Force Numerical Range
Infoveave Data Automation — Numeric
A single configuration clips all values above 100,000 to exactly 100,000 — or removes those rows entirely — so outliers never distort downstream aggregations or model training.
Numeric columns in real-world datasets routinely contain values that fall outside the valid business range — a transaction amount of -500 in a column that should only contain positive values, a sensor reading of 150 where the sensor physically maxes at 100, or an age value of 999 from a form where users entered placeholder text that was parsed as a number. These out-of-range values distort averages, break model training, and cause incorrect threshold comparisons. Force Numerical Range applies a boundary rule to each selected column — clipping values that exceed the maximum to the maximum, raising values below the minimum to the minimum, or removing rows where any selected column violates the range — so downstream analytics, reports, and models only ever see values that are within the correct operating range.
What Force Numerical Range does
Force numeric column values to stay within a user-defined minimum and maximum bound in Infoveave. Clip out-of-range values to the boundary or remove entire rows with outliers — all without writing filter formulas or manual data cleaning scripts.
When to use Force Numerical Range
- You are preparing numeric sensor, IoT, or measurement data where the physical range of the instrument is known — temperature between -40 and 150 degrees, pressure between 0 and 500 PSI — and values outside that range represent instrument errors that should be capped or removed before aggregation
- You are normalizing financial transaction data where negative amounts or amounts exceeding a defined maximum indicate data entry errors, and you want to enforce a valid range for all transaction amounts before computing averages, totals, or fraud indicators
- You are training a machine learning model and need to bound all numeric features within a specified range to prevent extreme outliers from dominating the scaled feature space during normalization and to remove clearly erroneous rows from the training set
- You are enforcing a business rule that score, rating, or percentage columns should always fall within 0 to 100 — and want values like 105 or -2 clipped to the boundary rather than removed so all rows are retained with valid values
When to avoid it
- You need to remove rows based on logical or text conditions rather than numeric range violations — use Filter on Formula or Filter on Numerical Range for condition-based row exclusion
- You want to scale or normalize values to a 0-to-1 range while preserving their relative distribution — that is normalization, not bounded enforcement; Normalize Columns handles min-max scaling separately
- You only want to round or adjust decimal precision without enforcing a range limit — use Round Numbers for precision control when no bounding is needed
Where it fits in your Infoveave automation
Force Numerical Range is one step inside a multi-step Infoveave workflow. Chain it with other activities — no code, no manual hand-offs.
Build this workflow visually in Infoveave Data Automation — drag, connect, and schedule with no infrastructure setup.
How teams use Force Numerical Range
Real scenarios where this transformation saves hours of manual work.
Clip Out-of-Range Sensor Readings Before OEE Calculation
A manufacturing data team ingests temperature and pressure sensor readings from production equipment. Sensor malfunctions occasionally produce readings above the physical maximum of the instrument, creating outlier values that inflate average readings and distort historical trend analysis. Force Numerical Range is configured with the physical maximum as the upper bound, clipping all over-range readings to the maximum rather than removing rows so the time-series record remains intact and the clipped value signals a sensor anomaly for maintenance review.
Enforce Valid Transaction Amount Range Before Fraud Model Input
A fraud analytics team processes payment transaction records where valid amounts fall between 0.01 and 50,000. Negative amounts and amounts above 100,000 represent data entry errors or test records that should be removed from the fraud model training set. Force Numerical Range is configured with Clear Outliers enabled and bounds of 0.01 to 50,000, removing any row that contains an amount outside that range so the model trains only on valid production transaction data.
Cap Promotions Discount Column at 100 Percent Maximum
A retail analytics team finds that a promotions dataset contains discount percentage values exceeding 100 due to stacked voucher combinations that were applied beyond intended limits. Rather than removing those rows — which represent real transactions — Force Numerical Range caps the discount column at 100 with the upper bound set to 100 and Clear Outliers disabled, so the transaction is retained with a valid maximum discount value for reporting and margin analysis.
See Force Numerical Range in action
Input data (left) is transformed using the configuration below. The output table (right) is ready for dashboards or downstream steps.
Sales, Revenue500100000Disabled (clip to boundary)Input Data
| Region | Sales | Revenue |
|---|---|---|
| A | 500 | 1000 |
| B | 120000 | 150000 |
| C | 75000 | 75000 |
| D | 900 | 5000 |
Output Data
| Region | Sales | Revenue |
|---|---|---|
| A | 500 | 1000 |
| B | 100000 | 100000 |
| C | 75000 | 75000 |
| D | 900 | 5000 |
Configuration
Key fields to configure in the Infoveave workflow builder. Full reference available in the documentation.
Lower Bound and Upper Bound
Define the inclusive valid range for the selected columns. Any value strictly below the lower bound or strictly above the upper bound is treated as out of range. When Clear Outliers is disabled, out-of-range values are replaced by the nearest bound — values below the lower bound become the lower bound, and values above the upper bound become the upper bound. When Clear Outliers is enabled, the entire row is removed if any selected column contains a value outside the bounds.
Clear Outliers
Controls how out-of-range values are handled. When disabled, values are clipped to the boundary — the row is retained but the value is replaced. This preserves row count and is appropriate when capping unrealistic values is preferred over losing data. When enabled, any row where at least one selected column falls outside the configured range is removed entirely. Use Clear Outliers enabled for strict data quality enforcement in model training and compliance reporting where boundary-violating rows must not appear in the output.
Frequently asked questions
Everything you need to know about Force Numerical Range in Infoveave.
Also in Numeric — and what runs before & after
Transformations in the same family as Force Numerical Range, often chained together in the same Infoveave workflow.
Part of Infoveave Data Automation
80+ transformations. Zero manual steps.
Force Numerical Range is one of over 80 transformation activities available inside Infoveave workflows. Chain transformations together — no code, no exports, no waiting for IT.
Ready to see Infoveave in action?
