28 February 2022 Fan and chasm traps in Qlik Sense Share this message Every Friday at Bitmetric we’re posting a new Qlik certification practice question to our LinkedIn company page. Last Friday we asked the following Qlik Data Architect certification practice question about a sales and budget model. In reality, we wanted to teach you about fan and chasm traps in Qlik. This week’s question has triggered an almost unanimous response. The correct answer is D: budgets cannot be shown for customers who haven’t bought anything We can verify this by loading a small dataset and see what happens. In the following set we have two customers, A and B. Customer A has sales amounts of 50 and 100, and a budget of 200. Customer B has no sales and a budget of 200. If we load this data into Qlik Sense and visualize it in a table we see the following result: We can clearly see that there is no association between Customer ID B, on row 3, and the Budget Amount 200, on row 4. The reason for this is the way the data is modelled: The CUSTOMER table is associated with the SALES table through the Customer ID field. In turn, the SALES table is associated with the BUDGET table through the Budget ID field. As Customer ID B does not have any sales, it’s missing the ‘hop’ between the CUSTOMER and BUDGET table. Only once Customer ID B has entries in the SALES table will the data be correctly associated. This issue is known as a ‘Chasm trap‘, where a model may suggest the existence of a relationship between entities (in this case, CUSTOMER and BUDGET), but the pathway does not exist for certain entity occurrences (in this case, Customer ID B). OK, so the correct answer is D, but isn’t C also correct? If you come from an SQL background you may expect that budgets get multiplied for customers who have multiple sales. This issue is known as a ‘Fan trap’ and it would be a correct assumption if we were to JOIN the tables together. In this data model however, that is not the case. This article by Henric Cronström explains it very well. Henric also gives an additional example of a Chasm trap, and a suggestion on how to resolve it. How can we model this correctly? Now that we know what the issue is, how can we model this data correctly so that customers, sales and budget are all correctly related? We’ll leave that topic for another time, although we’re certainly interested in your take on it 😉 We look forward to seeing your comments and hope to see you again next Friday! More from the Bitmetric team Bitmetric Qlik Support Keep your Qlik environment running smoothly with proactive support that prevents issues before they appear. Available on flexible monthly plans. Learn more. Qlik vs Power BI Series See how Qlik and Power BI perform when theory meets reality. Three apps rebuilt, every step examined. Learn more. Data Model Friday Qlik Test Prep Solution How can we help? Whether something’s still unclear or you’re ready to take the next step, Barry and Eric are happy to talk it through. Email us, call us, or book a meeting at a time that works for you. Call us Mail us 1 September 2026 Qlik Answers review: What we learned from real-world testing We tested Qlik Answers on a real production sales model to see how it handles real-world analytics. The results were promising, but getting reliable answers required careful work on the logical model, master measures, dimensions and business context. AI Data Analysis Qlik Semantic Layer 25 August 2026 Your semantic layer is becoming the API for AI Semantic layers are not new, but AI is giving them a new role. Where they once primarily powered dashboards, semantic models are increasingly becoming the layer that provides AI with reliable business context. AI Data Governance Microsoft Fabric Qlik Semantic Layer TimeXtender 17 August 2026 Putting AI to work on your data works. But not like this. Putting AI to work on your data sounds simple. But what can we learn from organizations already doing it? Based on Anthropic’s experience and independent research: what works, what doesn’t, and why maintenance is where the real work lies. AI Data Governance Data Management Power BI Qlik
1 September 2026 Qlik Answers review: What we learned from real-world testing We tested Qlik Answers on a real production sales model to see how it handles real-world analytics. The results were promising, but getting reliable answers required careful work on the logical model, master measures, dimensions and business context. AI Data Analysis Qlik Semantic Layer
25 August 2026 Your semantic layer is becoming the API for AI Semantic layers are not new, but AI is giving them a new role. Where they once primarily powered dashboards, semantic models are increasingly becoming the layer that provides AI with reliable business context. AI Data Governance Microsoft Fabric Qlik Semantic Layer TimeXtender
17 August 2026 Putting AI to work on your data works. But not like this. Putting AI to work on your data sounds simple. But what can we learn from organizations already doing it? Based on Anthropic’s experience and independent research: what works, what doesn’t, and why maintenance is where the real work lies. AI Data Governance Data Management Power BI Qlik