QuestionPunkQuestionPunk
Start free
All templates
Operations & Data

Internal Data Product SLA Expectations Survey

Captures stakeholder expectations for data product availability, freshness, and quality to inform internal SLO/SLA definitions. Designed for data consumers across engineering, analytics, and business teams.

Use this templateBrowse more templates

Sample questions

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

17 questions · ~8 min
Q01
Message

Thank you for participating in this survey about our internal data products. We're gathering your expectations around reliability (uptime), data freshness, and data quality to set clear, realistic service-level targets. This survey takes approximately 9 minutes. Your participation is voluntary and you may stop at any time. Responses will be anonymized and reported in aggregate to the data platform team. There are no right or wrong answers—we simply want your honest expectations.

Q02
Dropdown

How often do you use internal data products (dashboards, datasets, pipelines) in your work?

  • Daily
  • 2–3 times per week
  • Weekly
  • Less than weekly
  • Rarely or never
Q03
Multiple Choice

What minimum monthly availability (uptime) do you expect from the data products you rely on?

  • 99.0% (~7.3 hours downtime/month)
  • 99.5% (~3.6 hours downtime/month)
  • 99.9% (~43 minutes downtime/month)
  • 99.95% (~22 minutes downtime/month)
  • Unsure
Q04
Dropdown

What is the minimum acceptable overall data accuracy rate for your production use?

  • 99.9% or higher
  • 99.5%
  • 99.0%
  • 97%
  • 95%
  • 90%
  • Below 90%
  • Unsure
Q05
Multiple Choice

How quickly should we notify you when a data incident is detected?

  • Immediately
  • Within 15 minutes
  • Within 1 hour
  • Within 4 hours
  • Same business day
  • Next business day
Q06
AI Interview

Based on your responses, is there anything else we should consider about your data reliability, freshness, or quality expectations? Please share any additional context, pain points, or priorities.

Q07
Multiple Choice

What is your primary role?

  • Data analyst
  • Data engineer
  • Data scientist
  • Product manager
  • Software engineer
  • Business stakeholder
  • Other (please specify)
Q08
Message

Thank you for your input. Your responses will help us define clear, realistic service-level targets for our internal data products. We expect to share proposed SLOs with stakeholders within the coming weeks.

Q09
Opinion Scale

How critical are internal data products for completing your work on time?

Scale: 1 – 5
Min:Not at all criticalMax:Absolutely critical
Q10
Multiple Choice

Which planned maintenance windows are acceptable to you? Select all that apply.

  • No regular windows acceptable
  • Weeknights 6–10 pm (local)
  • Overnight 10 pm–6 am (local)
  • Weekends
  • Flexible with advance notice
Q11
Dropdown

What is the maximum acceptable duplicate record rate in datasets delivered to you?

  • 0% (no duplicates tolerated)
  • Under 0.1%
  • Under 0.5%
  • Under 1%
  • Under 2%
  • Under 5%
  • Unsure
Q12
Multiple Choice

What are your preferred channels for incident and maintenance notifications? Select all that apply.

  • Slack/Teams
  • Email
  • Status page
  • PagerDuty/On-call
  • In-product banner
  • Other (please specify)
Q13
Dropdown

Which team or department are you part of?

  • Analytics
  • Data platform
  • Finance
  • Operations
  • Marketing
  • Sales
  • Product
  • Engineering
  • Other
Q14
Multiple Choice

What data freshness (maximum acceptable lag) do you require for your primary workflows?

  • Real-time (under 1 minute)
  • Under 15 minutes
  • Under 1 hour
  • Under 6 hours
  • Under 24 hours
  • Weekly or less frequently
  • Unsure
Q15
Ranking

Please rank the following data quality dimensions by importance to your work (drag to reorder, 1 = most important).

  1. Accuracy
  2. Completeness
  3. Timeliness
  4. Consistency
  5. Validity
  6. Lineage/transparency
Drag to rank
Q16
Dropdown

How many years of experience do you have working with data in your current or similar roles?

  • Under 1 year
  • 1–2 years
  • 3–5 years
  • 6–10 years
  • More than 10 years
Q17
Dropdown

What is your primary working time zone?

  • UTC−8 to −5 (Americas)
  • UTC−4 to 0 (Atlantic/Europe West)
  • UTC+1 to +3 (Europe/Africa)
  • UTC+4 to +7 (Middle East/Asia)
  • UTC+8 to +10 (East Asia/Australia)
  • UTC+11 to +12 (Pacific)
  • Prefer not to say

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 a dedicated AI follow-up interview question that probes deeper into each stakeholder's SLA expectations after they answer the structured questions, something static form builders can't replicate.
  • Purpose-built for data product SLOs/SLAs: covers concrete metrics like uptime percentage, maintenance windows, freshness lag, accuracy rate, and duplicate record rate rather than generic satisfaction questions.
  • Captures incident-response expectations directly (notification speed and preferred channels) plus a ranked prioritization of quality dimensions, giving teams data they can turn straight into SLO targets.
  • Segments results by role, department, tenure, and time zone so engineering, analytics, and business stakeholders' differing expectations can be compared side by side in the auto-generated report.

SurveySparrow

Product Feedback Survey Template

This is a general-purpose product feedback template, not one designed for internal data product SLA/SLO definition — it lacks any uptime, freshness, or data-quality-specific questions. It's a reasonable starting point for basic satisfaction feedback but would need heavy customization to serve as a data governance/SLA survey.

What it does well

  • Quick to deploy conversational survey format
  • Established template library and easy customization for general feedback use cases

Where it falls short

  • No adaptive AI follow-up interview — responses are static and can't be probed further
  • No built-in questions or logic for SLA metrics like uptime, freshness lag, or duplicate rates
  • No automated per-response quality scoring or transparent prompt methodology

QuestionPro

Product Evaluation Survey Template and Sample Questionnaire

A generic product evaluation template aimed at rating product features and satisfaction broadly, not internal data products specifically. It offers a starting questionnaire structure but contains no data-SLA vocabulary (availability, freshness, incident notification) and would require substantial rebuilding for this use case.

What it does well

  • Broad question bank suited to general product evaluation
  • Established enterprise survey platform with standard logic/branching features

Where it falls short

  • No adaptive AI interview to explore stakeholder-specific SLA concerns
  • No native questions covering data freshness, uptime targets, or duplicate/accuracy thresholds
  • No automated quality scoring or transparent prompt disclosure for any AI-assisted follow-up

Ready to launch?

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

Use this templateStart free

Related templates

More studies from the same category.

See all
Operations & Data

Data Literacy & Self-Service Analytics Adoption Assessment

An internal assessment for measuring employees' data literacy, self-service analytics confidence, tool adoption, and support needs — designed to surface skill gaps, trust issues, and barriers that inform data-enablement strategy.

View template
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.

View template
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

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

New Vendor Request Justification Survey

For employees requesting to onboard a new vendor or supplier. Captures the business need, estimated spend, urgency, risk factors, and alternatives already considered so procurement and operations teams can triage requests quickly. The AI follow-up interview probes why existing approved vendors won't meet the need and surfaces risk details a form alone would miss.

View template
Operations & Data

Vendor & Supplier Diversity Program Assessment

Evaluates how well your organization sources from certified diverse-owned suppliers — spend levels, barriers, and category priorities — for procurement and sourcing leaders. An AI follow-up interview reconstructs a real recent sourcing decision to surface what actually helped or blocked a diverse supplier from winning the business.

View template