AI Surveys & Data Analysis
Analysing Open-Ended Survey Responses with AI and Human Review
Turn open-ended survey comments into reviewable themes using a codebook, AI-assisted classification, validation, and careful quotation rules.
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AI Data Analysis
White Storm Technologies helps your team turn collected survey or business data into an analysis you can inspect and use. We clarify the question, check the structure and quality of the data, and build the analytical steps around that context. AI can assist with text coding, exploration, or draft explanations, while calculations and findings remain tied to reviewable inputs.
YOUR GOALS. OUR EXPERTISE.WHO WE HELP
Built around your team.
For research teams, business operations, customer insight leads, and organizations with collected data to understand.
The challenges we help solve.
A dataset may contain duplicate records, missing values, inconsistent categories, or measures whose definitions changed over time. Automated summaries can overlook those issues or present a pattern as an explanation. Useful analysis begins with understanding how the data was produced and what it can reasonably support.
WHAT WE DELIVER
What we can build with you.
- 01
Data preparation and quality record
An organized analytical dataset, variable definitions, agreed cleaning and exclusion rules, and a record of material quality issues. Original supplied data and derived fields remain distinguishable.
- 02
Reproducible analytical workflow
The agreed tables, comparisons, calculations, and text coding process. Statistical methods, weighting, or models are included only when appropriate to the data and explicitly scoped.
- 03
Clear findings and reporting
Charts, tables, dashboards, or a narrative brief linked to the business question. We explain the relevant sample or coverage, observed patterns, limitations, and questions that require further investigation.
ILLUSTRATIVE WORKFLOW
How the solution works.
- 01
Understand the data and question
Review collection methods, definitions, time periods, and intended decisions. Inspect a sample before agreeing the outputs or analytical method.
- 02
Prepare and analyze
Apply documented quality checks and transformations, then build the agreed analysis. Review AI-assisted text codes against original responses and check calculations independently of generated prose.
- 03
Explain and hand over
Review findings with the data owner, make uncertainty and limitations visible, and provide the agreed outputs and rerun guidance for future datasets.
A CLEAR STARTING POINT
What we need from you.
- The business or research question and intended audience
- A permitted data sample, complete exports, and field definitions
- Collection context, known issues, and any existing cleaning rules
- A data owner and preferred reporting or handover format
Systems & integrations.
We assess access, supported APIs, and suitability during discovery. These tools are options to evaluate for your project.
- CSV, spreadsheet, database, and survey exports
- Permitted CRM, product, or operations datasets
- Python, SQL, or other analytical tooling selected for the task
- Your chosen reporting, dashboard, or document environment
Illustrative example: understand a customer feedback dataset
A product team could combine collected rating questions with reviewed themes from open-text feedback. The workflow would document missing responses and quality flags, compare agreed groups where the data supports it, and show example comments without treating AI-generated summaries as independent evidence.
BEFORE WE BEGIN
Questions worth asking.
Can you work with messy spreadsheets?
Yes. We first assess the structure, definitions, volume, and known issues. Some problems can be addressed through documented cleaning; others may need clarification from the data owner or additional collection.
Can the analysis explain why something happened?
It may identify patterns and plausible questions to investigate. A correlation or group difference does not by itself establish a cause. We match claims to the collection method and analytical design rather than letting a generated explanation overstate the evidence.
Can you code open-text answers?
Yes. We can define a codebook, use AI-assisted classification, and review representative and ambiguous cases. The scope includes how reviewers correct codes and how themes are checked against the actual responses.
Can the workflow run on future data?
It can be designed for repeat use when the data structure and update process are understood. We agree checks for schema changes, ownership, rerun frequency, and what requires a new analytical review.
WHITE STORM INSIGHTS
Plan your next step.
AI Surveys & Data Analysis
A Practical Survey Data-Cleaning Checklist
Clean survey data with documented rules for identifiers, duplicates, missing values, routing, quality flags, transformations, and release checks.
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AI Surveys & Data Analysis
Reading Survey Dashboards: Segmentation, Uncertainty, and Action
Read survey dashboards responsibly by checking the respondent base, subgroup coverage, uncertainty, comparability, and the evidence behind proposed actions.
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LET'S START WITH YOUR GOAL
What would you like to make possible?
Tell us about your team, current systems, and the outcome you need.