近不可访问的对象，并定期对账以发现遗漏事件和连接器漂移。
Can InfiniSynapse replace an enterprise discovery and classification system?
InfiniSynapse能否替代企业数据发现与分类系统？
No. InfiniSynapse is publicly presented as a multi-source, multimodal analysis tool across structured databases, documents, audio and video. It can analyze approved, supported inputs after discovery controls are complete, but it does not replace enterprise crawling, inventory, ownership mapping, access governance, parsing, OCR, transcription, classification or lifecycle control.
不能。InfiniSynapse公开定位是跨结构化数据库、文档、音频与视频的多源多模态分析工具。它可在发现控制完成后分析获批且受支持的输入，但不能替代企业级爬取、盘点、所有者映射、访问治理、解析、OCR、转录、分类或生命周期控制。
Official and First-Party Sources官方与第一方来源
- NIST SP 1800-39: Data Classification Practices for unstructured dataNIST SP 1800-39：面向非结构化数据的数据分类实践
- AWS: Discovering sensitive data with Amazon MacieAWS：使用Amazon Macie发现敏感数据
- Microsoft Learn: Purview Data Map scanning and classificationMicrosoft Learn：Purview数据地图扫描与分类
- Apache Tika: Supported document formats and extraction boundaryApache Tika：支持的文档格式与提取边界
- InfiniSynapse: Public multi-source and multimodal analysis capabilitiesInfiniSynapse：公开的多源多模态分析能力
The NIST page is an initial public draft published February 12, 2026; its comment period is closed. AWS and Microsoft pages describe vendor-specific services and constraints, while Apache Tika documents parser-specific support. Use them as factual examples, not universal requirements. The workflow and scorecard in this guide are a decision framework; validate exact sources, formats, identities, jurisdictions, policies, product versions and workload evidence before action.
NIST页面是2026年2月12日发布的初始公开草案，征求意见期已经结束。AWS与Microsoft页面描述特定厂商服务和限制，Apache Tika记录解析器专用支持。应把它们视为事实示例，而不是通用要求。本指南的工作流与评分卡属于决策框架；采取行动前应验证具体来源、格式、身份、司法管辖区、策略、产品版本与工作负载证据。