This article will give you tips on how to analyze responses from a Civil Servant survey about open data awareness and use, covering practical AI tools and actionable strategies for robust survey response analysis.
Choosing the right tools for analyzing your data
Your approach and the tools you use to analyze survey responses truly depend on the format and structure of your data. Let’s break down the most common scenarios and what works best for each:
Quantitative data: For things like, “How many civil servants completed the open data training?” or multiple choice questions, classic tools like Excel or Google Sheets work perfectly. It’s straightforward—just count responses, run percent calculations, maybe toss in a quick chart. If 10% of civil servants reported completing their upskilling hours, simple functions show progress without extra hassle. [1]
Qualitative data: When you gather open-ended responses (“What do you find most challenging about using open data?”), that’s when the real complexity kicks in. Reading hundreds or thousands of answers isn’t reasonable. Here’s where AI becomes your new best friend—you need modern tools that can understand, summarize, and structure all that textual feedback automatically. Trying to do this manually is slow, error-prone, and just exhausting, especially with in-depth follow-up questions.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Copy-paste and chat: You can export your survey data, paste it into ChatGPT or an equivalent large language model, and start a conversation about your results.
Not always convenient: This workflow is quick for small-scale analysis, but it doesn’t scale well. Formatting massive datasets for GPT chat input, managing follow-ups, and tracking iterations gets hairy fast. It also lacks features designed specifically for survey data, which means lots of manual prep, and potential privacy or workflow headaches.
All-in-one tool like Specific
Tailored for civil servant surveys about open data awareness and use: Tools like Specific are built so you never need to fiddle with spreadsheets or manual export—just collect survey responses (including auto-generated, conversational follow-ups), and analyze everything with AI directly inside the same platform.
Better data collection: Automatic follow-up questions lead to richer responses, not one-liners. Learn more about how it works in this in-depth writeup on automatic AI survey follow-ups.
AI-powered summaries, theme extraction, and direct conversation: You get instant summaries, recurring themes, and can chat with AI about the results as easily as you talk to a human. There are extra safeguards for handling which data the AI analyzes, so you’re always in control of context.
For everything from policy feedback to data skills assessment, having analysis and collection under one roof removes friction. Specific is a popular pick for civil servants and teams managing open data programs, but other tools can work if you’re set up for more DIY workflows.
For a deeper dive into creating these types of surveys, the article how to create civil servant surveys about open data awareness and use walks you through survey setup from scratch.
Useful prompts that you can use for civil servant open data survey analysis
When you’re ready to analyze qualitative responses from your open data awareness survey, well-crafted prompts are your secret weapon in unlocking value with AI or GPT-based tools. Whether you’re tackling follow-up replies directly inside Specific or using a standalone GPT tool, these prompts cover everything from the big picture to granular insights.
Prompt for core ideas: This one is a classic—you want the AI to surface main ideas and themes with clear numbers up front. Here’s the exact text Specific uses, and it works great in ChatGPT, too:
Your task is to extract core ideas in bold (4-5 words per core idea) + up to 2 sentence long explainer.
Output requirements:
- Avoid unnecessary details
- Specify how many people mentioned specific core idea (use numbers, not words), most mentioned on top
- no suggestions
- no indications
Example output:
1. **Core idea text:** explainer text
2. **Core idea text:** explainer text
3. **Core idea text:** explainer text
Contextual boosts: AI models always do better when you provide extra context about your survey, your audience, or your goals. For example, add a short description to your prompt:
“These responses are from a 2024 survey of UK civil servants about open data awareness and usage. I want to understand the most common challenges and opportunities they see. My primary goal is to improve future training initiatives. Please extract core ideas as above.”
Dive deeper into topics: After identifying themes, prompt the AI with “Tell me more about XYZ (core idea)”—it’s an easy way to explore hidden patterns in depth.
Prompt for specific topics: Whenever you suspect a key issue is brewing (like “risk management concerns”), just ask: “Did anyone talk about risk management—or risks with open data disclosure? Include quotes.”
Prompt for personas: Civil servants aren’t all the same. To find patterns, use: “Based on the survey responses, identify and describe a list of distinct personas—like data enthusiasts or cautious managers. For each, summarize key characteristics, motivations, and any relevant quotes.”
Prompt for pain points and challenges: “Analyze the survey responses and list the most common pain points or challenges civil servants face regarding open data. Note patterns, frequency, and include supporting examples.” This is especially relevant given that only 10% of civil servants completed recent upskilling efforts, despite strong perceived value in open data. [1][5]
Prompt for suggestions & ideas: Want to harvest actionable improvements? Ask: “Identify and list all suggestions or requests mentioned by survey participants about open data initiatives. Organize by topic and frequency and include direct quotes where relevant.”
For additional question ideas or inspiration, check out this guide on best questions for civil servant open data surveys.
How Specific analyzes qualitative data from different question types
Open-ended questions with or without follow-ups: For every open-ended question, you’ll get both a summary for the question and detailed analysis of all AI-probed follow-up answers. Instead of mining raw responses, Specific structures those deep-dive insights in one place—so you’re not left guessing what, say, “lack of data skills” really means in this context.
Choices with follow-ups: Each survey choice (for example, “Yes, I have accessed open data” vs. “No, never accessed”) gets its own summary of related follow-up responses, turning multi-select answers into cohesive mini-analyses. This approach reveals how attitudes or knowledge levels cluster by group, and why.
NPS (Net Promoter Score) questions: Specific automatically splits follow-up answers by category—detractors, passives, and promoters—so you can see what might turn a critic into a supporter, or what keeps already-engaged civil servants coming back.
You can manually get similar results in ChatGPT, but it takes extra labor to filter, format, and analyze each set of answers by type.
To learn how surveys are built to surface these insights from the start, see the AI survey generator for civil servants focused on open data awareness.
How to handle challenges with AI context limit
Even the best AI tools (including ChatGPT and Specific) have context size limits. Basically, if your open data awareness survey yields too many detailed responses, the AI might not fit them all in at once. Here are two ways to keep your analysis practical and accurate—both available seamlessly in Specific:
Filtering: Filter conversations by participant actions or responses—such as only including those civil servants who completed data training modules, or only those who discussed perceived barriers—making the AI focus on the right segment for your needs.
Cropping: Crop down to just the most critical survey questions before sending to the AI for analysis. This ensures you maximize insights from your core qualitative questions, rather than overwhelming the model with background or less-relevant answers.
This approach is especially useful when, for example, you want to deep dive specifically into the group of civil servants who didn’t participate in upskilling initiatives—surfacing why uptake remained below 25%. [1]
For a quick start on editing or refining your survey questions for max insight, check out the AI survey editor—just describe what you want in plain English, and the tool updates your survey instantly.
Collaborative features for analyzing civil servant survey responses
When teams analyze civil servant open data awareness and use surveys, collaboration can quickly become chaotic—multiple spreadsheets, email threads, and disconnected notes don’t cut it.
Live collaboration, all in one place: Specific lets your team chat directly with AI about responses, share and refine insights, and even spin up parallel analysis conversations. Each chat can have its own segment filters, summaries, or deep-dive themes—giving you broad flexibility and traceability as you work toward actionable insights.
Know who’s contributing what: Each analysis thread shows who created it, plus avatars for every message—so you always know which colleague shared what perspective, and cross-team collaboration feels more natural.
No scrambling between tools: Comment on findings, update follow-up questions, and track results—all in-context and visible to the right stakeholders.
By bringing everything under one roof, you’ll spend less time chasing colleagues for their input and more time surfacing the right actions, supported by robust qualitative and quantitative analysis.
If you’re ready to get started with these collaborative capabilities, you can explore survey templates with built-in NPS for open data awareness or start from scratch using the AI survey generator.
Create your civil servant survey about open data awareness and use now
Launch your next survey and uncover actionable insights instantly, with richer follow-ups, automated analysis, and seamless team collaboration tailored for open data initiatives.