29 June 2022 How to use the Lookup() function 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 the Lookup() function in Qlik: The correct answer is C: Roller, UMW, Cobretti, Grotti Most of you got this answer right and that is no surprise. Things like vertical lookup, horizontal lookup and maybe even index match must be very familiar topics for many, but why still use Excel when we have Qlik, right? The lookup() function will evaluate a field from the current or a previously loaded table and find the matching field you enter. Or to put it as the syntax: lookup(field_name, match_field_name, match_field_value [, table_name]) To evaluate the right answer let’s visualize what lookup() has exactly done in the figure below: The Lookup() function is told to find the field value of ‘Manufacturer’ in the target table and match this to the field value of ‘ManufacturerID’ in the target table and ‘ManufacturerID’ in the current table. In the last part of the syntax we determine the target table which in this example is ‘ManufacturerData’. After the match is made it is added to the current table as the column ‘Manufacturer’. Please notice that all the input which refer to the target table are given in quotes. This is mandatory since the arguments without quotes refer to the current table. In this example we also immediately notice some of the limitations of lookup(). When Lookup() is being used, it will always return the value based on the load order and if no match is found it will return NULL. So in this example we are missing ManufacturerID = 4, so it returns NULL and since ManufacturerID = 5 is duplicated, it will return Grotti, since that value is loaded first. And hopefully this is the moment many of you hear the alarm bells ring about data quality. There are quite some things to fix in this data model, we agree, and probably lookup shouldn’t even be used here. Other options like ApplyMap() or even a join could just as well be used and are maybe even better, since the performance of lookup() is not known to be the fastest. So why use lookup? The main reason of this example is not to only show the disadvantages of lookup() but also bring us to the advantages. The strength of lookup() doesn’t lie in applying the lookup to whole tables, but more to get single values from a table, for example as use in variables: LET vExcludeManufacturer =Lookup('Manufacturer', 'ManufacturerID', 1, 'ManufacturerData'); LET vExcludeManufacturerID =Lookup('ManufacturerID', 'Manufacturer', 'Roller' , 'ManufacturerData'); These will result in: vExcludeManufacturer = Roller vExcludeManufacturerID = 1 And while this is an example in a very small dataset, the use case for large datasets is quite clear. So to cut it short, while ApplyMap() or Join would be the better and most of the times faster solutions to join fields to another table, Lookup() is a great way to quickly find individual values from a table, for example to use in a variable. That’s it for this week. See you 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. Friday Qlik Test Prep Functions Performance 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