모든 템플릿

Data Asset Discoverability, Documentation & Trust Survey

Measures how easily analytics teams can find, evaluate, and trust data assets across the organization, revealing gaps in metadata, documentation, and lineage practices. Designed for data and analytics professionals; estimated 6–8 minutes.

샘플 질문

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질문 25개 · 약 11분
Q01
메시지

Welcome to the Data Asset Discoverability, Documentation & Trust Survey. This survey explores how you find, evaluate, and trust data assets in your organization. Your responses will help us identify gaps and improve the data experience for everyone. There are no right or wrong answers — we are interested in your honest opinions and experiences. Your responses are confidential and will be reported only in aggregate. Participation is voluntary, and you may stop at any time. Estimated time: 6–8 minutes.

Q02
객관식

In the past 30 days, have you accessed or evaluated any data assets (e.g., tables, dashboards, or reports)?

  • Yes, in the past 30 days
  • No, not in the past 30 days
Q03
의견 척도

How would you rate your proficiency with your organization's data tools?

척도: 15
최소:Novice최대:Expert
Q04
의견 척도

Overall, how easy or difficult was it to find a suitable data asset in the past 30 days?

척도: 17
최소:Very difficult최대:Very easy
Q05
객관식

How did you discover the most recent data asset you used? Select all that apply.

  • Search in data catalog or portal
  • Direct link or bookmark
  • Recommendation from a colleague
  • Browsed dashboards or reports
  • Wrote queries in the data warehouse or lake
  • API documentation or SDKs
  • Other
Q06
객관식

For the last data asset you used, was documentation available?

  • Yes, comprehensive
  • Yes, partial
  • No
  • Not sure
Q07
의견 척도

How confident are you in the accuracy and reliability of the last data asset you used?

척도: 17
최소:Not at all confident최대:Extremely confident
Q08
순위 매기기

Rank the following areas by how much they would improve your data experience. Drag to reorder from most to least impactful.

  1. Search relevance in the catalog/portal
  2. Access and permissions clarity
  3. Ownership and contact clarity
  4. Documentation completeness
  5. Lineage and provenance clarity
  6. Quality monitoring and alerts
  7. Tool usability and performance
드래그하여 순위 지정
Q09
장문형

Based on your responses, is there anything else you'd like to share about finding, documenting, or trusting data assets in your organization?

Q10
객관식

What is your primary role?

  • Data analyst
  • Business analyst
  • Data scientist
  • Data engineer
  • Product manager
  • Operations
  • Other
Q11
메시지

Thank you for your time — your feedback will directly help us improve data discoverability, documentation, and trust across the organization.

Q12
객관식

In the past 30 days, what challenges, if any, did you encounter when trying to use data assets? Select all that apply.

  • I didn't know where to search
  • Too many similar datasets to compare
  • Access or permissions were unclear
  • I couldn't identify a data owner
  • Relevant documentation was missing or unclear
  • The tools were hard to use
  • Time constraints
  • None — I didn't encounter challenges
  • Other
Q13
의견 척도

When you search in your organization's data catalog or portal, how often do the first-page results meet your needs?

척도: 15
최소:Never최대:Always
Q14
의견 척도

How would you rate the completeness of the documentation for that data asset?

척도: 17
최소:Very incomplete최대:Very complete
Q15
순위 매기기

Rank the following factors by how much they influence your confidence in a data asset. Drag to reorder from most to least important.

  1. Documentation completeness
  2. Data freshness / low latency
  3. Historical stability of values
  4. Data owner responsiveness
  5. Provenance and lineage clarity
  6. Quality monitoring and alerts
드래그하여 순위 지정
Q16
AI 인터뷰

We'd like to understand your experience in more depth. An AI moderator will ask you a couple of follow-up questions about your data discovery and trust experience.

Q17
객관식

Which domain or team do you primarily support?

  • Finance
  • Marketing
  • Sales
  • Product
  • Operations
  • IT
  • HR
  • Other
Q18
의견 척도

How would you rate the clarity of the documentation for that data asset?

척도: 17
최소:Very unclear최대:Very clear
Q19
의견 척도

How likely are you to reuse this data asset for future analyses?

척도: 17
최소:Very unlikely최대:Very likely
Q20
드롭다운

How long have you been at your current organization?

  • Less than 6 months
  • 6–12 months
  • 1–2 years
  • 3–5 years
  • 6–10 years
  • More than 10 years
Q21
의견 척도

How would you rate the recency (up-to-dateness) of the documentation for that data asset?

척도: 17
최소:Very outdated최대:Very up to date
Q22
의견 척도

How likely are you to recommend this data asset to a colleague?

척도: 17
최소:Very unlikely최대:Very likely
Q23
드롭다운

Where are you primarily located?

  • North America
  • Europe
  • Asia-Pacific
  • Latin America
  • Middle East & Africa
  • Prefer not to say
Q24
의견 척도

How would you rate the lineage and provenance information in the documentation for that data asset?

척도: 17
최소:Very poor최대:Very thorough
Q25
드롭다운

What is the maximum acceptable data freshness (latency) for your typical analyses?

  • Real-time (seconds)
  • Hourly
  • Daily
  • Weekly
  • Monthly
  • Quarterly or less frequent
  • Not sure

포함된 기능

  • AI 후속 질문

    정형화된 설문이 놓치는 세부 내용을, 주관식 답변에 맞춰 AI가 심층 질문으로 끌어냅니다.

  • 주의력 확인 장치

    성의 없는 답변과 저품질 응답자를 걸러내는 내장 안전장치입니다.

  • AI가 작성한 문안

    문구, 질문 순서, 분기 로직까지 AI가 연구 목표에 맞춰 작성합니다.

  • 자동 리포트

    응답이 모이면 주요 주제, 인용문, 이해하기 쉬운 요약이 자동으로 작성됩니다.

다른 서비스와 비교

다른 설문 도구의 가장 유사한 템플릿을 검토했습니다. 그 도구들이 잘하는 점과, 이 템플릿이 한발 더 나아가는 지점을 정리했습니다.

이 템플릿을 선택하는 이유

  • Includes adaptive AI follow-up interviews (with optional voice AI) that probe deeper into why a specific data asset lacked documentation or trust, not just static ratings
  • Covers the full discoverability-to-trust journey in one flow: discovery method, catalog search friction, documentation completeness/clarity/recency, lineage/provenance, and confidence in accuracy
  • Uses ranking questions to force trade-off prioritization of trust factors and improvement areas, plus role/domain/tenure segmentation for cross-team analysis
  • Automated per-response quality scoring and auto-generated reports turn open-text and interview responses into structured, actionable findings without manual coding

QuestionPro

Technical Documentation Survey Template

This is a static template focused on rating the quality of technical documentation (clarity, completeness, usefulness), which overlaps with the documentation-quality portion of our survey but doesn't address broader data discoverability, catalog search behavior, or lineage/provenance trust signals. It appears to be a fielding-ready template rather than a guide, but it's narrower in scope than a full data-asset trust assessment.

잘하는 점

  • Purpose-built around documentation quality rather than a generic satisfaction survey
  • Likely offers standard survey distribution and reporting features typical of QuestionPro's platform
  • Ready-to-use template structure for quick deployment

아쉬운 점

  • No adaptive AI follow-up interviewing to probe why documentation was rated poorly or how it affected trust in the underlying data asset
  • Doesn't appear to cover data discovery methods, catalog search friction, or lineage/provenance — key drivers of data asset trust
  • No published prompt-level methodology or per-response quality scoring, so response depth relies entirely on respondent effort

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