Data Products for Qlik Analytics - Part 5 - Data Quality - Semantic Types

In Part 5 of the Do More With Qlik series on Data Products for Qlik Analytics, Mike walks through how semantic types and data validation rules improve data quality, strengthen governance, and increase trust in datasets using Qlik Cloud.

You’ll learn how to:
✅ Configure semantic types
✅ Apply validation rules to datasets
✅ Understand profiling vs. preview sampling
✅ Use dictionary, pattern, and compound semantic types
✅ Improve Qlik Trust Score accuracy
✅ Detect invalid, empty, and inconsistent values automatically

This session demonstrates practical data quality workflows using Qlik Data Catalog and Data Quality features, helping data stewards quickly identify and resolve issues across datasets.

00:00 🎵 Introduction to the video series
00:10 📦 Overview of data products and datasets
00:21 🛡️ Semantic types and validation rules explained
00:37 📚 Exploring the dataset catalog interface
01:04 📏 Data sampling vs. data preview sizes
01:49 ⚙️ Qlik Cloud admin settings for dataset preview
01:56 🔍 Validation rules overview
02:09 🤖 AI-generated validation rule suggestions
02:29 🧹 Introduction to the Data Quality section
02:44 📊 Configuring Qlik Trust Score weights
03:31 🏷️ Creating a semantic type for vendors
03:58 🧠 Semantic types vs. validation rules
04:20 📝 Reviewing vendor column metadata
04:38 🛠️ Creating a new semantic type
05:03 🔗 Dictionary, pattern, and compound semantic types
05:58 🌐 Example of compound URL semantic types
06:02 📖 Building a dictionary semantic type
06:27 🎮 Vendor data quality issue demonstration
06:58 ✅ Creating the Vendor Check semantic type
07:06 🔄 Refreshing and re-profiling the dataset
07:23 📈 Viewing valid, empty, and invalid values
07:39 📉 Changing sampling size to 100 rows
08:01 🔍 Comparing validation results after re-sampling
08:31 🎬 Wrap-up and preview of the next video

Видео Data Products for Qlik Analytics - Part 5 - Data Quality - Semantic Types канала Qlik Help