¶ QlikView
¶ Artificial Intelligence
There’s a quiet moment that every analyst, data scientist, or decision-maker experiences at some point—when a complex dataset suddenly reveals a pattern that was invisible just seconds earlier. It might appear as a rising curve, a sudden drop, an unusual cluster, or a correlation you didn’t expect. And in that moment, the relationship between data and insight becomes unmistakably clear. QlikView is a tool that was built for those moments.
Long before the current wave of AI dashboards, automated insights, cloud warehouses, and real-time business intelligence platforms, QlikView was already showing the world what fast, intuitive, interactive data exploration could feel like. It was one of the first tools that treated data not as something static and cautious but as something alive—ready to be sliced, filtered, rearranged, and reimagined based on how curious the human mind is willing to be. QlikView didn’t just let you analyze data; it let you think with data.
As artificial intelligence has grown into one of the most transformative forces of our time, tools like QlikView play a deeper role than people often realize. AI systems thrive on data—cleaned, structured, interpreted, and understood data. But none of this works unless people can explore and comprehend the information themselves. QlikView sits right at that intersection: the point where human intelligence meets artificial intelligence. It’s where raw numbers turn into meaningful stories, where dashboards become decision frameworks, and where organizations learn not just what is happening, but why it is happening.
This course begins with an appreciation of QlikView as more than a business intelligence tool. It is an approach to thinking. It encourages curiosity, fast exploration, and unexpected discovery. In a world overflowing with information, QlikView offers clarity. And clarity is the foundation on which all successful AI initiatives are built.
One of the most unique aspects of QlikView is the associative model that powers it. Most analytics tools work with fixed hierarchies or predefined drill-down paths. QlikView took a different path entirely. It allowed users to explore data freely, without restrictions. Click on a number, a product, a date, a region—immediately everything else in the dashboard rearranges itself to show what remains associated and what doesn’t. This freedom mirrors the way the human brain thinks: not in rigid steps, but in leaps of association.
This associative experience matters deeply in artificial intelligence workflows. Machine learning models often uncover relationships that humans don’t expect, but human insight still plays a central role in validating, interpreting, and acting on these findings. The best data scientists are not the ones who memorize algorithms—they are the ones who understand data intuitively. QlikView builds this intuition. It helps you see patterns, gaps, and outliers faster than traditional analytical systems allow.
The strength of QlikView lies in how it blends structure with creativity. On one hand, it organizes data into fast, memory-optimized structures that respond instantly to user actions. On the other hand, it invites you to explore without constraints. You can start with a simple metric and end up discovering a dozen unexpected relationships—all because the interface encourages exploration rather than restricting it.
This kind of exploration is essential for AI. Before you train a model, you must understand the data. Before you trust a prediction, you must compare it to observable patterns. QlikView becomes the bridge between raw datasets and intelligent systems. It doesn’t replace AI; it enhances your ability to reason with it.
What makes QlikView especially relevant today is how differently it approaches data compared to many modern tools. Instead of pushing users toward dashboards that look polished but behave rigidly, QlikView encourages engagement. It believes discovery is not something that happens at the end of the analysis—it happens throughout it. And AI thrives in environments where people understand their data deeply before building models on top of it.
People often underestimate how much impact visualization has on artificial intelligence. A model might generate predictions, but a human must interpret them. A dataset might contain millions of rows, but only a well-crafted visualization reveals which variables actually matter. QlikView excels in this area. It transforms the complexity of data relationships into visual clarity. It allows users to spot trends, inconsistencies, behaviors, and opportunities that algorithms alone cannot explain.
As you progress through this course, you’ll notice that QlikView isn’t simply about creating charts or dashboards. It’s about creating understanding. It teaches you how to design visual environments where users can ask questions naturally—questions they didn’t even realize they needed to ask. And that is where true intelligence happens: when curiosity intersects with capability.
Another reason QlikView has endured is because it understands the rhythm of business. Decision-makers don’t want static reports. They want tools that let them interact with their questions in real time, in a natural, conversational way. They want to see what happens if they filter one category, compare another, highlight an exception, isolate an outlier. QlikView was built around this dynamic thinking. It is not a reporting tool—it is a reasoning tool.
In artificial intelligence projects, this dynamic reasoning is crucial. Data preparation, feature engineering, anomaly detection, and exploratory analysis form the foundation of all machine learning pipelines. QlikView helps practitioners navigate these stages with speed and confidence. It helps them understand not only the data they have but the data they are missing. It brings context into view—context that AI models require to be effective.
As you dive into this course, you will also learn to appreciate how QlikView handles complexity behind the scenes. Its in-memory architecture allows for instant responsiveness. Its scripting language enables sophisticated data transformations. Its associative engine maintains relationships across datasets that traditional SQL-based systems fail to preserve naturally. This architecture is powerful enough for enterprise-grade intelligence but still accessible to individuals who want to experiment.
QlikView has another remarkable quality: it adapts to people, not the other way around. Some users explore visually, clicking through dashboards. Others want to build custom expressions. Others want to clean data. Others want to integrate machine learning outputs directly into dashboards. QlikView accommodates all these styles. It becomes what the user needs it to be—an explorer, a designer, a transformer, a storyteller.
And storytelling is where QlikView truly shines. Data is just information until someone interprets it. QlikView enables storytelling through guided exploration. A dashboard might begin with high-level metrics, then gradually invite users into deeper layers of understanding. Each visual, each filter, each selection becomes a part of the narrative. This narrative-driven exploration complements artificial intelligence wonderfully because AI itself is not the final answer—it is a tool for shaping better stories about what the data means.
As AI continues expanding into almost every industry, QlikView becomes the lens through which organizations see their data more clearly. It helps them identify opportunities that AI models can act on. It reveals problems AI systems can help fix. It provides human-level insight that guides machine-level computation. This harmony between visualization and intelligence is what makes QlikView an essential part of modern AI education.
By the end of this course, QlikView will feel far more than a BI platform. It will feel like an environment where curiosity grows naturally, where insights appear effortlessly, and where AI becomes easier to reason about. You will learn how to build dashboards that don’t just display numbers but reveal meaning. You will learn how to design data models that support fast thinking and deep exploration. And you will understand how QlikView fits into AI pipelines—not as an accessory, but as a powerful companion to intelligent systems.
This introduction marks the beginning of a journey into a tool that blends human insight with analytical power. Across the next hundred articles, you’ll explore the fundamentals, techniques, best practices, integrations, and philosophies that make QlikView a timeless instrument for data-driven intelligence. Along the way, you’ll develop a deeper understanding of how visualization strengthens AI, how exploration enhances modeling, and how good tools encourage good thinking.
If you’d like, I can also create:
• Article 81 of this QlikView course
• A complete 100-article outline
• A more technical or simplified version
¶ QlikView
¶ Beginner Level
1. Introduction to QlikView and Its Role in AI
2. Setting Up QlikView for AI Projects
3. Understanding QlikView’s User Interface for AI Applications
4. Basic Data Loading and Importing in QlikView
5. Connecting QlikView to Various Data Sources for AI
6. Exploring the QlikView Data Model
7. Basic Data Transformation and Preparation for AI in QlikView
8. Creating Basic Data Visualizations in QlikView
9. Building Simple Dashboards for AI Insights in QlikView
10. Understanding QlikView’s Scripting Language
11. Basic Functions and Expressions in QlikView
12. Using QlikView for Data Aggregation and Summarization
13. Exploring QlikView’s Set Analysis for AI Data Manipulation
14. Using QlikView’s Associative Data Model for AI Insights
15. Introduction to QlikView’s Charts and Visualizations for AI
16. Building Simple AI Reports in QlikView
17. Creating Interactive Dashboards in QlikView
18. Understanding Data Filtering and Selection in QlikView
19. Integrating QlikView with External Machine Learning Tools
20. Exploring QlikView’s AI-Powered Data Exploration
21. Introduction to QlikView’s Predictive Analytics Features
22. Building Forecasting Models in QlikView for AI
23. Using QlikView to Visualize Regression Analysis Results
24. Introduction to QlikView Extensions for AI Applications
25. Using QlikView for Basic Classification Tasks
26. Integrating QlikView with Python for AI Analysis
27. Introduction to QlikView’s Data Model Viewer for AI Projects
28. Using QlikView to Prepare Data for AI and Machine Learning
29. Exploring the Use of QlikView for Time Series Analysis
30. Creating AI Dashboards with Predictive Insights in QlikView
31. Using QlikView for Data Normalization in AI Projects
32. Understanding QlikView’s Data Load Editor for AI Data Transformation
33. Basic Machine Learning Integration with QlikView
34. Using QlikView’s Forecasting Functions for AI Models
35. Building Simple Decision Trees in QlikView for AI Analysis
36. Exploring QlikView’s Integration with R for AI Visualization
37. Introduction to QlikView’s Cognitive Services for AI
38. Using QlikView for Feature Engineering in AI
39. Creating AI Visuals with QlikView’s Custom Objects
40. Working with Statistical Functions in QlikView for AI Analysis
41. Implementing AI Predictive Models with QlikView
42. Using QlikView’s Data Linking for AI Insights
43. Integrating QlikView with Cloud Services for AI Analysis
44. Understanding QlikView’s GeoAnalytics for AI Mapping
45. Working with Unstructured Data in QlikView for AI Insights
46. Using QlikView’s Native AI Features for Trend Analysis
47. Building Basic AI Dashboards Using QlikView Extensions
48. Utilizing QlikView’s Variable and Field Functions for AI
49. Creating Interactive AI Reports with QlikView’s Expressions
50. QlikView’s Role in AI-Powered Business Intelligence
¶ Intermediate Level
51. Advanced Data Loading and Transformation in QlikView for AI
52. Implementing Machine Learning Models in QlikView
53. Using QlikView’s Data Functions for AI-Based Analysis
54. Integrating QlikView with Python for Complex AI Models
55. Building AI Models for Forecasting and Time Series in QlikView
56. Exploring QlikView’s Integration with TensorFlow for AI
57. Using QlikView for Clustering and Segmentation in AI
58. Implementing and Visualizing Decision Trees in QlikView
59. Advanced Predictive Analytics with QlikView
60. Creating Advanced Regression Models in QlikView for AI
61. Using QlikView’s Associative Model for Predictive Analytics
62. Building Sentiment Analysis Dashboards with QlikView
63. Using QlikView for Anomaly Detection in AI Models
64. Implementing AI Classification Models in QlikView
65. Using QlikView to Visualize Clustering Results
66. Advanced Time Series Forecasting with QlikView
67. Leveraging QlikView’s Extension Objects for Advanced AI Analytics
68. Integrating Deep Learning Models with QlikView
69. Advanced Feature Engineering with QlikView for AI
70. Building AI Dashboards for Business Decision Support in QlikView
71. Utilizing QlikView for Outlier Detection in AI Models
72. Exploring Neural Networks and AI Models in QlikView
73. Building Custom AI Models in QlikView with External Tools
74. Understanding AI Model Deployment in QlikView
75. Using QlikView for Multi-Class Classification Models
76. Implementing Clustering Techniques for AI in QlikView
77. Using QlikView for Sentiment Analysis and NLP Insights
78. Automating AI Model Monitoring and Reporting with QlikView
79. Integrating AI-Based Predictive Maintenance Models in QlikView
80. Using QlikView for Ensemble Learning Models
81. Exploring QlikView’s Integration with Hadoop for Big Data AI Analysis
82. Advanced Customization of AI Dashboards in QlikView
83. Applying Feature Selection Techniques in QlikView for AI
84. Building and Visualizing Neural Networks in QlikView
85. Using QlikView’s Statistical Functions for Advanced AI Insights
86. Understanding Model Accuracy and Evaluation in QlikView
87. Integrating QlikView with Azure AI for Advanced Analytics
88. Leveraging QlikView’s GeoAnalytics for AI-Driven Location Insights
89. Optimizing QlikView for Large-Scale AI Projects
90. Creating Custom AI Visuals in QlikView with R and Python
91. Implementing Reinforcement Learning Algorithms with QlikView
92. Using QlikView’s Data Modeling Capabilities for AI Workflow
93. Building AI-Based Recommender Systems in QlikView
94. Using QlikView for Advanced Text Mining and NLP Tasks
95. Understanding Hyperparameter Tuning for AI in QlikView
96. Building Custom Predictive Models for AI in QlikView
97. Integrating Real-Time Data Streams into QlikView for AI Insights
98. Using QlikView for AI-Based Fraud Detection Models
99. Advanced Data Visualization Techniques for AI in QlikView
100. Deploying AI Solutions in QlikView for Enterprise Applications