Use this article to standardize inconsistent free-text values — such as Job Title, Industry, State, and Country — on your CRM records using the Data Logic module.
Free-text fields such as Job Title, Industry, State, and Country often become inconsistent over time, as different people or systems enter values in various formats. This makes segmentation, reporting, and record matching unreliable.
The Data Logic module lets you standardize these fields using a Blueprint — a table that maps every raw variant of a value to one standardized result. You can apply this logic in bulk to your existing records, and automatically to new records as they enter your CRM.
Standardize Data with the Data Logic Module
The Data Logic module lets you apply structured business logic to your CRM data using tables called Blueprints. Instead of standardizing one field value at a time, Data Logic can evaluate multiple fields together and update several CRM fields at once—based on a table of conditions you define or select, rather than branching Workflows or code.
Process Summary
- Pick a Blueprint that defines the standardization logic.
- Map Blueprint input columns to CRM fields to define how records are matched.
- Map Blueprint output columns to CRM fields to define what gets updated.
- Filter records down to only those you want to evaluate.
- Preview the changes, then apply them to the CRM.
Step-By-Step Instructions
Step 1: Pick a Blueprint
Navigate to the Data Logic module, and in the top menu, select the database and object type.
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Under 1. Pick Blueprint, choose from five sources:
Generate with AI
Explore Examples
Use Reference Data
Create from CSV
Select Existing
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Once a Blueprint is selected, click the Preview
icon beside the Blueprint field to confirm you've chosen the correct table and version before continuing.
The image above shows the 1. Pick Blueprint step in the Data Logic module, with Select Existing chosen and a Territory Assignment Blueprint selected from the dropdown.
Step 2: Map Input Columns to Define How Records Are Matched
Under 2. Input Mapping, click + Input and configure each Blueprint Column, the CRM Field to compare it against, and the Matching Criteria (Exact, Contains, Starts With, Regex, Number Between, and others) that fits how your data is structured.
Apply Match Options, such as Case Insensitive or Trim Whitespace, to each input to account for casing or spacing differences without needing to clean the data first.
In the example below, Country, State, and Industry are each mapped to their corresponding CRM fields using Exact matching, with Case Insensitive and Trim Whitespace enabled on Country and State.
The image above shows the Data Logic 2. Input Mapping step with three inputs configured: Country, State, and Industry, each mapped to a CRM field with Exact matching and Match Options applied.
Step 3: Map Output Columns to Define What Gets Updated
Under 3. Output Mapping, click + Output and configure the Column Name, the Field Name to update, and the Update Condition (Always, Field Empty, Field Not Empty, or Output Non-Empty) that controls how existing field values are handled.
Set a Fallback Value for each output, which is written to the field only when a record doesn't match any Blueprint row.
In the example below, four outputs are configured:
- Territory Owner is set to update only when the CRM field is currently empty, so an existing manual territory assignment is left in place, and only unassigned records receive the Blueprint's value.
- Sales Tier is set to Always, meaning it's overwritten with the Blueprint's value on every matching record, regardless of what's currently in the field.
- Account Type is set to Output Non-Empty, so the CRM field only updates when the Blueprint itself produces a non-empty value for that row, protecting the field from being cleared out by a match with no output defined.
- Region is also set to Always, so it updates on every match the same way Sales Tier does.
None of the four outputs has a Fallback Value set, so records that don't match any Blueprint row will be left unchanged for these fields.
Step 4: Filter Records to Evaluate
Under 4. Filter Records, set filter criteria to limit which records Data Logic evaluates. By default, Data Logic evaluates all records of the selected object type, so it's best practice to filter down to only the records relevant to your logic.
In the example below, the filters are set to evaluate only records with values in the Country, State, and Industry fields.
The image above shows the Data Logic 4. Filter Records step with three filters configured: Country exists, State exists, and Industry exists.
Step 5: Preview Changes and Update the CRM
Click the Review button to choose a run mode. Select Preview to simulate the operation and see which records would be updated, without making any changes to your CRM. This is the recommended first step, especially the first time you run a new Blueprint or configuration.
Once you've confirmed the results look correct, click Review again and select Update to apply the changes to your live CRM data.
The image above shows the Data Logic Run step with Update mode selected.
Once you're satisfied with the results, save your configuration as a template and set up automation to keep CRM fields consistent as new records come in and existing records change over time.
The examples below apply this process to three common standardization scenarios — Industry, State/Province and Country, and Job Title. Each example includes a ready-to-use Blueprint CSV and the specific Input and Output Mapping for that field.
Standardizing Industry Values
Industry values often become inconsistent over time, especially when reps enter free text at deal creation. Values like "SaaS," "software," "tech," and "cloud" may all describe the same category, but they appear as separate values on your company records. This fragmentation makes industry-based reporting unreliable and creates near-duplicate categories, complicating segmentation and campaign targeting.
The Data Logic module lets you define a Blueprint that maps every raw variant of an industry value to one standardized value, then apply that logic in bulk across your existing company records — and automatically to new records as they enter your CRM.
CSV for Industry Standardization Blueprint
Using the Data Logic module, this Blueprint will standardize the Industry field on company records. The Blueprint below maps common raw Industry values to a single standardized value for each category. Each row catches one raw variant using Contains matching, so a company record with any of these values in its Industry field is matched and updated to the standardized value.
The image above shows the Industry Standardization Blueprint preview in the Data Logic module, with rows 2 through 4 mapping raw Industry values—SaaS, software, and tech—to the standardized value Computer Software, rows 5 and 6 mapping medical and health to Healthcare, rows 7 and 8 mapping bank and finance to Financial Services, and rows 9 through 11 mapping edu, legal, and real estate to Education, Legal Services, and Real Estate, respectively.
Data Logic Configuration for Industry Standardization
To apply the Industry Standardization Blueprint to company records in the Data Logic module, configure the following:
- Under 1. Pick Blueprint, select Create from CSV (or Select Existing once it's uploaded under Operations > Blueprints), and choose this Blueprint.
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Under 2. Input Mapping, map:
- Blueprint column Industry (Contains) to
- CRM field Industry
- Using Contains matching criteria
- Enable the Case Insensitive Match Option, so casing differences in the raw values — such as "SaaS" versus "saas" — are still caught without needing separate rows for each variant.
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Under 3. Output Mapping, map:
- Blueprint column Standardized Industry (Always) to
- CRM field Industry
- Using the Always update condition
- Add a Fallback Value of "Uncategorized", so any company whose Industry value doesn't match one of the ten patterns still gets a clear, reportable value instead of being silently skipped.
- Under 4. Filter Records, leave the filter blank to include all company records, so that records with a blank Industry value are also evaluated and receive the Fallback Value rather than being skipped.
- Click Review to run in Preview mode first and confirm the results, then run again in Update mode to apply the changes to the CRM.
Standardizing State/Province and Country Values
Country and State/Region fields often accumulate inconsistent formats over time—full names, abbreviations, ISO codes, and regional variants that differ by language or convention. A Country field might contain "USA," "US," or "United States of America" interchangeably, while a State/Region field might mix official abbreviations with local shorthand, such as "P.E.I." for Prince Edward Island or "Q Roo" for Quintana Roo.
The Data Logic module allows you to create a Blueprint that standardizes every raw variant of a Country or State/Region value to a single, consistent value. You can then apply this logic in bulk to your existing company records and automatically update new records as they enter your CRM.
CSV for State/Province and Country Normalization Blueprint
Using the Data Logic module, this Blueprint standardizes both the Country and State/Province fields on company records in a single pass. Each row maps one raw variant of either a Country or a State/Region value to its correct, standardized name using Exact matching.
The image above shows the Country & State/Province Normalization Blueprint preview in the Data Logic module, with rows 5 through 8 mapping raw Country values—Mexico, United Mexican States, MEX, and MX—to the standardized value Mexico, and rows 21 through 26 mapping raw State/Province values to their standardized Mexican state names: Hgo, HD, and HG to Hidalgo, OAX and OA to Oaxaca, and Q Roo to Quintana Roo.
Data Logic Configuration for State/Province and Country Normalization
To apply the Country & State/Province Normalization Blueprint to company records in the Data Logic module, configure the following:
- Under 1. Pick Blueprint, select Create from CSV (or Select Existing once it's uploaded under Operations > Blueprints), and choose this Blueprint.
- Under 2. Input Mapping, map two inputs:
- Blueprint column Country (Exact) to CRM field Country/Region, using Exact matching criteria, with Case Insensitive, Trim Whitespace, and Blank Cell Matches Any Match Options enabled.
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Blueprint column State/Province (Exact) to CRM field State/Region, using Exact matching criteria, with the same Case Insensitive, Trim Whitespace, and Blank Cell Matches Any Match Options enabled.
- Under 3. Output Mapping, map two outputs:
- Blueprint column Country Normalized to CRM field Country/Region, using the Always update condition.
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Blueprint column State/Region Normalized to CRM field State/Region, using the Always update condition.
- Under 4. Filter Records: add a filter to include records where Country/Region is not empty, or State/Region is not empty, so only companies that need either normalization are included.
- Click Review to run in Preview mode first and confirm the results, then run again in Update mode to apply the changes to the CRM.
Standardizing Job Titles
Job Title often contains a wide mix of abbreviations, seniority prefixes, and phrasing variants for what is effectively the same role — "Sr. Software Engineer," "Senior Software Engineer," and "SWE" may all describe the same position, yet appear as distinct values in your contact records. This inconsistency makes it difficult to segment contacts by role or seniority, and complicates any reporting or targeting based on job function.
The Data Logic module enables you to create a Blueprint that maps each raw job title variant to a standardized title. It writes the standardized result to a separate field, keeping the original raw value intact.
CSV for Job Title Normalization Blueprint
Using the Data Logic module, this Blueprint standardizes job titles on contact records. Each row maps one raw job title variant to its standardized title using Exact matching.
Data Logic Configuration for Job Title Normalization
To apply the Job Title Normalization Blueprint to contact records in the Data Logic module, configure the following:
- Under 1. Pick Blueprint, select Create from CSV (or Select Existing once it's uploaded under Operations > Blueprints), and choose this Blueprint.
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Under 2. Input Mapping, map:
- Blueprint column job title variation to
- CRM field Job Title
- Using Exact matching criteria
- Enable the Case Insensitive Match Option, so casing differences in the raw values don't prevent a match.
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Under 3. Output Mapping, map:
- Blueprint column normalized job title to
- CRM field Job Title (Standardized)
- Using the Always update condition
- Under 4. Filter Records, filter to records where Job Title is not empty, so only contacts with an existing job title are evaluated. Click Review to run in Preview mode first and confirm the results, then run again in Update mode to apply the changes to the CRM.
Advanced Techniques
Export Field Data to Create a Data Logic Blueprint
In the Cleanse Data module, when you've identified a field with variations that need management, you can export the values as a Blueprint CSV to use with the Data Logic module. Blueprints scale to thousands of values, enforce mapping across all records continuously, and update instantly with a simple CSV edit.
- Select the field under Step 2. Explore Field.
- Click the Layout tab, then drag it into the field in the Visible Fields list.
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In Step 4. Value Distribution, change the Rows per page so that all the values are visible at once (without needing to click through pages). For example, if the field has 55 different values, change Rows per page to 100.
- Click the blue download icon
next to the 4. Value Distribution heading to export the CSV.
- Insycle automatically adds a Mapped column to the exported CSV. Use this column to map each incorrect value to its correct counterpart, upload the CSV as a Blueprint, and configure Data Logic to automatically standardize these values ongoing.
Frequently Asked Questions
What happens when a record does not match any Blueprint row?
When a record does not match any row in the Blueprint, Data Logic provides two ways to handle it: a Fallback Value configured in the Output Mapping, or a Fallback Row added as the last row of the Blueprint itself.
Fallback Value
A Fallback Value is configured per output field in Data Logic's Output Mapping. When a record matches no Blueprint row, Data Logic writes the fallback value to that field instead, subject to the same update condition configured for the output. If no fallback value is configured for an output field, that field is left unchanged on non-matching records.
The screenshot below shows the Output Mapping configured with Blueprint Column Region mapped to CRM Field Region using the Always update condition, with a Fallback Value of Unassigned. Records that do not match any row in the Blueprint will have their Region field set to "Unassigned."
Fallback Row
A Fallback Row is an alternative way to define what happens to non-matching records directly inside the Blueprint. The Fallback Row is the last row in the Blueprint CSV with all input columns left blank. When Blank Cell Matches Any is enabled on all input columns in Data Logic's Input Mapping, each blank cell acts as a wildcard — so the row catches any record not matched by an earlier row and is evaluated like any other matched row.
The screenshot below shows the Territory Assignment Blueprint preview with the Fallback Row highlighted at the bottom. Every input cell in that row — Country, State, and Industry — is blank. With Blank Cell Matches Any enabled on all three input columns, the row catches any record not matched by an earlier row and assigns Region = Unassigned, Territory Owner = unassigned-pool@company.com, and Sales Tier = Review, making unmatched records immediately visible.
The screenshot below shows the Data Logic Input Mapping for the Territory Assignment example. Country and State both have Blank Cell Matches Any enabled alongside Case Insensitive and Trim Whitespace, enabling the blank cells in the Fallback Row to act as wildcards for those columns. Industry has Case Insensitive and Trim Whitespace enabled, but not Blank Cell Matches Any — rows that leave Industry blank skip that condition rather than matching any value, which is the default behavior for a non-Exact column.
See Fallback Row vs. Fallback Value in the Module Overview: Data Logic for guidance on when to use each approach, including how to avoid configuring both for the same output field.
Can Data Logic standardize more than one field at once?
Yes, Data Logic can standardize more than one field in a single Blueprint and configuration. Each field you want to standardize gets its own Input Mapping and corresponding Output Mapping, and all of them are evaluated together when you run the Blueprint. For example, a single Blueprint can normalize both Country/Region and State/Region on the same company records in one pass, rather than requiring a separate Blueprint and run for each field.
Check out the Data Logic & Blueprints FAQs to find more answers to common questions about creating, editing, and applying Data Logic and Blueprints.
Additional Resources
Related Help Articles
- Module Overview: Data Logic
- Blueprints
- Cleanse Data
- Explore Database Fields and Values
- Map Values From One Field to Another
Related Blog Posts