All templates
Operations & Data

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.

Sample questions

A preview of what’s in the template. Every question is editable before you launch.

26 questions · ~11 min
Q01
Message

Welcome to the Data Lineage Trust & Impact Analysis Survey. This survey explores your experience with data lineage tools and impact analysis workflows over the last 30 days. Your responses will help identify opportunities to improve lineage accuracy, tooling, and change management processes. Participation is voluntary and you may stop at any time. All responses are confidential and will be reported in aggregate only. There are no right or wrong answers — we are interested in your honest experience. Estimated time: 7–9 minutes.

Q02
Dropdown

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
Q03
Opinion Scale

Overall, how much do you trust the accuracy of data lineage for your work over the last 30 days?

Scale: 17
Min:Not at allMax:Completely
Q04
Opinion Scale

How would you rate the speed of completing a typical impact analysis over the last 30 days?

Scale: 17
Min:Far too slowMax:Very fast
Q05
Multiple Choice

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)
Q06
Ranking

Rank the following outcomes by importance for your work (drag to reorder; 1 = most important).

  1. Accurate coverage
  2. Faster impact scoping
  3. Fewer false positives
  4. Ease of use
  5. Clear ownership links
  6. Proactive change alerts
Drag to rank
Q07
Dropdown

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
Q08
Message

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].

Q09
Multiple Choice

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)
Q10
Opinion Scale

How confident are you in the accuracy of column-level lineage information you have access to?

Scale: 17
Min:Not at all confidentMax:Extremely confident
Q11
Multiple Choice

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)
Q12
Multiple Choice

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
Q13
Long Text

If you could change one thing to improve lineage trust or impact analysis at your organization, what would it be?

Q14
Dropdown

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
Q15
Opinion Scale

How confident are you in lineage coverage across different systems and platforms in your organization?

Scale: 17
Min:Not at all confidentMax:Extremely confident
Q16
Opinion Scale

For your most recent impact analysis, how confident were you that you identified all affected assets?

Scale: 17
Min:Not at all confidentMax:Completely confident
Q17
Dropdown

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
Q18
Long Text

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?

Q19
Dropdown

Approximately how large is your organization?

  • 1–49
  • 50–249
  • 250–999
  • 1,000–4,999
  • 5,000+
  • Prefer not to say
Q20
Opinion Scale

How confident are you in the timeliness of lineage updates (i.e., that lineage reflects recent changes)?

Scale: 17
Min:Not at all confidentMax:Extremely confident
Q21
Dropdown

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
Q22
AI Interview

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?

Q23
Dropdown

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
Q24
Opinion Scale

How confident are you in the accuracy of ownership and contact metadata associated with lineage?

Scale: 17
Min:Not at all confidentMax:Extremely confident
Q25
Dropdown

Which region do you primarily work in?

  • North America
  • Europe
  • Asia
  • Latin America
  • Middle East / Africa
  • Oceania
  • Prefer not to say
Q26
Dropdown

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

What’s included

  • AI follow-ups

    Adaptive probes on open-ended answers that pull out detail a static form would miss.

  • Attention checks

    Built-in safeguards against rushed answers and low-quality respondents.

  • AI-drafted copy

    Wording, ordering, and branching written by the AI — tuned to your research goal.

  • Auto report

    Themes, quotes, and a plain-English summary write themselves once responses come in.

How it compares

We reviewed the closest templates from other survey tools. Here’s what they do well — and where this template goes further.

Why this template

  • 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, SaaS

This 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.

What it does well

  • 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

Where it falls short

  • 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

Ready to launch?

Open this template in the editor. Every part is yours to change before the first respondent sees it.

Related templates

More studies from the same category.

See all
Operations & Data

Data Catalog Governance Health Survey: Findability, Ownership & Trust

Diagnoses catalog discoverability, ownership clarity, and data trust across teams. Designed for internal data practitioners to identify governance gaps and prioritize catalog improvements using NPS and behavioral metrics.

View template
Operations & Data

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.

View template
Operations & Data

Incident Communication Effectiveness Survey

Measures customer perceptions of clarity, timeliness, and trust in crisis and outage communications. Designed for B2B operations teams seeking to benchmark and improve incident response communication.

View template
Operations & Data

Internal Tools & Workflow Usability Assessment

Measures employee-perceived usability, reliability, and efficiency of internal tools and workflows. Use with cross-functional teams to identify friction points and prioritize improvements.

View template
Operations & Data

Meeting Availability & Scheduling Preferences Survey

Maps when employees are genuinely available and willing to meet, how their meeting time actually breaks down, and where scheduling friction eats into focus work. An AI follow-up interview digs into one specific recent meeting that went wrong and what would have fixed it. Built for ops, IT, and people teams auditing meeting culture.

View template
Operations & Data

Supply Chain Transparency Trust & Priorities Survey

Measures how much customers trust your supply chain claims, which transparency details (sourcing, labor, environmental impact, certifications) actually matter to them, and what they'd pay for verified information — with an AI follow-up that digs into the real story behind a specific trust or mistrust moment.

View template