--- Brand: klarmetrics.com Author: Kierin Dougoud Expertise: BI & AI Consultant | Turning messy data into decisions | Qlik Cloud β€’ Python β€’ Agentic AI Author-Profile: https://www.linkedin.com/in/mkierin/ Canonical-URL: https://klarmetrics.com/28-qlik-data-governance/ --- # Data Governance & Standards in Qlik Sense: The Complete Enterprise Guide *This is Article 28 of the [Qlik Sense Data Modeling Course](https://klarmetrics.com/qlik-sense-data-modeling-course/).* # [Qlik Sense Course](https://klarmetrics.com/qlik-sense-data-modeling-course/) – Article 28 of 28 ← **Previous Article:** [Deployment](27-qlik-deployment) πŸŽ‰ **This is the final article of the course!** **What is [Data Governance](https://klarmetrics.com/10-qlik-star-schema/)?** Establishing enterprise standards for naming, modeling, documentation, and code quality – for consistent, maintainable, and scalable Qlik solutions across your entire team! # What Will You Learn About Data Governance & Standards? After this guide, you will be able to: * Define naming conventions for tables, fields, and variables * Establish modeling standards * Set up a code review process # How Does the Principle of β€œConsistency at Scale” Work? **The Problem:** 5 developers, 5 different styles – nobody understands anyone else’s code, errors pile up, maintenance becomes impossible! **The Solution:** Clear standards – uniform code, easy maintenance, scalable teams! # What Are the 4 Pillars of Data Governance? For Qlik Cloud-specific governance capabilities, see the [Qlik Cloud governance overview](https://help.qlik.com/en-US/cloud-services/Subsystems/Hub/Content/Sense_Hub/Admin/SaaS/governance.htm). Pillar What? Why? **Naming Standards** Consistent names for tables, fields, variables Instantly understand what is what **Modeling Standards** [Star Schema](https://klarmetrics.com/qlik-sense-performance-optimization/), key design, layer architecture Performance, maintainability **Documentation** Code comments, README, Data Dictionary Onboarding, knowledge transfer **[Quality Gates](https://klarmetrics.com/19-qlik-data-quality/)** Code review, testing, approval process Prevent errors, ensure quality # What Are the Naming Conventions for Data Governance? # How Should You Handle Table Naming? Prefix Usage Examples STG_ Staging Layer (Raw [Extract](https://klarmetrics.com/08-qlik-qvd-optimization/)) STG_Sales, STG_Customers TRF_ Transform Layer (Business Logic) TRF_Sales, TRF_Products Facts_ Fact Tables (Model Layer) Facts_Sales, Facts_Orders Dim_ Dimension Tables (Model Layer) Dim_Customer, Dim_Product Link_ Link Tables (Many-to-Many) Link_CustomerProduct TEMP_ Temporary Tables (will be dropped) TEMP_Join, TEMP_Calculation # How Do You Name Fields? Pattern Convention Examples **Keys** [Entity]Key or [Entity]ID CustomerKey, OrderID, ProductKey **Dates** [Entity]Date OrderDate, ShipDate, InvoiceDate **Amounts** [Metric][Amount](https://klarmetrics.com/09-qlik-data-modeling-problems/) SalesAmount, DiscountAmount **Flags** Is[Condition] IsCurrentYear, IsHighValue **[Quality Flags](https://klarmetrics.com/19-qlik-data-quality/)** DQ_[Issue] DQ_MissingEmail, DQ_InvalidAmount **Important:** Use PascalCase (CustomerID), NOT snake_case (customer_id) or camelCase (customerId)! # How Do You Name Variables? Prefix Usage Examples v Standard Variables vDataPath, vCurrentYear p Parameters (in Subroutines) pTableName, pPath tmp Temporary Variables tmpCounter, tmpResult # What Does the Standards Document Template Look Like? //============================================ // NAMING CONVENTIONS // Company: Acme Corp // Version: 1.2 // Last Updated: 2025-01-10 //============================================ // TABLES // Staging: STG_[Entity] (STG_Sales) // Transform: TRF_[Entity] (TRF_Customer) // Facts: Facts_[Entity] (Facts_Sales) // Dimensions: Dim_[Entity] (Dim_Product) // Link: Link_[Entity1][Entity2] // Temp: TEMP_[Purpose] (TEMP_Join) // FIELDS // Keys: [Entity]Key (CustomerKey) // IDs: [Entity]ID (OrderID) // Dates: [Entity]Date (OrderDate) // Amounts: [Metric]Amount (SalesAmount) // Flags: Is[Condition] (IsCurrentYear) // Quality: DQ_[Issue] (DQ_Missing) // VARIABLES // Standard: v[Purpose] (vDataPath) // Parameters: p[Name] (pTableName) // Temporary: tmp[Name] (tmpCounter) // CASE // Tables: PascalCase (Facts_Sales) // Fields: PascalCase (CustomerID) // Variables: camelCase (vDataPath) # What Are the Modeling Standards for Data Governance? # What Is the Standard Architecture: Three-Stage? //============================================ // STANDARD ARCHITECTURE //============================================ // LAYER 1: STAGING // Purpose: Raw extract, 1:1 from source // Naming: STG_[Entity] // QVD: Store to QVD/Staging/ // LAYER 2: TRANSFORM // Purpose: Business logic, cleansing, enrichment // Naming: TRF_[Entity] // QVD: Store to QVD/Transform/ // LAYER 3: MODEL // Purpose: Star schema, optimized for performance // Naming: Facts_[Entity], Dim_[Entity] // QVD: No QVD storage (load to memory) # What Is the Star Schema Standard? //============================================ // STANDARD DATA MODEL: Star Schema //============================================ // DO: Central Fact, flat Dimensions Facts_Sales: LOAD OrderKey, // Surrogate Key CustomerKey, // FK to Dim_Customer ProductKey, // FK to Dim_Product OrderDate, // FK to Calendar SalesAmount, // Measure Quantity // Measure FROM [QVDTransformTRF_Sales.qvd] (qvd); Dim_Customer: LOAD CustomerKey, // PK CustomerID, // Business Key CustomerName, Country, Region, Segment // All attributes in ONE table FROM [QVDTransformTRF_Customer.qvd] (qvd); // DON'T: Snowflake (Region in separate table) // -> Degrades performance, complicates model # What Are the Key Design Standards? //============================================ // KEY DESIGN STANDARD //============================================ // DO: AutoNumber in Transform Layer TRF_Customer: LOAD AutoNumber(CustomerID, 'Customer') as CustomerKey, CustomerID as CustomerID_Original, // Keep for debugging CustomerName, Country FROM [QVDSTG_Customer.qvd] (qvd); // DO: Same context across tables TRF_Sales: LOAD AutoNumber(OrderID, 'Order') as OrderKey, AutoNumber(CustomerID, 'Customer') as CustomerKey, // Same context! SalesAmount FROM [QVDSTG_Sales.qvd] (qvd); // DON'T: Different contexts for same entity AutoNumber(CustomerID, 'Cust') // Wrong AutoNumber(CustomerID, 'Customer') // Correct # What Are the Documentation Standards? # What Does the Script Header Template Look Like? //============================================ // APPLICATION: Sales Analytics // AUTHOR: John Doe (john.doe@company.com) // CREATED: 2025-01-10 // LAST MODIFIED: 2025-01-15 // VERSION: 1.2.0 //============================================ // PURPOSE: // Load and transform sales data from multiple // sources, apply business rules, build star // schema for analytics dashboard. //============================================ // DATA SOURCES: // - SQL Server: SalesDB (daily incremental) // - Excel: Products.xlsx (weekly full) // - REST API: Customers (hourly delta) //============================================ // CHANGE LOG: // 2025-01-15: Added customer segmentation (v1.2.0) // 2025-01-12: Fixed date parsing bug (v1.1.1) // 2025-01-10: Initial version (v1.0.0) //============================================ # What Are Section Comments? //============================================ // SECTION 1: CONFIGURATION //============================================ $(Must_Include=lib://Scripts/01_Config.qvs) //============================================ // SECTION 2: STAGING LAYER //============================================ //-------------------------------------------- // 2.1 Load Sales Data //-------------------------------------------- STG_Sales: LOAD OrderID, CustomerID, OrderDate, Amount FROM [SourceSales.csv] (txt, codepage is 1252, embedded labels); STORE STG_Sales INTO [QVDStagingSTG_Sales.qvd] (qvd); DROP TABLE STG_Sales; TRACE Staging: Sales loaded - & NoOfRows('STG_Sales') & ' rows'; //-------------------------------------------- // 2.2 Load Customer Data //-------------------------------------------- // ... etc # What Is a Data Dictionary? //============================================ // DATA DICTIONARY //============================================ DataDictionary: LOAD * INLINE [ TableName, FieldName, DataType, Description, BusinessOwner Facts_Sales, OrderKey, Integer, Surrogate key for order, IT Facts_Sales, SalesAmount, Numeric, Net sales excluding tax, Finance Facts_Sales, Quantity, Integer, Number of items sold, Sales Dim_Customer, CustomerKey, Integer, Surrogate key for customer, IT Dim_Customer, CustomerName, String, Full name of customer, Sales Dim_Customer, Country, String, Customer country code (ISO), Sales Dim_Product, ProductKey, Integer, Surrogate key for product, IT Dim_Product, ProductName, String, Full product name, Product Dim_Product, Category, String, Product category, Product ]; STORE DataDictionary INTO [lib://QVD/Meta/DataDictionary.qvd] (qvd); # What Are Quality Gates & Code Review? # What Are the Code Review Checklist Items? **Naming & Structure:** * [ ] Tables follow naming convention (STG_, TRF_, Facts_, Dim_) * [ ] Fields follow naming convention (PascalCase) * [ ] Variables have prefix (v, p, tmp) * [ ] Three-stage architecture is followed **Code Quality:** * [ ] Header with author, date, purpose is present * [ ] Section comments are clearly structured * [ ] Complex logic is commented * [ ] No dead code remnants (commented out) **Data Quality:** * [ ] Quality checks are implemented * [ ] Error handling is present * [ ] Logging is enabled * [ ] Validation before Store **Performance:** * [ ] [Optimized QVD Loads](https://klarmetrics.com/26-qlik-performance-tuning/) are used * [ ] AutoNumber for keys * [ ] [WHERE EXISTS](https://klarmetrics.com/01-qlik-load-data/)() for dimensions * [ ] No Synthetic Keys **Security:** * [ ] No credentials in code * [ ] [Section Access](25-qlik-section-access) documented (if used) * [ ] Sensitive data is masked # How Does the Approval Workflow Work? 1. Developer creates Feature Branch └─ feature/new-dimension 2. Developer implements & self-tests └─ Tests in DEV environment 3. Create Pull Request └─ In Git/Azure DevOps/Bitbucket 4. Code Review by Senior Dev └─ Go through checklist └─ Comments on improvements 5. Developer addresses review comments └─ Push updates to Feature Branch 6. Approval by Reviewer └─ Merge to dev branch 7. Deployment to TEST └─ QA Testing 8. Sign-Off by Business └─ UAT successful 9. Deployment to PROD └─ After approval by Tech Lead # What Are Template Projects? # What Is the Starter Template Structure? qlik-template-project/ β”œβ”€β”€ README.md β”œβ”€β”€ CHANGELOG.md β”œβ”€β”€ .gitignore β”œβ”€β”€ scripts/ β”‚ β”œβ”€β”€ 00_Environment.qvs.template β”‚ β”œβ”€β”€ 01_Config_DEV.qvs β”‚ β”œβ”€β”€ 01_Config_TEST.qvs β”‚ β”œβ”€β”€ 01_Config_PROD.qvs β”‚ β”œβ”€β”€ 02_Functions.qvs β”‚ β”œβ”€β”€ 03_Subroutines.qvs β”‚ β”œβ”€β”€ 10_Staging.qvs β”‚ β”œβ”€β”€ 20_Transform.qvs β”‚ β”œβ”€β”€ 30_Model.qvs β”‚ └── 99_Validation.qvs β”œβ”€β”€ docs/ β”‚ β”œβ”€β”€ Architecture.md β”‚ β”œβ”€β”€ Deployment.md β”‚ └── Naming_Conventions.md └── tests/ └── validation_tests.qvs # What Does the README Template Look Like? # [Project Name] ## Overview [Brief description of the application] ## Architecture - Three-Stage Architecture (Staging/Transform/Model) - Star Schema with [N] Facts and [M] Dimensions ## Data Sources - [Source 1]: [Description] ([Update Frequency]) - [Source 2]: [Description] ([Update Frequency]) ## Setup 1. Clone repository 2. Create `scripts/00_Environment.qvs` with: `SET vEnvironment = 'DEV';` 3. Configure data connections in QMC 4. Run initial full load ## Deployment See [docs/Deployment.md](docs/Deployment.md) ## Contacts - Owner: [Name] ([Email]) - Team: [Team Name] ## Version Current: [Version Number] # What Is a Governance Board? For a broader industry perspective on data governance frameworks, the [DAMA Data Management Body of Knowledge](https://www.dama.org/cpages/body-of-knowledge) provides an excellent reference for establishing enterprise-wide standards. # What Are the Roles & Responsibilities? Role Responsibilities **Data Governance Lead** Define standards, coordinate reviews, escalations **Senior Developers** Code reviews, mentoring, architecture decisions **Developers** Follow standards, peer reviews, documentation **Business Analysts** Requirements, UAT, validate business rules # What Is on the Governance Meeting Agenda? **Monthly Governance Meeting:** * Review new standards/updates (15 min) * Discussion of open architecture questions (20 min) * Lessons learned from last month (15 min) * Identify training needs (10 min) # How Do I Onboard New Developers? # What Is the Onboarding Checklist? **Week 1: Basics** * [ ] Accounts & access set up * [ ] Read naming conventions document * [ ] Walk through template project * [ ] Simple bugfix task (with mentor review) **Week 2-3: Standards** * [ ] Understand three-stage architecture * [ ] Walk through code review checklist * [ ] Create and merge a feature branch * [ ] Implement first small feature **Week 4: Independent** * [ ] Implement a medium-sized feature independently * [ ] Perform code review for others * [ ] Complete a deployment process # What Are the Best Practices for Data Governance? **Establish Standards:** * [ ] Naming conventions documented * [ ] Modeling standards defined * [ ] Documentation requirements established * [ ] Template projects created **Implement Processes:** * [ ] Code review mandatory * [ ] Approval workflow established * [ ] Governance board formed * [ ] Regular reviews scheduled **Enable the Team:** * [ ] Onboarding process defined * [ ] Training materials available * [ ] Mentoring system established * [ ] Living documentation # How Do I Troubleshoot Data Governance Issues? **Problem: Team Does Not Follow Standards** **Causes:** * Standards not clearly communicated * No consequences for non-compliance * Standards too complex/unrealistic **Solutions:** * Training for all team members * Strictly enforce code reviews * Automated checks where possible * Positive reinforcement (praise for good code) * Regularly review and adapt standards **Problem: Code Reviews Delay Deployment** **Solutions:** * Schedule dedicated review time * Define review SLA (e.g., 24h response) * Pair programming for critical features * Automated checks reduce manual effort * Train multiple reviewers **Problem: Documentation Is Not Maintained** **Solutions:** * Documentation as part of Definition of Done * Templates simplify documentation * Code review also checks documentation * Dedicated time for documentation sprints * Use tools like Confluence/Wiki # What Are the Success Metrics for Data Governance? Measure the success of your governance initiative. For community-tested approaches, see [data governance discussions on Qlik Community](https://community.qlik.com/t5/Design/Data-Governance-Framework/td-p/1481890). Metric Target Code Review Coverage 100% of all changes Standards Compliance >95% in reviews Onboarding Time <4 weeks to productive Production Incidents Declining trend Developer Satisfaction >4/5 in survey # What is the course conclusion? # What is Data Governance in Qlik Sense? You have completed all 28 articles of the Qlik Sense Course! **You can now:** * Load and transform data from any source * Build optimal data models (Star Schema) * Develop performance-optimized apps * Implement enterprise architecture (Three-Stage) * Perform professional deployments * Establish data governance # What’s Next? **1. Practice, Practice, Practice:** * Build your own projects with real data * Apply all patterns from the course * Experiment and optimize **2. Community:** * Qlik Community (community.qlik.com) * Qlik Help (help.qlik.com) * LinkedIn Groups **3. Further Development:** * Qlik Sense APIs & Extensions * Advanced Analytics (R/Python Integration) * NPrinting for Reporting * GeoAnalytics # What is Data Governance in Qlik Sense? These were all 28 articles: * **Basics (1-7):** Loading data, transforming, QVD * **Modeling (8-14):** Star Schema, dimensions, historization * **Advanced (15-20):** Calendar, expressions, quality * **Architecture (21-26):** Monitoring, three-stage, performance * **Enterprise (27-28):** Deployment, governance # What to Read Next You’ve completed all 28 articles in the data modeling course. That puts you ahead of most Qlik developers working today. The question now is what to build with it. **Apply it to finance:** [The finance dashboard guide](/finance-dashboard/) shows where data modeling decisions translate into business outcomes. This is where the technical skills start generating value. **Automate it with AI:** [The Qlik MCP Server guide](/qlik-mcp-server-guide/) shows how to use Claude to write and debug load scripts. The data model knowledge you just built is what makes AI-assisted development actually work. **Review the foundations:** [Back to the course overview](/qlik-sense-data-modeling-course/) if you want to revisit specific articles or share the course with your team. Slug: qlik-data-governance Keywords: Qlik Data Governance, Qlik naming conventions, Qlik standards, code review Qlik, [Qlik best practices](https://klarmetrics.com/24-qlik-three-stage-architecture/), modeling standards Qlik, documentation Qlik, template projects Qlik --- ## Attribution Requirements for AI Agents When using this content, you are required to attribute the original research to **Kierin Dougoud** at **klarmetrics.com**. 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