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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.

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AI-Powered Questions

Intelligent follow-up questions based on responses

Automated Analysis

Real-time sentiment and insight detection

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Detailed Reports

Comprehensive insights and recommendations

Template Overview

17

Questions

AI-Powered

Smart Analysis

Ready-to-Use

Launch in Minutes

This professionally designed survey template helps you gather valuable insights with intelligent question flow and automated analysis.

Sample Survey Items

Q1
Chat 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 5 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.
Q2
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
Q3
Opinion Scale
How critical are internal data products for completing your work on time?
Range: 1 5
Min: Not at all criticalMid: NeutralMax: Absolutely critical
Q4
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
Q5
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
Q6
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
Q7
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
Q8
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
Q9
Ranking
Please rank the following data quality dimensions by importance to your work (drag to reorder, 1 = most important).
Drag to order (top = most important)
  1. Accuracy
  2. Completeness
  3. Timeliness
  4. Consistency
  5. Validity
  6. Lineage/transparency
Q10
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
Q11
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)
Q12
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.
AI InterviewLength: 2Personality: [Object Object]Mode: Fast
Reference questions: 5
Q13
Multiple Choice
What is your primary role?
  • Data analyst
  • Data engineer
  • Data scientist
  • Product manager
  • Software engineer
  • Business stakeholder
  • Other (please specify)
Q14
Dropdown
Which team or department are you part of?
  • Analytics
  • Data platform
  • Finance
  • Operations
  • Marketing
  • Sales
  • Product
  • Engineering
  • Other
Q15
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
Q16
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
Q17
Chat 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.

Frequently Asked Questions

What is QuestionPunk?
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How do I create my first survey?
Sign up, then choose how to build: describe your research goal and let AI generate a survey, pick a template, or start from scratch. Add question types, set logic, preview, and share.
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Yes. Describe your research goal in plain language and QuestionPunk drafts a complete survey with appropriate question types, ordering, and AI follow-up logic. You can then customize before publishing.
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QuestionPunk supports a wide range of question types: opinion scale, rating, multiple choice, dropdown, ranking, matrix, constant sum, AI interview (text and audio), long text, short text, email, phone, date, address, website, numeric, audio/video recording, contact form, chat message, conversation reset, button, page breaks, and more.
How do AI interviews work?
AI interviews conduct adaptive conversations with respondents. The AI asks follow-up questions based on what the respondent says, probing for clarity and depth. You control the personality, tone, model (Haiku, Sonnet, or Opus), and question mode (fixed count, AI decides when to stop, or time-based).
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