---
Brand: klarmetrics.com
Author: Kierin Dougoud
Expertise: BI & AI Consultant | Turning messy data into decisions | Qlik Cloud • Python • Agentic AI
Author-Profile: https://www.linkedin.com/in/mkierin/
Canonical-URL: https://klarmetrics.com/19-qlik-data-quality/
---

# Qlik Sense Data Quality Framework: Implementing Quality Gates in Your ETL Pipeline

*This is Article 19 of the [Qlik Sense Data Modeling Course](https://klarmetrics.com/qlik-sense-data-modeling-course/).*

# 📚 Qlik Sense Course – Article 19 of 28

← **Previous Article:** [Flag-Based Modeling](18-qlik-flag-based-modeling)

→ **Next Article:** [Error Handling](20-qlik-error-handling)

**What is a Data Quality Framework?** A systematic approach to validating data quality at EVERY level of the ETL process – from staging through transform to the final data model. Early detection minimizes the effort required for later error correction.

# What Will You Learn About the Data Quality Framework?

After this guide, you will be able to:

* Implement and automate **Quality Gates** on all ETL layers.

* Use Quality Flags for automatic data validation and for **performant** analyses. (Topic of the [previous article](https://klarmetrics.com/18-qlik-flag-based-modeling))

* Build a Quality Dashboard for transparent monitoring.

* Detect critical errors using thresholds and variables.

**Time investment:** 25 min reading + 4 hrs hands-on

**Quick Win:** In 20 minutes you’ll have your first quality checks implemented

# How Do Quality Gates Work at Each Level?

**The problem:** Faulty data spreads unnoticed through all layers and corrupts business decisions! Fixing an error in the application is 10x more expensive than fixing it in the [load script](https://klarmetrics.com/01-qlik-load-data/).

**The solution:** Quality Gates after EVERY processing step – detect and fix problems early! This three-layer model is the foundation for all robust Qlik architectures.

**The three validation layers:**

Layer
Validation Purpose
Examples

**1. Staging**
Structural integrity of the import
File loaded? Row count? Data types? (Primary: [Loading Data](https://klarmetrics.com/01-qlik-load-data/))

**2. Transform**
Compliance with business rules
Required fields? Valid values? Duplicates?

**3. Model**
Relationships & model hygiene
No [Synthetic Keys](https://klarmetrics.com/qlik-sense-synthetic-keys/)? Referential integrity?

# What Are the 6 Data Quality Dimensions?

Data quality can be divided into six measurable dimensions – in the Qlik script, each dimension represents a group of `If()` checks and flags. These dimensions are defined by the [DAMA Data Management Body of Knowledge](https://www.dama.org/cpages/body-of-knowledge), the authoritative reference for data quality standards.

# How Is Completeness Evaluated?

Are all expected data present (no NULL values in required fields)?

// Check: Required fields populated?
If(IsNull(CustomerID) OR Len(Trim(CustomerID)) = 0, 1, 0) as DQ_MissingCustomerID

# How Is Accuracy Ensured?

Are the values correct and within valid ranges (contradiction to reality)?

// Check: Amount in reasonable range?
If(Amount <= 0 OR Amount > 1000000, 1, 0) as DQ_InvalidAmount

# How Important Is Consistency?

Are values logically consistent across different fields (e.g. totals match)?

// Check: ShipDate not before OrderDate?
If(ShipDate < OrderDate, 1, 0) as DQ_InvalidDateOrder

# What Is Validity?

Do values match the expected format (e.g. numeric, date, defined list)?

// Check: Email contains @ and .?
If(NOT Match(Email, '*@*.*'), 1, 0) as DQ_InvalidEmail

# What Is Uniqueness?

Are primary keys truly unique with no duplicates?

// Check: Duplicates by OrderID? (Checked with GROUP BY in the script)
Count(*) as DuplicateCount
GROUP BY OrderID
HAVING Count(*) > 1

# How Important Is Timeliness?

Is the data current enough for analysis (e.g. load date vs. today)?

// Check: Last update more than 24h ago?
If(Today() - LoadDate > 1, 1, 0) as DQ_DataStale

# How to Implement Structural Validation in the Staging Layer?

In the staging layer, right after loading raw data, you check the basic integrity of the import.

// Load raw data
STG_Sales:
LOAD
    OrderID,
    CustomerID,
    ProductID,
    OrderDate,
    Amount,
    Quantity
FROM
(ooxml, embedded labels);

// Quality Check 1: Was the file loaded at all?
LET vRowCount = NoOfRows('STG_Sales');
TRACE [QC] Staging: Loaded $(vRowCount) rows;

IF $(vRowCount) = 0 THEN
    TRACE Staging: No data loaded! Stopping script.
    SET vQuality_Staging = 'FAIL';
    EXIT SCRIPT;
ELSE
    SET vQuality_Staging = 'PASS';
ENDIF

**Explanation:**

* **NoOfRows():** Counts records in the loaded table.

* **EXIT SCRIPT:** Stops on critical error (e.g. no data loaded) – this is part of [Error Handling](https://klarmetrics.com/20-qlik-error-handling/).

# How Do I Check the Row Count Range?

Check whether the record count is within the expected range to detect export errors or data corruption early.

// Define expected range
LET vExpectedMin = 1000;
LET vExpectedMax = 1000000;

IF $(vRowCount) < $(vExpectedMin) OR $(vRowCount) > $(vExpectedMax) THEN
    TRACE Staging: Row count outside expected range! Check the export.;
    SET vQuality_Staging_RowCount = 'WARN';
ELSE
    SET vQuality_Staging_RowCount = 'PASS';
ENDIF

# How Does Data Type Validation Work?

Data type validation is essential. If a column that should be numeric (e.g. `Amount`) is loaded as text, all aggregations will fail later.

// Check data types
Temp_DataTypeCheck:
LOAD
    Count(*) as TotalRows,
    Sum(If(NOT IsNum(Amount), 1, 0)) as AmountNotNumeric,
    Sum(If(NOT IsNum(Quantity), 1, 0)) as QuantityNotNumeric
RESIDENT STG_Sales;

LET vAmountNotNumeric = Peek('AmountNotNumeric', 0, 'Temp_DataTypeCheck');
TRACE [QC] Staging: Amount NOT numeric in $(vAmountNotNumeric) rows;

DROP TABLE Temp_DataTypeCheck;

**Explanation:** `IsNum()` checks whether the value is numeric. If `AmountNotNumeric` is greater than 0, you need to correct the load statement.

# How to implement business rules and quality flags in Qlik Sense?

In the transform layer, you validate the logic and create the actual **Quality Flags**. This is the most performant method for error marking (see [Flag-Based Modeling](https://klarmetrics.com/18-qlik-flag-based-modeling/)).

TRF_Sales:
LOAD
    OrderID,
    CustomerID,
    ProductID,
    Date(OrderDate) as OrderDate,
    Amount,
    Quantity,

    // Quality Flags - Completeness
    If(IsNull(CustomerID) OR Len(Trim(CustomerID)) = 0, 1, 0)
        as DQ_MissingCustomerID,
    If(IsNull(ProductID) OR Len(Trim(ProductID)) = 0, 1, 0)
        as DQ_MissingProductID,

    // Quality Flags - Range Validation
    If(Amount <= 0, 1, 0) as DQ_ZeroOrNegativeAmount,
    If(Quantity <= 0, 1, 0) as DQ_InvalidQuantity,

    // Quality Flags - Consistency
    If(OrderDate > Today(), 1, 0) as DQ_FutureOrderDate,

    // Composite Quality Flag: Marks every faulty record
    If(IsNull(CustomerID) OR Amount <= 0 OR OrderDate > Today() OR Quantity <= 0,
  	   1, 0) as DQ_HasAnyIssue

FROM (qvd);

**Advantages of Quality Flags:** Instead of deleting faulty records, you mark them. This way, business users can later **in the frontend** use Set Analysis (e.g. Sum({<DQ_HasAnyIssue={'0'}>} Sales)) to analyze exclusively the “good” data, while the faulty records are preserved for root cause analysis (error debugging). You can use the [Qlik ErrorMode system variable](https://help.qlik.com/en-US/cloud-services/Subsystems/Hub/Content/Sense_Hub/Scripting/SystemVariables/ErrorMode.htm) to control script behavior when quality gates fail.

# How Do You Calculate the Quality Summary?

Summarize the error counts to monitor the health of the data.

Quality_Transform:
LOAD
    'TRF_Sales' as TableName,
    Count(*) as TotalRecords,
    Sum(DQ_HasAnyIssue) as RecordsWithIssues,
    1 - (Sum(DQ_HasAnyIssue) / Count(*)) as QualityScore
RESIDENT TRF_Sales;

// Log quality score
LET vQualityScore = Peek('QualityScore', 0, 'Quality_Transform');
TRACE [QC] Transform: Quality Score = $(vQualityScore);

// Threshold Check
IF $(vQualityScore) < 0.95 THEN
    TRACE Transform: Quality Score below 95% threshold!
    SET vQuality_Transform = 'ALERT';
ELSE
    SET vQuality_Transform = 'PASS';
ENDIF

**Explanation:** `QualityScore` is 1 minus the error rate, giving you a percentage of “good” records. When the defined `Threshold` (here 95%) is not met, an **ALERT** is triggered.

# How is referential integrity enforced in the model layer?

In the final model, the relationship between fact and dimension tables must be validated. This is primarily about avoiding *orphaned records*. This is closely related to avoiding [Synthetic Keys](https://klarmetrics.com/qlik-sense-synthetic-keys/).

// Load dimensions
Dim_Customer:
LOAD CustomerID, CustomerName
FROM (qvd);

// Facts with Referential Integrity Check
TRF_Sales:
LOAD
    OrderID,
    CustomerID,
    ProductID,
    Amount,

    // Does CustomerID exist in Dim_Customer?
    If(NOT Exists(CustomerID, CustomerID), 1, 0) as DQ_OrphanedCustomer,

    // Does ProductID exist in Dim_Product?
    If(NOT Exists(ProductID, ProductID), 1, 0) as DQ_OrphanedProduct

FROM (qvd);

**Explanation:**

* **Exists(Field, Value):** Checks whether the value of `CustomerID` in the currently loaded table already exists in the `CustomerID` field of the `Dim_Customer` table (the loaded dimension).

* **DQ_OrphanedCustomer:** 1 when the `CustomerID` exists in the Sales table but **no** matching counterpart is found in the dimension.

**Why is this important?** Orphaned records cause NULL associations, which can lead to missing values in your dimensions and **incorrect aggregations**.

In production environments, quality gates are most effective when embedded in a [three-stage ETL architecture](https://klarmetrics.com/24-qlik-three-stage-architecture/), where each layer – staging, transform, and model – validates data at the appropriate level.

# How Does Duplicate Detection Work?

Duplicates are one of the most common causes of unreliable metrics. Identify them before processing the data further.

// Identify duplicates (by OrderID)
Temp_DuplicateCheck:
LOAD
    OrderID,
    Count(*) as DuplicateCount
RESIDENT STG_Sales
GROUP BY OrderID;

// Add duplicate flag
LEFT JOIN (STG_Sales)
LOAD
    OrderID,
    If(DuplicateCount > 1, 1, 0) as DQ_IsDuplicate,
    DuplicateCount
RESIDENT Temp_DuplicateCheck;

// Log duplicate flag
LET vDuplicateCount = Sum(If(DQ_IsDuplicate = 1, 1, 0), 'STG_Sales');
TRACE [QC] Duplicates: Found $(vDuplicateCount) duplicate records;

# How Does the Deduplication Strategy Work?

Choose a strategy: The best results come from keeping the “newest” or “most correct” record:

// Strategy 2: Keep newest record (MOST IMPORTANT STRATEGY)
TRF_Sales_Latest:
LOAD *
RESIDENT STG_Sales
// Keep all non-duplicates AND only the newest duplicate record
WHERE DQ_IsDuplicate = 0
   OR RecNo() = FirstSortedValue(RecNo(), -OrderDate, OrderID);

**Explanation:** This advanced logic keeps all unique records and, for duplicates, selects only the record with the latest `OrderDate` (or the highest `SourceRowNumber` if `OrderDate` is not unique). This type of [data transformation](https://klarmetrics.com/05-qlik-data-transformation/) is essential for invoices and orders.

# What Is Cross-Field Validation?

Use consistency checks to uncover logical errors between related fields.

TRF_Orders:
LOAD
    OrderID,
    Date(OrderDate) as OrderDate,
    Date(ShipDate) as ShipDate,
    TotalAmount,

    // Cross-Field Consistency Flags
    If(ShipDate < OrderDate, 1, 0) as DQ_ShipBeforeOrder,
    If(Abs(TotalAmount - (OrderAmount + ShippingCost)) > 0.01, 1, 0)
  	    as DQ_TotalMismatch,
    //... additional checks
FROM (qvd);

# How Do I Integrate the Quality Dashboard and Set Up Alerting?

Collect quality metrics over time (historization) and use Qlik to react automatically to problems.

// Summarize and historize current load quality
//... (Summary logic)...

// Append to historical log
CONCATENATE(Quality_History)
LOAD * RESIDENT Quality_Summary;

// Save historical data
STORE Quality_History INTO (qvd);

# How Does Automated Alerting with Thresholds Work?

We use the calculated `vQualityScore` from Layer 2 to send an automated notification if quality falls below a threshold.

// Define thresholds
LET vQualityThreshold = 0.95;  // 95% quality required

// Calculate quality score
//... (Logic to calculate vQualityScore)...

// Alert Logic
IF $(vQualityScore) < $(vQualityThreshold) THEN
    TRACE Quality Score $(vQualityScore) below threshold!

    // Trigger automated action
    // Here e.g. Qlik Automate can send an email:
    // EXECUTE;

    TRACE See Qlik Automate Email notifications for setup.
ENDIF

# What Are the Best Practices for the Data Quality Framework?

The following best practices ensure your framework remains efficient and maintainable.

**Layered Validation:**

* [ ] Staging Layer: Structural checks (File loaded? Row count?)

* [ ] Transform Layer: Business rules (Required fields? Ranges?)

* [ ] Model Layer: Relationships ([Data model problems](https://klarmetrics.com/09-qlik-data-modeling-problems/), No orphans?)

**Quality Flags:**

* [ ] Mark faulty records, DON’T delete them

* [ ] Clear naming convention (DQ_ prefix)

* [ ] Composite flags for “HasAnyIssue”

**Quality Logging & Performance:**

* [ ] Store quality metrics in QVD (historical data)

* [ ] Quality dashboard for monitoring

* [ ] Apply [performance strategies](https://klarmetrics.com/qlik-sense-performance-optimization/) before critical checks

# How Does Performance Affect Quality?

Quality checks have a performance overhead (approx. +11% to +55% additional load time) – but the cost of *incorrect* business decisions far exceeds this effort. It’s a necessary trade-off.

Scenario
Load Time (1M Records)
Overhead

Without Quality Checks
45 sec
–

With Staging Checks
50 sec
+11%

With Transform Checks
57 sec
+27%

**With All Checks**
**70 sec**
**+55%**

**Conclusion:** +55% load time, but 100% confidence in data quality – a good trade-off! Ideally, keep checks at the transform layer to keep staging performance as fast as possible.

# How to troubleshoot Qlik Sense Data Quality Framework?

**Problem: Quality checks slow down loads too much**

**Solution 1: Selective Checks**

Don’t check ALL records, just a sample:

// 10% sample for fast checks
WHERE Rand() < 0.1

**Solution 2: Parallel Processing**

Use separate QVDs for quality checks – load main data in parallel:

// Main Load
STORE TRF_Sales INTO (qvd);

// Quality Checks in separate job
// Load TRF_Sales.qvd and run checks

**Problem: Too many quality flags make the model confusing**

**Solution: Composite Flags**

Combine multiple checks into one flag:

If(DQ_MissingCustomerID = 1 OR
   DQ_ZeroOrNegativeAmount = 1 OR
   DQ_FutureOrderDate = 1,
   1, 0) as DQ_HasAnyIssue

**Problem: Quality alerts fire too frequently**

**Solution: Realistic Thresholds**

100% quality is unrealistic – set practical thresholds:

// Only alert on actual degradation
IF $(vQualityScore) < 0.95 THEN
    // Alert
ENDIF

# What Are the Next Steps?

You can now implement systematic data quality gates! Next:

**1. Error Handling:** What happens when quality checks fail and the script needs to stop? [Error Handling](20-qlik-error-handling) shows you how to catch critical errors and terminate the script in a controlled manner.

**2. Performance Optimization:** To minimize the performance overhead, you need to further optimize the [load times](https://klarmetrics.com/qlik-sense-performance-optimization/) of your applications.

# How to implement quality gates in an ETL pipeline?

* [Error Handling](20-qlik-error-handling) – What to do when quality checks fail?

* [Flag-Based Modeling](18-qlik-flag-based-modeling) – Flags for ultimate performance and simple Set Analysis

**Previous:** [Flag-Based Modeling](https://klarmetrics.com/18-qlik-flag-based-modeling/) | **Next:** [Error Handling](https://klarmetrics.com/20-qlik-error-handling/)

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