Think Like a Human, Calculate Like a Machine: Qlik Associative Engine

Author: Igor Alcantara

Close your eyes and think of “apple.” What popped into your mind? A crisp fruit? Maybe the company with the bitten logo? Maybe your grandmother’s pie, or Steve Jobs in a black turtleneck? In my case, the Beatle's recording label after they left Parlophone?

That chain reaction, one idea sparking a web of others; is how the human brain works. Neuroscientists call it associative memory: our neurons form networks where one concept lights up another. When we recall, we don’t search with exact queries; we navigate associations.

That's natural, that's human. On the other hand, queries are unnatural, a bit strange, even. Nobody wakes up and thinks:

SELECT * FROM Breakfast
WHERE Ingredient = 'Coffee';

But that’s exactly what most analytics tools force you to do. They ask you to think in rigid queries, predefined models, and filters that only show you what you already knew to ask for. That is not how we work, that is not how we have sense of the world around us. We don't make sense of things by queries or programming languages: we are animals who understand the world through association.

The Human Brain is not a SQL engine, but an Association Engine.

Let me give another example: how do you teach a child about gravity? A query minded person might explain Newton's Law of Gravity or even Einstein's General Relativity. We know it won't work. They won't make the link between that theory with consequences in the real world. However, once they start letting objects go and realizing they fall, they will understand. They will get it even if they cannot calculate it. When a child is letting objects fall, they're not doing it to make you mad, they are doing an experiment and learning by association.

Association isn’t just how humans think, but also, it’s how modern AI works too. Neural networks, the backbone of today’s AI revolution, are literally networks of artificial synapses trained to activate by association. One node lights up another, context flows, meaning emerges. Qlik’s founders didn’t know it in the 1990s, but by choosing association over rigid queries, they laid down an engine design that mirrors the way AI itself “thinks.” That’s why when AI entered the analytics world, Qlik was already ready.

A simplified illustration of a basic Artificial Neural Network

Build on a strong foundation, and the future doesn’t catch you off guard; it meets you where you already are.

How Thinking in Associations Revolutionized Analytics Forever

In Europe early 1990's something revolutionary was brewing. I am not talking about the Oasis, but something that happened far from Manchester in a small university town in southern Sweden (no, it is not Spotify either). While the tech world buzzed about the dawn of the World Wide Web, two brilliant minds in Lund with the help of a couple others were working on a different kind of breakthrough: one that would fundamentally change how humans interact with data. Björn Berg and Staffan Gestrelius had a radical idea: what if we could make computers think more like the human brain?

Their idea wasn’t about faster processors or prettier dashboards. It was about thinking differently. Humans don’t recall information through structured queries or predefined joins; we make connections, jump between ideas, and discover meaning through association. They wanted to combine data from multiple sources without the need to worry about column types and query compatibility. So, instead of teaching people to think like databases, they set out to build an architecture that could think like people. That vision became the foundation of the Qlik Associative Engine, a piece of technology so ahead of its time that decades later, as AI rose to prominence, it turned out Qlik had already been speaking the same associative language all along. Even before Qlik was called Qlik.

The Most Natural Thing in the World

Before we explore the technical marvel that is the Qlik Associative Engine, let's start with something extraordinary yet completely ordinary: your brain. Right now, as you read these words, your mind is making thousands of associations. The word "Sweden" might trigger thoughts of snow, IKEA, or perhaps ABBA. Mention "data," and your brain instantly connects it to spreadsheets, numbers, insights, or maybe even that dreaded quarterly report.

This is associative thinking: the brain's most fundamental way of processing information. When neuroscientist Donald Hebb famously declared in 1949 that "neurons that fire together, wire together," he was describing the biological foundation of all learning. Every time you encounter related pieces of information simultaneously, your brain strengthens the pathways between them, creating a web of associations that becomes your knowledge.

Artistic illustration of the brain synapses

Your brain doesn't work like a database with rigid tables and predefined relationships. It doesn't require you to write queries to remember that your childhood dog was brown, loved tennis balls, and always greeted you at the door. Instead, thinking about your dog instantly brings up all these related memories in a flash of recognition. This is the power of associative memory: the ability to understand relationships between seemingly unrelated items and recall entire networks of information through a single trigger. In simpler terms, your brain makes a selection of a dog, which retrieves associated memories, excluding unrelated information (like the meeting you were supposed to pay attention). It is the green, white, and gray in action.

The Neural Network Connection: When Biology Inspires Technology

Remarkably, the very concept that drove Qlik's revolutionary approach in the 1990s has become the cornerstone of modern artificial intelligence. Today's neural networks—the foundation of everything from ChatGPT to autonomous vehicles—operate on precisely the same principle of associative learning that Berg, Gestrelius, and their team built into the Qlik engine early on.

In artificial neural networks, synaptic plasticity allows connections between artificial neurons to strengthen or weaken based on patterns of activation, directly mimicking the biological processes Hebb described decades earlier. Modern AI systems learn by adjusting the weights of millions of artificial synapses, creating associative pathways that enable pattern recognition, language understanding, and complex reasoning. Recent research at Harvard's Kempner Institute has even discovered that certain brain synapses operate remarkably similarly to artificial neural networks, implementing "sign-switching plasticity" that allows connections to change from positive to negative: exactly how AI systems learn and adapt.

This convergence isn't coincidental. It reveals a fundamental truth about intelligence itself. Association is the universal language of learning, whether biological or artificial. When Oxford researchers developed optical processors using "Pavlovian associative learning" that outperformed traditional neural networks on certain tasks, they were validating what Qlik understood thirty years ago: that associative approaches can be more efficient and intuitive than rigid, query-based systems.

This prescient foundation is why Qlik was ready for the AI revolution from the start. While other analytics platforms struggle to integrate AI capabilities into their query-based architectures, the Qlik Associative Engine provides a natural foundation for artificial intelligence, machine learning, and automated discovery. The same associative principles that make the platform intuitive for humans make it ideal for AI agents that need to explore data rapidly, understand complex relationships, and learn from interactions.

That's what happens when you build on strong foundations: you're not just ready for the present: you're architected for the future.

The Problem with Traditional Analytics: Square Pegs, Round Holes

Here's where the story gets interesting, and frustrating. In the 1980s, when businesses desperately needed to understand their data, the available tools forced human minds into the rigid constraints of computer logic. SQL and relational databases, designed brilliantly for transactional systems, became the foundation for analytics. But asking humans to think in SQL is like asking a bird to swim underwater: technically possible, but fundamentally unnatural.

Traditional analytics tools create what we might call "tunnel vision." You write a query, get a result set, and see only what matches your specific criteria. Everything else—the context, the relationships, the broader story—simply disappears. If you want to explore a different angle, you need to write another query. Want to see how changing one filter affects everything else? Write more queries. It's a linear, predefined, limiting way to explore information that leaves insights buried in blind spots.

This approach has profound limitations that go far beyond mere inconvenience:

• Data gets left behind: SQL joins force you to choose primary and secondary data sources, inevitably losing information in the process.

• Exploration is restricted: You're limited to pre-built reports and dashboards, unable to follow your natural thought processes.

• Questions must be anticipated: Someone has to build every possible analysis in advance, making discovery nearly impossible.

• Performance suffers: Each new question requires hitting the database again, creating bottlenecks and slowdowns.

• Context is lost: Related information doesn't stay connected, forcing you to mentally piece together the bigger picture.

The Swedish Revolution: A Different Kind of Engine

Back in Lund in 1993, Berg and Gestrelius saw this fundamental mismatch between human thinking and computer tools. Gestrelius, with his background in medical physiology and technical physics, understood both how the brain processes information and how computers could be made to work differently. Berg brought deep technical expertise and vision. Together, they asked a seemingly simple question: What if we built an analytics engine that worked the way humans actually think?

Their answer became what we now know as the Qlik Associative Engine; a completely different approach to analytics that would revolutionize business intelligence. Instead of forcing users to adapt to computer logic, they designed a system that mirrors human cognitive processes.

The name "QuikView" (later QlikView) wasn't chosen randomly. QUIK stood for Quality, Understanding, Interaction, and Knowledge: four principles that remain at the heart of the platform today. The goal was to create something that would provide quality insights through understanding derived from natural interaction, ultimately building knowledge rather than just displaying data.

The Three Pillars of Associative Thinking

The Qlik Associative Engine rests on three revolutionary capabilities that distinguish it from every other analytics platform:

1. All Your Data, Unified

Traditional analytics tools struggle when you try to combine multiple data sources. SQL joins require you to define primary and secondary relationships upfront, often resulting in lost or miscounted data. You might join customer data with sales data, but what about customers who haven't made purchases yet? They disappear. What about sales that can't be matched to customers? Also gone.

Qlik takes a radically different approach. The engine achieves what's technically known as a "many-to-many full outer join". Essentially bringing together all your data from all your sources without leaving anything behind. It doesn't matter if your data is messy, incomplete, or comes from dozens of different systems. The engine loads it all, creating a comprehensive view where every piece of information maintains its relationships to everything else.

In addition to all of that, you gain an important knowledge: what is not associated. Like, what products are not sold in the selected region? It is beauty of the gray, which I will explore more soon. There is a lot of power there, almost as much as there is in the associated pieces.

2. Context-Aware, Associative Exploration

Here's where the magic really happens. When you make a selection in a Qlik application, say, choosing "Q4 2025" from a time filter; something extraordinary occurs. The engine doesn't just filter that one chart or table. Instead, it instantly performs logical inference across your entire data model, determining what's possible, what's related, and what's not. You might think "oh, my other data tool does the same". Actually, it does not, they mimic this but under the rug lies complex, repetitive and ugly filters and queries.

In Qlik, every visualization, every number, every data point in your entire application updates simultaneously to reflect this new context. But here's the crucial difference: unlike query-based tools that would simply hide unrelated data, Qlik shows you everything using its iconic color-coding system:

• Green: Selected values (what you've chosen)

• White: Possible values (what's associated with your selections)

• Gray: Excluded values (what's not related to your current context)

Image inspired by HIC illustration of the Associative Model

This green-white-gray paradigm is revolutionary because it reveals not just "what is" but also "what is not". Those gray values often contain the most valuable insights. Why aren't customers buying product B when they purchase product A? Which employees don't have certifications for specific projects? What regions aren't performing well for certain products? Query-based tools would never show you these patterns because they filter out the "unrelated" data. This is true and pure data exploration leading to information knowledge.

3. Speed of Thought Calculation

The third pillar addresses a fundamental human need: the ability to think quickly and fluidly. When you have an insight or question, you need immediate feedback to maintain your train of thought. Waiting for queries to run breaks your cognitive flow and kills discovery.

The Qlik engine achieves "speed of thought" calculation through several groundbreaking technologies:

Compressed Binary Indexing: Instead of storing data traditionally, Qlik creates an optimized in-memory structure where each unique value is stored only once, with everything else represented by binary pointers. This dramatically reduces memory usage and speeds up calculations.

Logical Inference: Before any calculations occur, the engine uses pure logical processes to determine which data is relevant to your current selections. This happens virtually instantaneously because there's no number crunching involved; just logical evaluation of relationships. Read more here.

Dynamic Calculation: Once logical inference determines the relevant data set, high-performance calculation engines compute all analytics in real-time. These calculations use advanced techniques like hypercubes and are heavily multi-threaded, utilizing all available processing power.

The Technical Marvel: How It Actually Works

Symbol Tables and Bit-Stuffed Pointers

At its core, the Qlik engine performs a remarkable transformation during data loading. Instead of storing data as traditional rows and columns, it creates two distinct structures:

• Data Tables

• Symbol Tables

Symbol tables contain one row per distinct value for each field, acting as lookup tables. The actual data tables contain the same structure you'd see in any database viewer, but instead of data, they contain only bit-stuffed pointers: indices that point back to the symbol tables.

These aren't ordinary pointers. They're ingeniously compressed, using only the exact number of bits needed to represent the distinct values in each field. If a field has four distinct values, the pointer is only two bits long. If it has 256 distinct values, it uses eight bits. This bit-stuffing creates massive compression while maintaining perfect fidelity. That also means something really important: in Qlik, columns are not typed, meaning a column by definition does not have a data type, like a regular database would. Instead, the cell is typed, using only the memory needed.

This approach delivers several advantages:

• Extreme compression: Data loads faster and uses less memory.

• Perfect relationships: Every value knows how it relates to every other value.

• Automatic joins: No need for complex SQL; relationships are built into the data structure.

• Optimized calculation: Aggregations can be performed with maximum efficiency.

The State Vector System

The engine maintains sophisticated state vectors. These are binary arrays that track the status of every data point and field value in your application. When you make selections, these vectors instantly update to reflect:

• Which field values are selected, possible, alternative, or excluded.

• Which records in data tables are included in the current context.

• Which calculations need to be updated.

• What relationships exist in the current state.

This state management system is what enables the engine to maintain global context across your entire application while supporting incredible performance even with complex selections and large datasets. It also helps to make search, in a given field and globally in the entire data model, much faster than tradicional query base. Check more here.

Hypercube Technology

For calculations and visualizations, Qlik uses hypercubes—dynamic, multi-dimensional data structures that organize information for optimal analytical computation. Don't let the name intimidate you, I am not talking about tesseracts. Hypercubes are essentially sophisticated tables that can handle complex aggregations across multiple dimensions simultaneously.

The beauty of hypercubes lies in their flexibility. They're created on-demand based on your specific analytical needs, can handle millions of data points, and are optimized for the types of calculations that drive modern analytics.

Revolutionary in Practice: Why This Matters

The End of "Ask, Wait, Answer" Cycles

Traditional BI tools create what experts call "ask, wait, answer" cycles. Business users identify a question, request analysis from technical experts, wait for reports to be built, receive answers, then identify follow-up questions and start the cycle again. This process can take days or weeks, by which time the business context may have changed entirely.

The Qlik Associative Engine eliminates this cycle completely. Every business user; regardless of technical expertise; can explore data as naturally as they explore their own thoughts. Questions lead to insights, insights lead to new questions, and the exploration continues in real-time without barriers.

Turning Every User into an Analyst

Perhaps most importantly, the associative model democratizes analytics. When exploration is as natural as clicking and seeing immediate results, when relationships are visually obvious through color-coding, when all data remains accessible without technical barriers, every employee becomes capable of analytical discovery.

This isn't just theoretical. Organizations using Qlik report that their business users make hundreds of selections and discoveries in a typical session—far more than would be possible with query-based tools. Each interaction builds understanding and leads to more informed decisions. It's not magic: it's brilliant engineering.

Performance That Scales

Independent benchmarking studies consistently show Qlik outperforming traditional analytics platforms by orders of magnitude. In 2025 BARC performance benchmarks, Qlik demonstrated:

• 3x faster response times than competing platforms.

• 30% fewer user interactions needed to complete analytical tasks.

• 2x more completed sessions at scale with no measurable performance impact.

This performance advantage becomes critical as organizations deploy AI and automated agents that can generate hundreds of analytical queries in rapid succession.

The Platform Revolution Begins

Qlik started in the 1990s but it was in the new century that its popularity really reached new levels. The success of QlikView in the early 2000s proved that the associative approach wasn't just theoretically sound, it delivered transformational business value. Organizations could finally combine all their data sources without losing information, explore freely without technical limitations, and discover insights that were invisible in traditional tools.

By 2014, Qlik introduced Qlik Sense, built on the same associative engine but designed for modern self-service analytics. The QIX (Qlik Interactive eXperience) engine—the technical name for their associative technology—became the foundation for an entire platform encompassing data integration, artificial intelligence, and cloud-scale analytics.

Since Qlik's breakthrough, numerous analytics vendors have attempted to copy aspects of the associative experience. They've added color-coding to their interfaces, improved interactivity in their dashboards, and marketed themselves as "exploratory" platforms. But surface-level imitation can't replicate fundamental architecture.

The associative difference isn't just about user interface—it's about the entire engine architecture. Query-based tools will always be limited by their foundational approach:

• They must define data relationships in advance.

• They filter out unrelated information.

• They process queries sequentially rather than maintaining global context.

• They struggle with complex joins and multiple data sources.

• They can't achieve the same performance characteristics with large datasets.

The Qlik Associative Engine, in contrast, was designed from the ground up around human cognitive principles. Every component—from bit-stuffed pointers to state vectors to logical inference—works together to support associative thinking.

Today, what started as an insight about human cognition in a Swedish university town powers analytics for tens of thousands of organizations worldwide. The associative engine processes billions of data points, supports millions of users, and enables discoveries that simply wouldn't be possible with query-based approaches.

The AI Era: Associations Become Even More Critical

As artificial intelligence transforms analytics, the associative approach becomes more valuable, not less. AI agents need to explore data rapidly, test hypotheses, and understand relationships across complex datasets. The sequential, query-based approach that frustrates human users becomes completely impractical for AI systems that might generate thousands of analytical requests per minute.

Qlik's associative engine excels in AI scenarios because:

• Context retention allows AI agents to build complex reasoning chains.

• High-speed calculation supports rapid hypothesis testing.

• Complete data access ensures AI models have full information.

• Relationship visibility helps AI understand data connections.

• Governed calculations provide trustworthy results for automated decisions.

Context Awareness: Chain-of-Thought Analytics

The Qlik Engine isn’t just fast. It remembers.

Ask one question, then another, then another, and Qlik retains the context, refining and recontextualizing after every click. This “chain-of-thought” capability is exactly what humans do in conversation: you don’t restate all prior facts every time you ask a new question.

This is also what makes Qlik uniquely suited for AI:

• Agentic AI: Qlik supports fast sequential queries where each builds on the last.

• Predictive AI: real-time “what-if” analysis at scale.

• Monitoring AI: outlier and drift detection across massive dimensions.

Most “AI in BI” tools still translate natural language to SQL under the hood; which means they’re stuck in the rigid world of queries. Qlik’s engine, by contrast, enables both humans and machines to think contextually, associatively, incrementally.

The Enduring Revolution

What began as an observation about human cognition—that we understand the world through associations, not queries—has become one of the most significant innovations in business intelligence. The Qlik Associative Engine doesn't just process data differently; it honors the fundamental way human minds work.

In boardrooms and factory floors, in research labs, hospitals and retail stores, millions of people now explore information as naturally as they explore their own thoughts. They discover relationships that were hidden in traditional tools, answer questions that couldn't be asked before, and make decisions based on complete pictures rather than partial views.

The green-white-gray paradigm that seemed so simple in early QlikView demonstrations actually represents a profound shift: from forcing humans to think like computers to enabling computers to support human thinking. In those colors lies the difference between analysis and discovery, between reporting what happened and understanding why it happened.

Today, as data grows more complex and decisions become more critical, the associative principle becomes more relevant than ever. The engine that started in Lund continues to evolve, but its core insight remains unchanged: the most powerful analytics happen when technology amplifies human intuition rather than replacing it.

That's the true legacy of the Qlik Associative Engine—not just a faster way to query data, but a fundamentally better way to think with information. Any information, sales, medical, apples or gravity data. In our increasingly data-driven world, that revolution has only just begun.