Systems | Development | Analytics | API | Testing

In this video, Mike Tarallo shows you a simple Qlik Automate workflow with defined inputs, then uses the Automate Agent in Qlik Answers to pass those inputs directly into the automation. This demonstrates how users can move beyond simply asking questions and begin taking action based on their data—all from a conversational experience. You’ll see how Qlik Answers and Qlik Automate work together to turn natural-language requests into real, automated workflows with minimal setup.

In Part 3, we keep things simple with a “Hello World” example that shows how to create a basic Qlik Automate workflow and trigger it directly from Qlik Answers. You’ll see how an insight can quickly become an action—all within Qlik, without a complicated setup. Check out Parts 1 and 2 for the full series.

With the Data Quality Agent in Qlik, AI can guide data engineers through a full dataset quality assessment using natural language prompts. The agent can identify trusted catalog definitions, generate intelligent data quality validation rules, and provide transparent reasoning behind every recommendation.

With Declarative Pipelines in Qlik, teams can use tools like Claude Code, GitHub Copilot, and integration with VS Code, to build and update Qlik Talend Cloud data integration pipelines using natural language prompts.

With the Data Product Agent in Qlik, AI can automatically generate curated Data Products using trusted catalog definitions, datasets and metadata. It defines the specification, contract, and reasoning behind the data, while also providing action confirmation before anything is changed.

Join Ouadie Limouni and Mike Tarallo on this week's Qlik Live Stream Friday for a look at Choose Your Champion 2026, an interactive World Cup prediction experience powered by. Ouadie will walk through the application, highlighting how machine learning, interactive analytics, and conversational were combined to create a unique fan experience for World Cup 2026 predictions.

Welcome to Part 7 of the “Data Products for Qlik Analytics” series! In this episode, we take Data Quality to the next level by building advanced data validation rules using IF / THEN / ELSE logic within Qlik Analytics. You’ll learn how to create conditional validation logic that evaluates relationships across multiple columns, enabling smarter and more dynamic data quality checks for your data products.

In Part 6 of the Data Products for Qlik Analytics series, we explore how to improve trust and reliability in your data products using validation rules within datasets. Learn how data validation helps enforce consistent standards, identify data issues early, and ensure higher-quality analytics across your organization.

In this video, we dive into how Click Data Products combine Data Quality, Semantic Types, and business context to create trusted, scalable, and reusable data assets. We explore how semantic types in Qlik help classify and validate data using meaningful business definitions — improving consistency, discoverability, and confidence in analytics and AI-driven insights.

In part 4 of this series, Mike Tarallo form Qlik, walks you through the core components of Qlik Datasets, giving you a clear understanding of how to navigate and interpret key features within the platform. We explore the Profile tab, Data Lineage, Impact Analysis, and Data Preview to see how each helps you better understand your data and its flow across systems.