Replace Null Values
Infoveave Data Automation — Column Operations
A single configuration step replaces every null and blank cell in your chosen columns with the correct typed default — no more filter failures or aggregation errors caused by missing data reaching downstream activities.
Null and blank values propagate silently through data pipelines until they cause a filter to return no results, an aggregation to produce incorrect totals, or a join to drop rows that should have matched. Replace Null Values stops nulls at the source by substituting a defined fallback for each configured column — a string placeholder for text fields, zero or a sentinel number for numeric fields, a reference date for date fields, and a boolean default for flag fields. The result is a dataset with guaranteed completeness in every configured column, so downstream filters, aggregations, ML feature extraction, and dashboard rows all behave predictably.
What Replace Null Values does
Automatically fill null and blank cells in Infoveave datasets with typed default values for String, Number, Date, and Boolean columns. Ensure data completeness before filters, aggregations, joins, and ML pipelines that require non-null inputs.
Part of Infoveave Data Automation on the Unified Data Platform.
When to use Replace Null Values
- You are preparing data for downstream activities — filters, aggregations, joins, or ML features — that require non-null inputs and currently fail or produce incorrect results because some rows contain null or blank values in key columns
- You have received data exports from source systems where missing values are represented as blank strings or null and need to substitute meaningful typed defaults before the data enters your reporting pipeline
- You want to ensure dashboard rows always display a value — showing Unknown for a missing country, 0 for a missing quantity, or a reference date for a missing date field — rather than propagating empty cells into visualizations
- You are building an ML data preprocessing step where null values in feature columns need to be imputed with a defined default before the dataset is used for model training or scoring
When to avoid it
- You want to forward-fill or back-fill missing values from adjacent rows rather than replacing them with a fixed default — use Fill Columns for time-series or ordered gap filling that propagates the nearest non-null value
- You need to filter out rows that contain null values rather than replacing them — use Filter on Falsy Values to remove incomplete rows from the dataset entirely
- Your missing values require a computed replacement derived from other columns or statistical measures like mean or median — Replace Null Values uses fixed typed defaults only, not computed imputation values
Where it fits in your Infoveave automation
Replace Null Values 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 Replace Null Values
Real scenarios where this transformation saves hours of manual work.
Replace Missing Customer Profile Fields Before Risk Scoring
A financial data team processes customer records for a credit risk scoring pipeline where several optional profile fields — country of residence, account open date, and active status — are nullable in the source system. Replace Null Values substitutes Country with Unknown, AccountOpenDate with a sentinel date, and IsActive with false for rows where these fields are absent. The scoring model downstream receives a complete dataset with no null inputs, eliminating null-driven scoring errors.
Fill Missing Inventory Fields Before Aggregation
A retail operations team processes daily inventory snapshots where the quantity on hand and reorder flag can be null for newly added SKUs in the source system. Replace Null Values fills QuantityOnHand with 0 and ReorderFlag with false for null rows. The downstream aggregation that sums total inventory and counts items needing reorder then includes every SKU correctly in its totals rather than silently dropping SKUs with null quantities.
Impute Missing Demographic Fields Before Patient Analytics
A health data team processes anonymized patient records where optional demographic fields — age group, region, and chronic condition flag — are null for patients who did not complete the full intake form. Replace Null Values fills Age with 0, Region with Not Specified, and ChronicConditionFlag with false. Patient analytics that segment by region and filter by chronic condition status then include all records rather than excluding patients with incomplete intake data.
See Replace Null Values in action
Input data (left) is transformed using the configuration below. The output table (right) is ready for dashboards or downstream steps.
Unknown02023-01-01trueInput Data
| Name | Country | Age | JoinedOn | IsActive |
|---|---|---|---|---|
| Alice | India | 30 | 2023-04-10 | true |
| Bob | ||||
| Carol | UK | 22 | 2023-02-18 | false |
Output Data
| Name | Country | Age | JoinedOn | IsActive |
|---|---|---|---|---|
| Alice | India | 30 | 2023-04-10 | true |
| Bob | Unknown | 0 | 2023-01-01 | true |
| Carol | UK | 22 | 2023-02-18 | false |
Configuration
Key fields to configure in the Infoveave workflow builder. Full reference available in the documentation.
Columns Map
Add one mapping entry per column you want to fill. Each entry specifies the Column Name (the exact column to watch for null or blank values), the Data Type (String, Number, Date, or Boolean), and the Value (the typed default to substitute). Only the columns you explicitly configure are processed — all other columns remain exactly as they are in the input, including any nulls they may contain.
Data Type
Select the type that matches the column's intended data type — String for text fields, Number for integer or decimal fields, Date for date or datetime fields, Boolean for true-or-false flag fields. The replacement value is validated against the selected type: number columns receive a numeric default, date columns receive a date-parseable string like 2023-01-01, and boolean columns receive true or false.
Value
Specify the exact default to substitute for null or blank cells in this column. String defaults can be any text — Unknown, N/A, Not Provided. Number defaults should be a numeric value — 0, -1, or a domain-appropriate sentinel. Date defaults should be a parseable date string. Boolean defaults should be true or false. The value is substituted only when a cell is null or blank — non-null values in that column are not affected.
Frequently asked questions
Everything you need to know about Replace Null Values in Infoveave.
Also in Column Operations — and what runs before & after
Transformations in the same family as Replace Null Values, often chained together in the same Infoveave workflow.
Part of Infoveave Data Automation
80+ transformations. Zero manual steps.
Replace Null Values is one of over 80 transformation activities available inside Infoveave workflows. Chain transformations together — no code, no exports, no waiting for IT.
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