Workflow Templates for Google Sheets
Overview
Workflow templates turn a multi-step data cleaning job into one click. Each template chains Standardize, Smart Deduplicate and Fuzzy Match steps automatically, then writes the results to a review sheet so your source data is never touched. If the result sheet already exists, the template reuses it and replaces its previous results.
Templates are available in three categories: Accounting, E-Commerce and HR / RevOps. They are a Data Nova feature, so every template works exactly the same regardless of your dataset size.
| Template | Category | What It Does | Best For |
|---|---|---|---|
| AP Vendor Reconciliation | Accounting | Matches an accounts payable list against your master vendor table with a confidence score | Vendor master data clean-ups and supplier onboarding |
| Order-to-Shipping Reconciliation | E-Commerce | Matches orders against your shipping log and flags unfulfilled orders | Fulfilment audits and customer support triage |
| User List Consolidation | HR / RevOps | Merges two user lists and flags people who appear in both systems | CRM, HR and RevOps system migrations |
How to Open Templates
In Google Sheets, go to Extensions > Flookup Data Wrangler > Standard Data Cleaning > Templates. Templates is the first card in the Standard Data Cleaning sidebar, above Fuzzy Match Data. You will see the template browser grouped by category. Click a template to open its detail view, set your inputs and run it.
How Templates Work
Every template follows the same flow:
- Set your inputs. Enter the ranges and column numbers the template asks for. Most inputs have sensible defaults; only the range inputs are required.
- Click Run. The template standardises names, flags internal duplicates and fuzzy-matches your records in sequence.
- Review the results. The result sheet contains a review table that shows each source record, its best match, the match score, a status and a confidence label.
Templates are a Data Nova feature and are included in every Data Nova purchase. Each run consumes credits for the credit-based operations that it performs and is subject to the menu function limits.
Output and matching caveats
- The result sheet is reused when it already exists. Its previous template results are replaced.
- !No match found means that no record met the selected match threshold. Review the threshold and source ranges before treating the result as final.
- Output rows are generated by the selected fuzzy matching threshold. They are candidates for review and are not proof that two records refer to the same entity.
- Confidence labels summarise the match score. They do not replace checking the underlying values.
- Duplicate groups depend on the first-column key used by Smart Deduplicate. Records with different first-column values can remain in the same broader review group or be left unresolved.
Template errors
- No data found in the selected range. Select a range containing the records required by the template.
- The range is larger than the credit check allows. Reduce the range size or use Data Cleaning Automation for larger datasets.
- You have used all your credits. Add credits before running the template again.
- Timed out before completion. Reduce the range size and run the template again.
AP Vendor Reconciliation
Match an accounts payable list against your master vendor table. The template standardises vendor names, flags duplicate entries and matches each AP record to the best master vendor with a confidence score. Results are written to the AP Vendor Recon result sheet, which is reused when it already exists.
Workflow
Standardize → Smart Dedupe → Fuzzy Match → Review
Inputs
| Field | Required | Description | Default |
|---|---|---|---|
| AP list range | Yes | The range containing your accounts payable records | - |
| Vendor name column in AP list | No | Column number holding the vendor name in your AP list | 1 |
| Master vendor table range | Yes | The range of your master vendor table | - |
| Vendor name column in master table | No | Column number holding the vendor name in the master table | 1 |
| Match threshold (0-1) | No | How strict the fuzzy match should be | 0.82 |
Output Columns
| Column | Meaning |
|---|---|
| Source Vendor Name | The vendor name as it appears in your AP list |
| Standardized Name | The cleaned vendor name used for matching |
| Best Match | The closest vendor from your master table |
| Match Score | Similarity score of the best match |
| Status | Matched or unmatched |
| Confidence | High, medium or low label derived from the match score |
Order-to-Shipping Reconciliation
Match orders against your shipping log and flag unfulfilled orders. The template standardises customer names from your orders export and matches each order to the best record in your shipping log using fuzzy customer matching. Results are written to the Order Ship Recon review sheet, which is reused when it already exists.
Workflow
Standardize → Fuzzy Match → Review
Inputs
| Field | Required | Description | Default |
|---|---|---|---|
| Orders list range | Yes | The range containing your orders export | - |
| Order ID column in orders list | No | Column number holding the order ID | 1 |
| Customer name column in orders list | No | Column number holding the customer name | 2 |
| Shipping log range | Yes | The range containing your shipping log | - |
| Order ID column in shipping log | No | Column number holding the order ID in the shipping log | 1 |
| Customer name column in shipping log | No | Column number holding the customer name in the shipping log | 2 |
| Match threshold (0-1) | No | How strict the fuzzy match should be | 0.85 |
Output Columns
| Column | Meaning |
|---|---|
| Order ID | The order ID from your orders export |
| Order Customer | The customer name from your orders export |
| Shipping Order ID | The matching order ID from the shipping log |
| Match Score | Similarity score of the best match |
| Status | Matched or Unmatched |
| Confidence | High, medium or low label derived from the match score |
User List Consolidation
Merge two user lists and flag people who appear in both systems. The template standardises names from two systems (HR, CRM, RevOps), detects which people appear in both lists, flags internal duplicates and produces a consolidated list of unique users. Results are written to a new User List Consolidation sheet.
Workflow
Standardize List A → Standardize List B → Smart Dedupe List A → Fuzzy Match → Review → Consolidated Unique Users
Inputs
| Field | Required | Description | Default |
|---|---|---|---|
| List A range | Yes | The range containing the first user list | - |
| Name column in List A | No | Column number holding the name in List A | 1 |
| List B range | Yes | The range containing the second user list | - |
| Name column in List B | No | Column number holding the name in List B | 1 |
| Match threshold (0-1) | No | How strict the fuzzy match should be | 0.85 |
Output Columns
| Column | Meaning |
|---|---|
| List A Name | The name as it appears in List A |
| Standardized Name | The cleaned name used for matching |
| Match in List B |
The closest matching name from List B or
!No match found
if no value meets the threshold
|
| Match Score | Similarity score of the best match |
| Status | Matched or Unmatched |
| Confidence | High, medium or low label derived from the match score |
Frequently Asked Questions
What are workflow templates?
Workflow templates are pre-built multi-step processes that chain the Standard Data Cleaning operations together. You provide your ranges and the template runs standardisation, deduplication and fuzzy matching automatically, writing the results to a review sheet.
Are workflow templates included with Data Nova?
Yes. Workflow templates are a Data Nova feature. Data Nova includes all templates in every category and runs continue until your credit balance lasts.
Does running a template modify my source data?
Each template writes its results to a review sheet, reusing the sheet if it already exists. The previous template results are replaced, but the source data is not modified.
What is the match threshold in a template?
The threshold is a value from 0 to 1 that controls how strict the fuzzy matching step is. A higher threshold returns fewer, more confident matches. Each template ships with a sensible default that you can adjust before running.