Data Lineage Trust & Impact Analysis Survey
Measures data practitioners' confidence in lineage accuracy, impact analysis efficiency, and tooling gaps. Designed for data engineering, analytics, and platform teams to identify high-priority improvements to lineage infrastructure and workflows.
샘플 질문
템플릿에 포함된 내용을 미리 확인해 보세요. 모든 질문은 설문 공개 전에 자유롭게 수정할 수 있습니다.
In the last 30 days, how often did you use data lineage or impact analysis tools?
- Daily
- Several times a week
- Weekly
- Every few weeks
- Monthly or less
- I did not use them in the last 30 days
Overall, how much do you trust the accuracy of data lineage for your work over the last 30 days?
How would you rate the speed of completing a typical impact analysis over the last 30 days?
What are the biggest blockers to trustworthy lineage and efficient impact analysis for you? Select all that apply.
- Incomplete coverage
- Stale or delayed updates
- Unclear ownership or contacts
- Low metadata quality
- Tool usability or learnability
- Missing column-level lineage
- Access or permissions issues
- Query parsing limitations
- Competing priorities or time constraints
- Other (please specify)
Rank the following outcomes by importance for your work (drag to reorder; 1 = most important).
- Accurate coverage
- Faster impact scoping
- Fewer false positives
- Ease of use
- Clear ownership links
- Proactive change alerts
What is your primary role?
- Data engineer
- Analytics engineer
- Data analyst / BI developer
- Data scientist / ML practitioner
- Data platform / Infrastructure
- Product manager
- People manager / Leader
- Other
Thank you for completing this survey. Your feedback will directly inform improvements to lineage tooling and workflows. If you have any questions, please contact [survey administrator email].
Which systems did you use for data lineage or impact analysis in the last 30 days? Select all that apply.
- Data catalog (e.g., DataHub, Collibra, Alation)
- dbt docs
- OpenLineage-based tooling
- In-house lineage service
- BI lineage (e.g., Looker, Power BI, Tableau)
- Graph database or store
- Custom SQL or notebooks
- Other (please specify)
How confident are you in the accuracy of column-level lineage information you have access to?
Which best describes the focus of your most recent impact analysis in the last 30 days?
- Upstream schema change
- Downstream dashboard or report change
- Production incident or root-cause analysis
- Cost or performance optimization
- Access or governance change
- Other (please specify)
Which methods do you use to validate or cross-check lineage information when making decisions? Select all that apply.
- Compare with query logs
- Manual SQL tracing
- Ask a subject matter expert
- Review dbt tests or data tests
- Graph traversal checks
- Cross-environment diffs
- Other (please specify)
- I don't validate lineage
If you could change one thing to improve lineage trust or impact analysis at your organization, what would it be?
How many years have you worked with data professionally?
- Less than 1 year
- 1–2 years
- 3–5 years
- 6–10 years
- 11+ years
- Prefer not to say
How confident are you in lineage coverage across different systems and platforms in your organization?
For your most recent impact analysis, how confident were you that you identified all affected assets?
What lineage update freshness do you typically need to trust lineage data for impact analysis?
- Real-time (under 5 minutes)
- Hourly
- Daily
- Weekly
- No strict requirement
Briefly describe a recent case (within the last 30 days) where lineage information either helped or misled your analysis. What happened, and how was it resolved?
Approximately how large is your organization?
- 1–49
- 50–249
- 250–999
- 1,000–4,999
- 5,000+
- Prefer not to say
How confident are you in the timeliness of lineage updates (i.e., that lineage reflects recent changes)?
On average, approximately how long did it take you to complete an impact analysis over the last 30 days?
- Under 15 minutes
- 15–30 minutes
- 31–60 minutes
- 1–2 hours
- More than 2 hours
- Not sure
Thank you for sharing your experiences. I'd like to explore a few of your answers in more depth. Based on what you've shared, could you walk me through a specific moment where lineage data influenced a decision you made — and what the outcome was?
Which industry best describes your organization?
- Technology
- Financial services
- Retail / CPG
- Healthcare / Life sciences
- Manufacturing
- Media / Entertainment
- Public sector / Education
- Other
- Prefer not to say
How confident are you in the accuracy of ownership and contact metadata associated with lineage?
Which region do you primarily work in?
- North America
- Europe
- Asia
- Latin America
- Middle East / Africa
- Oceania
- Prefer not to say
In the last 30 days, approximately how many times did lineage inaccuracies or gaps cause you to redo work?
- 0 times
- 1–2 times
- 3–5 times
- 6–10 times
- 11 or more times
- 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
- Combines opinion-scale confidence ratings across lineage accuracy, column-level lineage, cross-system coverage, timeliness, and ownership metadata for a granular trust profile
- Pairs quantitative impact-analysis speed and confidence metrics with open-text prompts asking practitioners to describe a specific recent lineage failure, grounding results in real incidents
- Includes an AI follow-up interview stage that can adaptively probe individual respondents' answers (e.g., digging into blockers or validation methods) rather than stopping at fixed-choice data
- Segments results by role, tenure, org size, industry, and region, and closes with a ranking exercise so priorities can be weighed against practitioner context
SurveySparrow
Business Impact Analysis Questionnaire Template | For IT, SaaSThis is a general-purpose IT/SaaS business impact analysis questionnaire rather than one built specifically for data lineage or impact-analysis tooling in data engineering workflows. It's a ready-to-field template but covers broad business continuity/impact concepts rather than lineage accuracy, column-level confidence, or tooling gaps. Useful as a generic starting point, but not purpose-built for data practitioners.
잘하는 점
- Ready-to-field template with SurveySparrow's conversational survey UI
- Applicable across general IT/SaaS business continuity and impact scenarios
- Likely supports standard question types (rating, multiple choice) for quick deployment
아쉬운 점
- No adaptive AI follow-up interviews or voice AI to probe individual responses in depth
- Not tailored to data lineage specifics (column-level lineage, ownership metadata, lineage freshness)
- No transparent published methodology or automated quality scoring per response
설문을 공개할 준비가 되셨나요?
이 템플릿을 편집기에서 열어 보세요. 첫 응답자가 보기 전에 모든 부분을 원하는 대로 바꿀 수 있습니다.