Calculator Inputs
Example Data Table
| Input | Example value | Why it matters |
|---|---|---|
| Data sources | 3 | Joins and consistency checks add integration effort. |
| Features | 60 | More features increase build, test, and review time. |
| Rows | 800,000 | Larger datasets raise profiling and compute overhead. |
| Missing / Outliers / Duplicates | 8% / 3% / 1% | Quality issues increase cleaning and validation work. |
| Feature mix | 35% numeric, 25% categorical, 10% text | Text and interactions typically take longer per feature. |
| Validation | Standard | More tests reduce defects but add upfront time. |
| Automation | Semi-automated | Reusable pipelines lower effort for repeated transforms. |
| Iterations | 2 | Feedback loops create predictable rework and tuning. |
Formula Used
The model estimates total effort hours by combining per-feature work with fixed setup work, then applying multipliers:
RawHours = (CoreHours + FixedHours) × Docs × Tooling × Experience × Automation × Iterations × Coordination
TotalWithContingency = RawHours × (1 + Contingency%)
- TypeMix is the weighted average of feature-type factors.
- Quality increases with missing, outliers, and duplicates.
- Contingency is suggested from a 0–100 risk score.
How to Use
- Enter data sources, feature count, and approximate rows.
- Set quality rates using quick profiling metrics.
- Choose complexity, validation depth, and documentation level.
- Adjust automation, iterations, and team settings.
- Click Estimate Effort to view results above the form.
- Use Download CSV or Download PDF for sharing.
Scope Baselines
Baseline effort starts with 1.6 hours per feature, then adds fixed setup work for profiling, environment checks, and baseline tests. Complexity scales the workload at 1.00 for simple, 1.35 for medium, and 1.75 for complex features. Each extra data source adds about 8% integration overhead beyond the first. This helps scope engineering work before model training begins and clarifies why rushed estimates fail during discovery and stakeholder alignment.
Data Quality Impact
Data quality drives effort because cleaning and validation multiply every transformation. The quality multiplier is 1 + 0.60×missing + 0.30×outliers + 0.20×duplicates, using rates as fractions. For example, 10% missing increases effort by 6%, while 5% outliers adds 1.5%. When quality problems cluster across sources, time is also spent reconciling definitions, rebuilding joins, and documenting assumptions for auditors. Track these rates weekly to prevent late-stage rework spikes.
Feature Type Mix
Feature type mix changes per-feature time because different transforms require different validation. Numeric features use a 1.00 factor, categorical encoding uses 1.20, datetime handling uses 1.10, aggregates use 1.30, interactions use 1.40, lag or rolling windows use 1.50, and text features use 1.60. A weighted average of these factors estimates your TypeMix multiplier. Shifting only 10% from numeric to text can increase total hours.
Process Multipliers
Rework and governance are captured through multipliers that reflect process maturity. Each extra iteration adds 15% via 1 + 0.15×(iterations−1). Validation scales at 1.00, 1.15, or 1.35 for basic, standard, or strict testing. Documentation adds 1.00, 1.10, or 1.25. Coordination grows with team size using 1 + 0.05×(team−1). Strong automation can reduce effort to 0.82 or 0.68 across repeat deployments.
Scheduling and Cost
Timeline depends on weekly productive capacity: (hours/day × workdays) minus meeting hours, with a minimum of 5 hours per week. Estimated weeks equal total hours divided by that capacity. A risk score from 0 to 100 suggests contingency between 5% and 30%, adding buffer hours to protect delivery. If you provide an hourly rate, cost equals total hours with contingency times that rate. Use the exports to share assumptions and align stakeholders.
FAQs
1) What counts as a feature to engineer?
Count any derived column used by modeling or rules: encodings, aggregations, ratios, rolling windows, text vectors, and interaction terms. Include monitoring features if they must be computed in production pipelines.
2) How do I estimate rows and quality rates?
Use recent profiling runs or warehouse statistics. For quality, sample representative partitions and calculate percent missing, outliers, and duplicates. If uncertain, start conservative and update after the first exploratory week.
3) Why do additional sources add overhead?
Each source introduces schema mapping, join logic, key integrity checks, and reconciliation of definitions. Cross-source drift also increases testing effort, so even small source counts can raise review and validation time.
4) When should I select strict validation?
Choose strict when features affect pricing, compliance, safety, or regulated reporting. It suits complex joins, heavy imputations, and time-based leakage risks, where extra unit tests and backtests prevent costly defects.
5) How should I interpret automation level?
Automation reflects reusable pipelines, templates, and standardized checks. Semi-automated assumes partial reuse with manual steps. Highly automated assumes strong tooling that reduces repeated transform and validation work across iterations.
6) How should I use the contingency recommendation?
Treat it as a planning buffer, not slack. Add it to the baseline estimate for deadlines and staffing, then reduce it only after risk drivers improve, such as data quality, stable requirements, or fewer iterations.