Solution Database / Science and Research
Research data cleaning
Undocumented cleaning steps undermine subsequent analysis. Reversible, documented transformations with researcher-approved rules.
01The offer
For research groups with messy observational datasets, turn authorized datasets, data dictionaries and cleaning rules into cleaned dataset and transformation script. Address the recurring problem: undocumented cleaning steps undermine subsequent analysis. The value hypothesis is a more complete, reviewable deliverable with less repeated preparation; the pilot must establish whether that benefit is real.
- For
- Research groups with messy observational datasets
- Takes in
- Authorized datasets, data dictionaries and cleaning rules
- Delivers
- Cleaned dataset and transformation script
- Message
- Research data cleaning for research groups with messy observational datasets. Reversible, documented transformations with researcher-approved rules. Demonstrate the claim through a reproducible cleaning report on a sample dataset.
- Lead magnet
- A reproducible cleaning report on a sample dataset
02How it works
- Profile missing values
- Detect inconsistent coding
- Propose transformations
- Preserve raw data
- Generate reproducible scripts
- Validate reviewed outputs
Workflow
Scope one technical task, inspect authorized material, propose an implementation, build in a controlled environment, run relevant checks, obtain the required change approval, deliver with recovery instructions, and monitor the agreed operating scope. Start with authorized datasets, data dictionaries and cleaning rules and finish with cleaned dataset and transformation script.
AI and people
Explain code or configuration, draft transformations and propose technical changes. Execute deterministic validation and meaningful tests. Engineers review correctness, access handling and failure behavior before deployment.
Screens
Key screens: Data profile, transformation preview, provenance log. Show a work backlog, proposed changes and verification results. Link each item to its source configuration, code or data mapping. Provide execution logs and an owner-facing health view. Keep environments and approval states clearly separated so a draft cannot be mistaken for a live change. In this product, the first view is data profile, followed by transformation preview and provenance log.
Admin
Project access, environment separation, versioned changes, test evidence, owner approvals, execution logs, rollback instructions and incident handling.
03Market gap
Alternatives buyers use today
Developers, system integrators, existing automation products and internal engineering work. Differentiate on this specific proposed advantage: reversible, documented transformations with researcher-approved rules. Test it against the buyer's current method on the same task. Competitor coverage and uniqueness have not been established.
Where this wins
Reliable niche implementations, integration knowledge, representative tests and ongoing operational responsibility. For this solution, build around reversible, documented transformations with researcher-approved rules. This advantage requires execution and accumulated customer trust; the base model alone is not a defensible asset.
04Why now
Science and Research teams are adopting AI for exactly this kind of repeatable work, and the cost of language and vision models has dropped far enough that a narrow, reviewed workflow pays back quickly. The buyer already feels the problem: undocumented cleaning steps undermine subsequent analysis.
05Proof & signals
Channels where buyers gather: Research data management services. Metrics that prove it works: Verified transformations, reproducibility.
Paid pilot
Implement one bounded task in a safe test environment. Demonstrate normal operation, failure handling and recovery with representative inputs. Have the responsible technical owner review the results. For this solution, use authorized datasets, data dictionaries and cleaning rules and evaluate cleaned dataset and transformation script. Agree success thresholds with the buyer before starting; collect a baseline for verified transformations, reproducibility. A positive signal is payment and repeat use with acceptable quality and delivery cost, not a favorable demo reaction alone.
06Execution plan
MVP
Begin with research groups with messy observational datasets and one recurring use case. Build the first two modules: profile missing values; detect inconsistent coding. Provide operator assistance for the third module: propose transformations. Deliver cleaned dataset and transformation script through a manual review queue. Perform other necessary full-scope functions manually during the pilot. Include all applicable access, accuracy and professional-review controls from the start.
First 30 days
Week 1: interview five prospective buyers in this segment: research groups with messy observational datasets. Ask to see a recent example of the problem and their current process. Week 2: prepare this demonstration using authorized or synthetic material: a reproducible cleaning report on a sample dataset. Week 3: present it through research data management services and seek one narrowly scoped paid pilot. Week 4: review verified transformations, reproducibility, total delivery effort and a concrete renewal decision before increasing scope.
After the pilot
After paid pilots establish value, automate the remaining modules: preserve raw data; generate reproducible scripts; validate reviewed outputs. Add one validated source integration, reusable customer configuration and recurring delivery. Expand to additional teams, document formats or languages only after testing the new scope.
Retention
Maintain agreed integrations or technical assets, review failures and upstream changes, and sell additional scoped work only after the first implementation is stable.
Integrations
Authorized datasets, papers, protocols, code and research records. Approved repositories, application APIs, execution platforms and monitoring systems. Validate current API access and behavior during discovery before promising compatibility. These are candidate integration categories, not verified supported connectors.
07Investment and running costs
| Phase | Scope | Time | Budget |
|---|---|---|---|
| MVP | One buyer segment, one recurring use case; first modules: profile missing values; detect inconsistent coding. Manual review in the loop. | 6 days | $10,000 |
| Paid pilot | Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers. | 7 days | $13,000 |
| Full product | Remaining modules: preserve raw data; generate reproducible scripts; validate reviewed outputs. Self-serve onboarding, billing, monitoring and the wider integration set. | 2 weeks | $17,500 |
| Total | $40,500 | ||
| Running | Hosting | AI usage | Total a month |
|---|---|---|---|
| MVP and paid pilot (about 3 customers) | $30–$60 | $60–$120 | $90–$180 |
| Full product (about 50 customers) | $110–$210 | $530–$1,050 | $640–$1,260 |
Revenue model to test
Test USD 1,000-4,000 for one bounded implementation or technical review, then USD 200-1,000 monthly for defined maintenance. Hosting, vendor fees and major feature changes are separate. Prices are hypotheses.
Cost drivers
Engineering, testing, cloud execution, third-party API fees, monitoring, incident response and vendor-change maintenance.
Safeguards
Preserve original data, methods, citations and research limitations. Use researcher review and document every substantive transformation. Validate source access and reviewer availability during the pilot. Maintain customer-level access, data deletion controls and a record of final approvals.
Take it further
Concept proposal expanded from the 315-solution conversation. Demand, pricing, differentiation, build scope and integration feasibility are hypotheses, not verified market findings. Category link is inspiration rather than evidence of business viability.