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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.

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Sample questions

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25 questions · ~11 min
Q01
Message

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
Multiple Choice

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
Opinion Scale

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

Scale: 1 – 5
Min:NoviceMax:Expert
Q04
Opinion Scale

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

Scale: 1 – 7
Min:Very difficultMax:Very easy
Q05
Multiple Choice

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
Multiple Choice

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

  • Yes, comprehensive
  • Yes, partial
  • No
  • Not sure
Q07
Opinion Scale

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

Scale: 1 – 7
Min:Not at all confidentMax:Extremely confident
Q08
Ranking

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
Drag to rank
Q09
Long Text

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

Q10
Multiple Choice

What is your primary role?

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

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

Q12
Multiple Choice

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
Opinion Scale

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

Scale: 1 – 5
Min:NeverMax:Always
Q14
Opinion Scale

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

Scale: 1 – 7
Min:Very incompleteMax:Very complete
Q15
Ranking

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
Drag to rank
Q16
AI Interview

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
Multiple Choice

Which domain or team do you primarily support?

  • Finance
  • Marketing
  • Sales
  • Product
  • Operations
  • IT
  • HR
  • Other
Q18
Opinion Scale

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

Scale: 1 – 7
Min:Very unclearMax:Very clear
Q19
Opinion Scale

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

Scale: 1 – 7
Min:Very unlikelyMax:Very likely
Q20
Dropdown

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
Opinion Scale

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

Scale: 1 – 7
Min:Very outdatedMax:Very up to date
Q22
Opinion Scale

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

Scale: 1 – 7
Min:Very unlikelyMax:Very likely
Q23
Dropdown

Where are you primarily located?

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

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

Scale: 1 – 7
Min:Very poorMax:Very thorough
Q25
Dropdown

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

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

  • 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.

What it does well

  • 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

Where it falls short

  • 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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