Recent Blog Posts

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  Support Updates

  Qlik Talend Administration Center - Security Patches Available

  The following two Qlik Talend Administration Center security issues have been identified and subsequently resolved. Patches are already available.   U... Show More

  The following two Qlik Talend Administration Center security issues have been identified and subsequently resolved. Patches are already available.

   

  URL access control vulnerability (CVE-2026-9057)

  A broken access control issue has been identified in Qlik Talend Administration Center, which allows a user with View permission to modify the Qlik Talend Studio update URL.

  Affected Software 

  • All versions of Qlik Talend Administration Center before Patch_20251121_QTAC-1471_R2025-11_v1-8.0.1.

  See Security fix for Qlik Talend Administration Center URL access control vulnerability (CVE-2026-9057) for details. 

   

  Cross-site scripting vulnerability (CVE-2026-9056)

  A stored cross-site scripting security issue in the Qlik Talend Administration Center has been identified.

  Affected Software

  • All versions of Qlik Talend Administration Center before Patch_20260123_QTAC-1883 (cumulative patch)_R2026-01_v1-8.0.1 are affected.

  See Security fix for Qlik Talend Administration Center cross-site scripting vulnerability (CVE-2026-9056) for details.

   

  Recommendation

  Upgrade at the earliest. The following table lists the patch versions addressing the vulnerabilities.

  Always update to the latest version. Before you upgrade, check if a more recent release is available.

  Product	Patch	Release Date
  Qlik Talend Administration Center  URL access control vulnerability	QTAC-1471	November 21, 2025
  Qlik Talend Administration Center  cross-site scripting vulnerability	QTAC-1883	January 23, 2026

   

  Thank you for choosing Qlik,
  Qlik Support

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  By Sonja_Bauernfeind 2026-05-12 / 08:13 AM

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  Support Updates

  Qlik Automate: Automation ownership changes for Analytics Admins May 2026

  Qlik introduced a change in how automation permissions are handled for the Analytics Admin role. When was the change introduced? The change is already... Show More

  Qlik introduced a change in how automation permissions are handled for the Analytics Admin role.

  When was the change introduced?

  The change is already live as of the 11th of May, 2026.

   

  What does that mean for me?

  Analytics Admins can now claim ownership of another user's automation. After claiming ownership, they can make necessary changes to it and enable the automation. However, they can no longer transfer ownership to another user.

   

  How do I claim ownership of an automation?

  As an Analytics admin, to claim ownership of an automation:

  1. Navigate to the Automations section in the Administration Console
  2. Locate the automation you want to claim ownership of, and click the Actions menu (...)
  3. Choose Claim ownership
     
     

   

  This behavior change only applies to Analytics Admins. Tenant admins can still transfer ownership to any user with the appropriate access rights in the tenant.

   

  If you have any questions, we're happy to assist. Reply to this blog post or take your queries to our Support Chat.

   

  Thank you for choosing Qlik,
  Qlik Support

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  By Sonja_Bauernfeind 2026-05-11 / 07:57 AM

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  Explore Qlik Gallery

  Predicting London ULEZ effects on emissions.

  Predicting London ULEZ effects on emissions.C40 CitiesUse historic emissions data and the expansion of Londo's Ultra Low Emissions Zone program to pre... Show More

  

  

  Predicting London ULEZ effects on emissions.

  C40 Cities

  

  Use historic emissions data and the expansion of Londo's Ultra Low Emissions Zone program to predict its effects on air quality in each of London's boroughs.

  

  

  Discoveries

  We have been able to corroborate the model's accuracy using real data from the first ULEZ expansion, so we are confident it will predict the second expansion effects effectively.

  

  Impact

  This app and the ML experiment behind it has served as an internal demonstration of Qlik's machine learning capabilities, making its adoption easier.

  

  Audience

  Our internal data science team. It serves as a proof of concept for Qlik Predict.

  

  Data and advanced analytics

  An effective use case of machine learning in its prediction mode.

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  By amonjaras_c40 2026-05-10 / 04:38 PM

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  Product Innovation

  From Raw Data to AI-Ready: Accelerating GenAI and Agentic Initiatives with Qlik ...

  Most enterprise AI projects don’t fail because the model is wrong. They fail because the data isn’t ready. Data engineering leaders are now being aske... Show More

  Most enterprise AI projects don’t fail because the model is wrong. They fail because the data isn’t ready. Data engineering leaders are now being asked to support a new wave of generative and agentic workloads that demand fresher data, broader source coverage, tighter governance, and richer context than traditional BI ever required — and to deliver it without growing the team.

  Qlik Talend Cloud Data Integration was built to close that gap. It provides a single, governed pipeline from operational sources to an open lakehouse — and on to the vector indexes, feature stores, and APIs that your AI systems actually consume. Combined with Qlik Open Lakehouse on Apache Iceberg, it turns your AI inputs into reusable AI data products: named, versioned, governed assets that any RAG application or agent can consume off the shelf.

  This post walks through the reference architecture, the pipeline that produces those data products, and a worked example that takes raw CRM and product data all the way to a working RAG copilot and an agentic workflow — both running off the same Iceberg foundation.

  Why data is the bottleneck for enterprise AI

  GenAI and agentic systems are not fundamentally different consumers of data, but they are far more demanding ones. A model is only as accurate, current, and trustworthy as the context it retrieves at inference time. For data engineering leaders, that translates into six hard requirements:

  • Freshness — Embeddings and agent context become stale quickly. Real-time CDC matters more than nightly batch.
  • Breadth — Useful AI requires content from CRMs, ticketing systems, document stores, ERPs, and operational databases — often dozens of sources per use case.
  • Quality — Bad data doesn’t just produce bad answers. It produces confidently wrong answers, which are worse.
  • Governance — PII, masking rules, lineage, and access controls must travel with the data into vector stores and tool calls, not stop at the warehouse boundary.
  • Openness — Locking AI-ready data into a proprietary store creates rework every time the model, framework, or query engine changes.
  • Reuse — Hand-rolling a new pipeline for every AI use case is how programs stall. The same curated data should serve a RAG copilot today and an agent tomorrow.

  Meeting all six at once with one-off pipelines is what kills enterprise AI velocity. The path forward is consolidation: one governed integration platform feeding one open lakehouse, with the Gold zone publishing reusable AI data products that any model, agent, or analyst can consume. Build once, govern once, serve many.

  Qlik Talend Cloud + Iceberg: a reference architecture

  The architecture has four layers: sources, integration, an open Iceberg lakehouse with medallion zones, and an AI serving layer. Qlik Talend Cloud handles change data capture, transformation, quality, and catalog metadata across the entire flow. The Gold zone is where curated outputs are published as named AI data products.

   

  Two design choices make this architecture work for AI specifically. First, the integration layer is real-time by default — log-based CDC keeps Bronze and Silver tables current without batch windows. Second, Gold is treated as a publishing surface, not a staging area. Each Gold data product is named, versioned, governed, and discoverable in the catalog. RAG and agents become two interfaces over the same products: built once, governed once, consumed many times.

   

   

  

   

  Figure 1. Reference architecture: Qlik Talend Cloud + open Iceberg lakehouse, serving RAG, agentic, and analytics workloads from the same governed Gold layer.

  The pipeline: from raw data to AI use

  The pipeline that operates on the architecture above runs in six stages — automated end-to-end, with quality and lineage enforced at every step. Each stage produces a more refined and trusted asset. Bronze preserves raw, append-only CDC for replay and audit. Silver applies data quality rules, deduplication, masking, and Type-2 history. Gold publishes AI data products: a document product (chunk-friendly text + metadata) for RAG, and a state product (curated entity, feature, and policy data) for agents. Both are versioned and registered, so consumers — vector indexers, semantic APIs, BI engines — read the same governed truth.

  

   

  Figure 2. The six-stage pipeline. Because every stage writes to Iceberg, downstream consumers — vector indexers, semantic APIs, BI engines — read the same governed truth.

  Worked example: from CRM tickets to a customer-support agent

  Picture a data engineering team chartered with delivering an AI-powered customer-support assistant. The use case has both a RAG side (deflecting common questions with vetted answers) and an agentic side (the assistant can look up customer status, open tickets, and trigger actions). The raw inputs are typical:

  • Salesforce — accounts, contacts, cases, case comments.
  • ServiceNow — incident records and resolution notes.
  • Confluence and SharePoint — a few thousand product KB articles.
  • Postgres operational DB — subscription and entitlement state.

  The pipeline at work

  1. Ingest. Qlik Talend Cloud uses log-based CDC to stream changes from Salesforce, ServiceNow, and Postgres in real time. KB articles are pulled on a connector schedule with content-hash detection so only changed docs flow through.
  2. Land in Bronze. Every change is written to append-only Iceberg tables in cloud object storage, partitioned by source and ingestion date. The raw audit trail is preserved for replay.
  3. Standardize in Silver. Push-down ELT cleanses text, masks PII (customer email, phone), conforms keys and status codes, and applies Type-2 history to entity tables (customer, case, entitlement, interaction). Trust scores are written alongside each table.
  4. Publish two Gold data products. rag_documents — KB articles + anonymized resolution notes from closed tickets, pre-joined and metadata-tagged for retrieval. agent_state — a fused customer_360 view, current entitlement state, and a small policy_rules table that defines what actions agents are allowed to take. Both are versioned, lineage-tracked, and registered in the catalog.
  5. Vectorize. rag_documents is chunked and embedded into a managed vector index with metadata filters for product, language, and access tier. The job is incremental — only new and changed rows of the data product trigger re-embedding.
  6. Serve and audit. agent_state is exposed via a thin Semantic API and parameterized SQL endpoints, ready for agent tool calls. Every agent action is written back to an audit_log Iceberg table — inputs, decision, tool call, outcome — so the same lakehouse that grounds the agent also makes its behavior explainable.

  Powering RAG

  When a customer asks “Why was my last bill higher than usual?”, the copilot retrieves the top-k chunks from the rag_documents data product, filtered by the customer’s product entitlement — with a structured lookup against agent_state for the customer’s current invoice context. Because the underlying data products are continuously refreshed by Qlik Talend Cloud, the copilot cites guidance that reflects the current pricing schedule, not last month’s. Every retrieved chunk carries its lineage, so answers can be traced back to a specific source row in Salesforce or a specific KB article version.

  Powering agentic workflows

  For agentic flows, the assistant plans and executes multi-step tasks against the same agent_state product: confirm identity, check entitlement, open a case in Salesforce via a write-back tool, and escalate to a human agent if confidence drops below a threshold defined in policy_rules. Every step is recorded in the audit_log table for explainability. The agent’s tools are backed by exactly the same data products the RAG side uses — which means a behavior change in the data, like a new product or pricing tier, propagates to both surfaces immediately, with no parallel pipelines and no copy-paste schemas. RAG and agents really are two interfaces over one set of products.

  From pipeline to production: your next move

  The fastest enterprise AI programs aren’t the ones with the cleverest prompts or the largest models. They’re the ones treating AI data products as the unit of delivery. Qlik Talend Cloud and Qlik Open Lakehouse give your team three things at once: real-time movement of broad source data, governed transformation into named and versioned data products, and an open Iceberg foundation that any model, framework, or agent can plug into. Build once, govern once, serve both RAG and agents from the same products.

  A 10–15 day starting sprint for data engineering leaders:

  • Pick one use case with two surfaces. Choose a domain where you need both a RAG copilot and a constrained agent (one or two write actions). Working backward from both surfaces forces the right data product shape.
  • Stand up CDC into Iceberg. Wire two or three high-value sources into Bronze via Qlik Talend Cloud, build the Silver entity layer, and publish two Gold data products: one for retrieval, one for action.
  • Measure freshness, trust, and reuse. Track event-to-context latency (freshness), quality-rule pass rate (trust), and how many AI surfaces consume the same Gold products (reuse). These three numbers tell you whether the pattern is ready to scale to the next domain — as configuration, not reinvention.

  Talk to your Qlik team. Ask about the AI-ready data solution templates — pre-built pipeline patterns for the most common GenAI and agentic use cases, including the customer-service pattern walked through above.

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  By QTC_Manuel 2026-05-08 / 05:37 PM

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  Product Innovation

  Unlocking Qlik Open Lakehouse Access from Talend Studio

  Native Qlik Open Lakehouse interoperability for Talend Studio With the March release, Talend Studio introduces native support for querying Qlik Open L... Show More

  
  Native Qlik Open Lakehouse interoperability for Talend Studio

  With the March release, Talend Studio introduces native support for querying Qlik Open Lakehouse datasets through Amazon Athena — available in both Standard Data Integration jobs and Spark-based Big Data workflows.

  This means developers can now connect to Qlik Open Lakehouse data, execute SQL queries, and integrate results downstream the Talend job without manual JDBC configuration or custom setup.

  Connecting Talend Studio to Qlik Open Lakehouse

  Talend Studio now connects natively to Qlik Open Lakehouse through Amazon Athena — a SQL query engine that runs directly on top of cloud storage, enabling access to Iceberg-managed data without data movement or duplication. Developers can:

  • Access Qlik Open Lakehouse data with an out-of-the-box configuration,  no manual JDBC setup required
  • Execute SQL queries directly within Talend jobs (Standard and Big Data)
  • Integrate Qlik Open Lakehouse data into existing Talend jobs without disrupting current workflows

  Reliable by Design

  Connecting to Qlik Open Lakehouse from Talend Studio is straightforward by design. The integration ships with dedicated Athena configuration and input components, eliminating manual setup. Runtime validation, improved error handling, and secure credential management ensure the connection remains stable and trustworthy in production environments.

  How Data is Organized in Qlik Open Lakehouse

  In Qlik Open Lakehouse, data is ingested incrementally and accumulated in Apache Iceberg tables. A logical abstraction layer — implemented as Trino views — resolves those changes into a consolidated latest-state representation, which different engines can query without handling change consolidation logic directly.

  This model supports two complementary data patterns:

  • Current-state access (SCD Type 1): query the latest-state view through Athena for operational and integration use cases
  • Full history access (SCD Type 2): query the underlying Iceberg tables directly for time-aware and audit analysis

  Both patterns are available across Standard Data Integration and Big Data jobs in Talend Studio, enabling teams to work with Qlik Open Lakehouse data in the way that best suits their use case.

   

  

  Looking Ahead

  This integration enables Talend Studio users to access Qlik Open Lakehouse data without changing their existing workflows — while aligning with modern, open-format architectures that support multiple query engines.

  Athena is the first fully supported access path in this model, with a roadmap to extend support to additional engines over time. For organizations moving away from traditional data warehouses or adopting multi-engine strategies, this represents a concrete step toward a more flexible data architecture.

   

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  By RMartins 2026-05-07 / 04:12 PM

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  Support Updates

  Watch! Q&A with Qlik: Qlik Cloud Migration

  Don't miss our latest Q&A with Qlik! Pull up a chair and chat with our panel of experts to help you get the most out of your Qlik experience.   WATCH ... Show More

  Don't miss our latest Q&A with Qlik! Pull up a chair and chat with our panel of experts to help you get the most out of your Qlik experience.

   

  WATCH HERE

   

   

  

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  By Troy_Raney 2026-05-08 / 03:23 AM

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  Japan

  SAP S/4HANA 移行の「壁」を突破する！テストデータ管理を劇的に変える！

  本コラムでは、先日開催された TechEd 2025 にて、Qlik の Miguel Antunes が提唱した「スマートなテストと迅速な移行」をテーマに、次世代の SAP データ管理手法を紐解きます。   1. 移行プロジェクトを阻む「テストデータの三重苦」 S/4HANA への移行は、単なるソ... Show More

  本コラムでは、先日開催された TechEd 2025 にて、Qlik の Miguel Antunes が提唱した「スマートなテストと迅速な移行」をテーマに、次世代の SAP データ管理手法を紐解きます。

   

  1. 移行プロジェクトを阻む「テストデータの三重苦」

  S/4HANA への移行は、単なるソフトウェアのアップグレードではなく、データモデルそのものの刷新を伴います。そのため、本番に近い環境でのテストが不可欠ですが、現場では以下の課題が頻出しています。

   

  • 時間とコストの浪費： フルコピー（システムの丸ごとコピー）を行うと、数日から数週間かかり、その間本番環境がロックされることもあります。
  • インフラの肥大化： 不要なテラバイト単位のデータまでコピーすることで、非本番環境（検証・開発環境）のストレージコストが跳ね上がります。
  • コンプライアンスのリスク： 個人情報（PII）が含まれるデータをそのままテストに使うことは、GDPR などの観点からも許されません。

   

  2. Qlik Gold Client が提供する「アジャイル」なデータ管理

  これらの課題を解決するのが、SAP 認定のテストデータ管理プラットフォーム「Qlik Gold Client」です。従来の「全部コピー」という力技ではなく、「必要なデータだけを、スライスして、同期する」というアプローチに切り替えます。

   

  主な特徴：

   

  • データサブセットの抽出： 関連するデータだけを選択してコピー。例えば、5TB の本番環境から必要な 950GB だけを切り出し、数時間でテスト環境を構築可能です。
  • 強力なマスキング（難読化）： 抽出と同時に個人情報を匿名化。セキュリティを担保しながら、実際のデータに基づいた精度の高いテストが行えます。
  • 並列実行とダウンタイム・ゼロ： ソースシステムをロックすることなく、バックグラウンドでのデータ抽出が可能です。

   

  3. S/4HANA 移行における具体的メリット

  Qlik Gold Client を導入することで、移行プロジェクトの ROI は劇的に向上します。

   

  1. 移行の加速： 迅速な概念実証（PoC）が可能になり、プロジェクト全体の納期短延を回避。
  2. TCO（総保有コスト）の削減： 非本番環境のサイズを 10〜50% 削減。インフラコストを最小限に抑えられます。
  3. DevOps の実現： 本番環境からのライブデータを差分更新できるため、常に最新の状況で開発・テスト・トレーニングを回すことができます。

   

  まとめ：2027年に向けて

  SAP S/4HANA への移行を「単なる苦行」にするか「ビジネス変革の好機」にするか。その鍵は、データの扱い方にあります。

   

  Qlik Gold Client は、RISE with SAP や S/4HANA Cloud（プライベートエディション）にも対応しており、SAP Store でも提供されています。膨大なデータに足を取られる前に、データ管理の「スマート化」を検討してみてはいかがでしょうか。

  ---

  詳細はこちら： Qlik Gold Client ページ

  ---

  本ソリューションのポイント

  • 課題： フルコピーによるダウンタイム、コスト増、プライバシーリスク
  • 解決： Qlik Gold Client による「データのサブセット化」と「自動マスキング」
  • 結果： テスト環境構築が数日にから数時間へ。インフラコストの大幅削減とプロジェクトの加速

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  By Qlik_JP_MKT 2026-05-08 / 12:05 AM

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  Design

  The New Write Table

  The write table was introduced to Qlik Cloud Analytics last month so in this blog post, I will review how it works and how it can be added to an app. ... Show More

  The write table was introduced to Qlik Cloud Analytics last month so in this blog post, I will review how it works and how it can be added to an app. The write table looks like the straight table but editable columns can be added to it to update or add data. The updated/added data is visible by other users of the app provided they have the correct permissions. Read more on write table permissions here. Something else to note, if using a touch screen device, is you will have to disable touch screen mode for the write table to work. Looking at the write table for the first time, I found it intuitive and easy to use. Let’s create a write table with some editable columns to see how easy it is.

  The write table object can be added to a sheet like any other visualization. Once it is added, columns can be added the same way dimensions and measures are added to a straight table. Below is a small write table with course information including the course ID, course name, instructor and location.

  

  To add an editable column from the properties panel, click on the plus sign (+) and select Editable column.

  

   

   

   

   

   

   

   

  The new editable column will be added. In the properties for the column, the title for the column can be modified and from the show content drop down, manual user input or single selection can be selected. Manual user input will create a free form column that the user can type into. The single selection option will allow me to create a drop-down list of options that the user can choose from.

  

  I will change the title to Course Level and for show content I will select single selection and add three list items by typing the list item and then clicking on the plus sign to add it to the list. The list items will be displayed in the drop-down in the order they are added but can be rearranged by hovering over the list-item and dragging it to the desired position. List-items can also be deleted by hovering over it and clicking the delete icon that appears to the left.

  

  When you come out of edit mode, the message below will appear for the editable column prompting you to define a set of primary keys.

  

  Once you click Define, you will see the pop-up below where you can select the column(s) that will be used for the unique primary key. This is necessary to save and map the data entered in the editable column to the data model. I will select the CourseID column as the primary key.

  

   Once this is done, I will see the Course Level column with the drop-down of list-items I added.

  

  Let’s add one more editable column that takes manual user unput and name it Notes.

  

  As I add data or update the editable columns, the cells will be flagged orange to indicate that my edits have not been saved. Once I save the table, they will be flagged green and any new values entered are visible to other users. A cell will be blue if another user is currently making changes to the row, thus locking it. Changes are saved for 90 days in a change store (temporary storage location) provided by Qlik. After 90 days, the data will be deleted. It is also important to note that if an editable column is deleted, the data will be lost. This is also the case if the primary key used for the editable column is removed.

  

  It is possible to retrieve the changes from a change store via the change-stores API or an automation. Using the REST connection and the change-store API, the changes made in a write table can be retrieved and stored in a QVD (if needed for more than 90 days) or added to the data model for use in other analytics. Qlik Automate can also be used to retrieve data from the change-store using the List Current Changes From Change Store block or the List Change Store History block. From there the data can be stored permanently in an external system for later use or used in the automation for another process. Qlik Help offers steps for retrieving data from a change-store.

  The write table can make it easy for users to add updates, feedback and important information that may not be available in the data model. Not only can this be done quickly, but it can be immediately visible to other colleagues. Learn more about the write table in the Product Innovation blog along with links to videos and write table FAQs.

  Thanks,

  Jennell

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  By Jennell_Yorkman 2026-01-16 / 02:59 PM

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  Design

  Do More with Qlik

  Today I want to introduce you to a gem that you may be missing out on.  It is the Do More with Qlik community forum lead by @Michael_Tarallo . This fo... Show More

  Today I want to introduce you to a gem that you may be missing out on.  It is the Do More with Qlik community forum lead by @Michael_Tarallo . This forum is made up of concise videos that cover everything from Qlik capabilities to innovative ways to solve business challenges. It is for users of all levels, beginners to seasoned Qlik users, with a wide range of topics. Check out this introductory video to learn more and bookmark the forum.  You do not want to miss out on this!

  Thanks,

  Jennell

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  By Jennell_Yorkman 2026-05-06 / 09:57 AM

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  Qlik Academic Program

  Beyond Dashboards: Preparing Students for the Future of AI-Powered Analytics

  As artificial intelligence continues to transform industries, universities are increasingly exploring how to prepare students for this shift. Modern a... Show More

  As artificial intelligence continues to transform industries, universities are increasingly exploring how to prepare students for this shift. Modern analytics is no longer only about looking at what happened in the past. It is about identifying patterns, predicting outcomes, automating processes, and helping people make faster and more informed decisions.

  One major change is the growing use of conversational analytics. Instead of manually navigating dashboards and filtering reports, users can increasingly ask questions in natural language and receive contextual insights based on trusted data. This makes analytics more accessible to a wider range of users and helps students engage with data in a more intuitive and interactive way.

  Another important development is predictive analytics. Rather than only analyzing historical information, students can now learn how to forecast trends, identify anomalies, and anticipate future outcomes using AI-supported tools and techniques. These skills are becoming increasingly valuable across industries such as finance, healthcare, marketing, operations, manufacturing, and supply chain management.

  At the same time, the rise of AI is also highlighting the importance of trusted and governed data. AI systems are only as effective as the quality and context of the data behind them. As Qlik highlights in its recent Agentic AI presentation, successful AI depends not only on AI capability itself, but also on trusted data, analytical context, and governance.

  This shift creates a valuable opportunity for educators to modernize analytics education and expose students to the technologies and workflows increasingly used in industry. Instead of treating analytics as a static reporting exercise, universities can introduce students to conversational analytics, predictive thinking, AI-assisted insights, and intelligent decision-making.

  Through the Qlik Academic Program, accredited university educators and students receive free access to Qlik Sense Cloud, including the full capabilities of a Qlik Sense tenant. This allows students to gain hands-on experience with interactive dashboards, data exploration, AI-powered analytics features, automation, and predictive analytics tools in a real-world environment.

  The program also provides free access to Qlik Learning, where educators and students can follow structured learning pathways and complete product qualifications to strengthen their analytics and data literacy skills. In addition, educators receive ready-to-use teaching resources including lesson plans, presentations, exercises, and classroom materials that can help integrate analytics into existing courses more easily.

  Importantly, these resources are designed not only for technical programs, but also for business, marketing, operations, finance, and other non-technical disciplines where data literacy is becoming increasingly essential.

  As AI continues to reshape the workplace, helping students understand how to work with data, analytics, and AI together will become more important than ever. Universities now have an opportunity to move beyond teaching dashboard creation alone and instead prepare students to become confident, curious, and data-driven decision makers in an increasingly AI-powered world.

  If you are interested in learning more about the Qlik Academic Program, feel free to contact us at academicprogram@qlik.com. More information about the program, including how to apply, can be found at qlik.com/academicprogram.

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  By ElizCayirli 2026-05-06 / 09:28 AM

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  Support Updates

  Upcoming removal of GET method for /api/v1/apps in Qlik Cloud Analytics, May 202...

  Update, 6th of May 2026: This is now deployed in all regions.   Previously, the /api/v1/apps endpoint could be used to list all apps on a tenant. This... Show More

  Update, 6th of May 2026: This is now deployed in all regions.

   

  Previously, the /api/v1/apps endpoint could be used to list all apps on a tenant. This method has always been unsupported and undocumented, and will be removed in the first week of May 2026.

   

  How will this affect me?

  If you are currently using /api/v1/apps, switch to GET with /api/v1/items instead.

  This can be further filtered by choosing a resource type (such as /items?noActions=true&resourceType=app, /items?noActions=true&resourceType=script, or similar).

  What about similar requests?

  • GET with /api/v1/apps/APPID for a specific app will keep on working
  • POST with /api/v1/apps will also keep on working

   

  For more information, see:

  • Apps | qlik.dev 
  • Items | qlik.dev 

   

  If you have any questions, we're happy to assist. Reply to this blog post or take your queries to our Support Chat.

   

  Thank you for choosing Qlik,
  Qlik Support

  Show Less

  By Sonja_Bauernfeind 2026-04-21 / 08:44 AM

  7 4 2004
• 
  

  Design

  Finance Report with Waterfall Chart

  Several years ago, I wrote a blog post on how to create a profit and loss statement in QlikView. @Patric_Nordstrom has built upon this method and buil... Show More

  Several years ago, I wrote a blog post on how to create a profit and loss statement in QlikView. @Patric_Nordstrom has built upon this method and built a financial statement in Qlik Analytics with a straight table and waterfall chart using inline SVG. In this blog post, I will review how he did it.

  Here is an example of the financial statement structure.

  

   

   

   

   

   

   

   

   

   

   

   

  There are plain rows, such as gross sales and sales return where the amount is the sum of the transactions made against the accounts. There are subtotals such as net sales and gross margin which are a sum of the previous plain rows. And there are also partial sums such as total cost of sales that is a sum of a subset of the previous plain rows, but not all the previous plain rows.

  Patric identified two functions, RangeSum() and Above() that are suitable for calculating the subtotal and partial sums in a table. The RangeSum function sums a range of values, and the Above function evaluates an expression at a row above the current row within a column segment in a table. The above function can be used with 2 optional parameters – offset and count to further identify the rows to be used in the expression.

  The layout table below is used as a template for the financial statement.

  

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

  • The RowStyle column is used for styling the rows making text bold or underlining it. Currently, we cannot use tab or blank tags but hopefully in the future this will become available in the straight table.
  • The RowTitle is the account category that is to be used in the financial statement.
  • RowType is used to as input to calculate the offset and count for the above function.
  • AC is the actual amount.

  The AC column is included here in the layout file for demo purposes but could be calculated from the accounts and transactions in the data model as well.

  In the script, the layout table was loaded, and additional fields were created to support the waterfall chart, specifically offset and count fields to be used with the above function.

  

   

  Here is a view of the layout table with the new fields that were created in the script.

  

   

  After the layout table is loaded and the new fields are created, some master measures can be created to be used in the inline SVG expression. Here are the 3 master measures Patric created:

  mBar is the bar length with an offset that is always 0.

  

   

  
  mStart is the starting position of the bar in the waterfall chart and for subtotals, this is always 0.

  

   

  
  mMax is the max bar length which is used to scale the bars in the waterfall chart.

  

   

  Now the straight table can be created. The RowNr field is added for sorting purposes. The RowTitle field and the AC fields are added to show the account groupings in the financial statement along with their value. The below inline SVG expression for the waterfall chart is the last column added to the straight table. It is made up of 3 parts:

  1. A plain line with an offset
  2. A thin gray line where x=0
  3. A label

  

   

  • The if statement on line 1 will determine if a bar is displayed. Bars will only be visible if the RowStyle is not blank.
  • Line 2 has the viewBox settings and sets the 0 for the x-axis.
  • On line 3 is the light gray line where x=0 and it is displayed on all non-blank rows of the financial statement.
  • Lines 4 and 5 in the yellow box is the plain line with the offset, scaled using the mMax measure to control the length of the line.
  • Line 6 handles the bar color, light green for positive values and red for negative values.
  • On line 7, the if statement is used to set the stroke width of the bar. A thin line is used if the RowType is retrosum and a wider line is used for all other bars.
  • In the red box, on lines 10 through 13, the label text is set and placed either to the left or right of the bar depending on the value. Positive values are placed to the right of the bar and negative values are placed to the left of the bar.

  The result of the financial statement looks like this:

  

   

  To add the text styling (bold and underline) from the layout table, the RowStyle field was added to the text style expression in the RowTitle and AC columns.

  

   

   

  Indentation is added by using the repeat function in the RowTitle column. It will repeat a non-breaking space 6 times if there is a tab tag in the RowStyle field. Otherwise, no indentation is done.

  

   

  If the RowStyle is not blank, a bar is displayed for the waterfall chart and the sum value for the actual amount (or mBar) in this case is displayed.

  

   

  The chart column representation is set to Image in the properties of the straight table.

  

   

   

  While this method looks complex, it is a simple and clean solution for adding a waterfall chart to a financial statement using straight table features and inline SVG. Using the layout table and inline SVG provides room for customization so that the financial report meets the needs and requirements of the user or customer.

  Thanks,

  Jennell

  Show Less

  By Jennell_Yorkman 2025-10-24 / 11:33 AM

  4 10 2831
• 
  

  Explore Qlik Gallery

  Color Palette

    Color Palette Nitin This Dashboard defines a set of predefined colors used to help maintain consistency and clarity across the dashboard, it... Show More

  

  

   

  

  Color Palette

  Nitin

  

  This Dashboard defines a set of predefined colors used to help maintain consistency and clarity across the dashboard, it help to find color code easily and see how it will looks side by side

  

  

  Discoveries

  These shift focus on reducing time to find out best colors and color coding that would improve speed to choose colors, and color combination can improves user sections

  

  Impact

  The Primary impact of this well designed palette is reducing the mental effort of choosing colors. it helps user to refer multiple colors side by side and check which color and suitable for each dimension. and

  

  Audience

  All dashboard developers

  

  Data and advanced analytics

  The Color are always act as a bridge between complex raw data executive decision making. its help to choose right matching colors with color code and help to reduce check each color one by one and see how it looks

  Show Less

  By NitinK7 2026-05-05 / 06:28 AM

  0 3 185
• 
  

  Explore Qlik Gallery

  Knowledge Nuggets 2026(Q1)

  Knowledge Nuggets - 2026(Q1) Insight Consulting A collection of Qlik and Data & AI Literacy related videos and articles Discoveries Contri... Show More

  

  

  Knowledge Nuggets - 2026(Q1)

  Insight Consulting

  

  A collection of Qlik and Data & AI Literacy related videos and articles

  

  

  Discoveries

  Contributors and Keywords

  

  Impact

  Learning resource

  

  Audience

  All Qlik stakeholders

  

  Data and advanced analytics

  Learning resource

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  By Jan-Hendrik 2026-05-04 / 12:15 PM

  0 0 106
• 
  

  Support Updates

  Introducing the QlikView to Qlik Sense Cloud Converter Tool (QV2QS)

  Are you still looking to convert your QlikView environment to Qlik Cloud, but find the concept of migrating each of your apps daunting? Then Qlik has ... Show More

  Are you still looking to convert your QlikView environment to Qlik Cloud, but find the concept of migrating each of your apps daunting? Then Qlik has the answer for you.

  QlikView to Qlik Sense Converter Tool – SaaS in 60

  We've introduced the QlikView to Qlik Sense Converter Tool (QV2QS), which delivers ready-to-use Qlik Sense apps complete with sheets, layout, expressions, and data. Better yet, it deploys them directly to your Qlik Cloud tenant, all of which reduces your conversion times from hours to minutes. 

   

  What's included?

  Developed and supported by Qlik, QV2Q can:

  • Convert .qvw files to Qlik Sense apps, including complete sheets, layout, styling, expressions, and 25+ object types
  • Recreate sheet layouts 1:1, preserving the position and formatting of your QlikView visualizations
  • Bridge the feature parity gap and convert alternate states, conditional show/hide, cycle or drill-down groups, and more!
  • Deploy directly to Qlik Cloud with data reload
  • Produce an Excel report and an HTML executive summary dashboard
  • Support single-app and parallel batch conversion

   

  Why use QV2QS?

  While other converters produce master items from a subset of charts and tables (no sheets, no layout, no text objects, buttons, containers, or backgrounds), QV2QS delivers complete, ready-to-use apps. 

  Not only that, but migrating to Qlik Cloud using QV2Q2 will also enable you to:

  • Preserve dashboards and expressions while unlocking Qlik Answers, Qlik Predict, Qlik MCP, Qlik Automate, and the Qlik Reporting Service
  • Eliminate servers, patching, and legacy licensing costs
  • Compress months of work into days with familiar dashboards available on day one
  • Redesign at your own pace with no big bang required

   

  Are you ready to get started?

  QV2QS is built for Qlik consultants, QlikView developers, Qlik Sense developers, BI teams, and IT administrators migrating from QlikView to Qlik Sense. QV2QS runs as a standalone Windows executable with two interfaces: a guided web-based wizard and a command-line interface.

  Here's what you will need:

  • Review the full documentation at QlikView to Qlik Sense Converter Tool | Help.qlik.com
  • Download the QlikView to Qlik Sense Converter from the Qlik Download Page 
    
    
    
    Tip: Select Search All, then search for QlikView to Qlik Sense in the Product field.
    
    
  • Check out our SaaS in 60 - QlikView to Qlik Sense Converter tool video

   

  Thank you for choosing Qlik,
  Qlik Support

  Show Less

  By Sonja_Bauernfeind 2026-04-23 / 08:00 AM

  1 1 702
• 
  

  Support Updates

  Techspert Talks - Migrating QlikView to Qlik Cloud

  Hi everyone, Want to stay a step ahead of important Qlik support issues? Then sign up for our monthly webinar series  where you can get first-hand ins... Show More

  Hi everyone,
  Want to stay a step ahead of important Qlik support issues? Then sign up for our monthly webinar series  where you can get first-hand insights from Qlik experts.

  The Techspert Talks session from April looked at Migrating QlikView to Qlik Cloud.

   

  But wait, what is it exactly?

  Techspert Talks is a free webinar held on a monthly basis, where you can hear directly from Qlik Techsperts on topics that are relevant to Customers and Partners today.

  In this session we will cover:

  • Converting QlikView documents to Sense Apps
  • Qlik Cloud Advantages
  • Migration Tools

   

  Click here to see the presentation

   

   

  

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  By Troy_Raney 2026-04-27 / 05:41 AM

  0 1 322
• 
  

  Design

  Building a World Cup Bracket App powered by Qlik

  With less than 50 days to go before the 2026 World Cup kicks off across the US, Canada, and Mexico, I wanted to share a project I've been working on t... Show More

  With less than 50 days to go before the 2026 World Cup kicks off across the US, Canada, and Mexico, I wanted to share a project I've been working on that brings together a few pieces of the Qlik platform I think work really well together: Choose Your Champion 2026.

  It's a web app where anyone can fill out their World Cup bracket, get AI-powered predictions for every possible matchup in the tournament powered by Qlik Predict, explore historical World Cup data, and compete on a leaderboard as the competition unfolds.

  You can try it here: https://webapps.qlik.com/choose-your-champion-2026/index.html#/ 

  

  The app is powered by Qlik, with Qlik Cloud Analytics for the data model and Historical Analysis, Qlik Predict for the matchup predictions, and various Qlik APIs to wire everything into a React front-end.

  In this post, I'll walk through how the predictions work under the hood, because that was the most interesting piece to build.

  What's in the app:

  Choose Your Champion is broken into 4 parts:

  • Build a bracket: Pick your group stage winners, advance teams through the knockout rounds, and lock in your champion.

   

  • Check the predictions: For every possible matchup in the tournament, the app surfaces a Qlik Predict generated win probability for each team plus a draw probability. When you're unsure about a matchup, you can pull up the prediction and use it to decide which team advances.

  

  

   

  • Explore historical World Cup data: The app includes various visualizations to help you uncover insights from past tournaments: goals, top scorers, host nation performance, biggest upsets. All powered by the associative engine.

  

   

  • Leaderboard: As real matches get played in June and July, submitted brackets are scored automatically and players are ranked in the leaderboard table.

  

   

  Under the hood: how the predictions work

  This was the fun part. The goal was simple, given two national teams, predict the outcome of a hypothetical match (team A wins / draw / team B wins), but the work that makes the predictions actually useful is mostly in the data, not the model (thanks to no-code ML with Qlik Predict).

  1. The training dataset

  I started with every international football match result from 1872 to March 2026. There's a well-maintained open dataset on GitHub (credit: martj42/international_results) that gets updated after every international window, about 49,000 matches in total.

  From that raw history, I built a training dataset focused on the modern era (2010 onwards) and only competitive matches (qualifiers, continental tournaments, World Cup finals). Friendlies got filtered out because they're noisy since teams often don't play their A squads, and the stakes don't match what happens in a real tournament.

  That left me with around 9,400 training rows, each representing a real historical match with a known result, enriched with 27 features describing both teams' state going into that match:

  • Elo ratings for both teams
  • FIFA rankings and points snapshot to the match date
  • Rolling 10-match form per team: win rate, goals for, goals against, goal difference
  • Head-to-head history in the last 10 meetings
  • Context flags: neutral venue, tournament tier, cross-confederation
  • World Cup pedigree: a score rewarding teams for deep runs in past tournaments, with more recent success weighted heavier

   

  2. ML Experiment

  Once the training CSV was in shape, I uploaded it to Qlik Predict, pointed at the result column as the target, and let it do its thing. This is where Qlik Predict really shines, zero code needed. No Python notebooks, no sklearn, no hyperparameter grids to tune. You just upload your data, pick a target, and it does the heavy lifting with full explainability on the outcomes and what drives the predictions.

  Qlik Predict runs multiple algorithms in parallel: LightGBM, CatBoost, XGBoost, Random Forest, and a few others, tunes their hyperparameters, and picks the best performer by F1.

  

  On my first run, I left all the columns in the dataset checked, including the team name columns (team_a, team_b). When I looked at the SHAP importance chart afterward, team_b and team_a were ranking as the #2 and #3 most influential features, meaning the model was essentially learning "team X usually wins" rather than learning from the engineered features.

  I created a new version, went back to the Data tab, unchecked the team name columns and a few date fields (which were also ranking higher than they should), and re-ran the experiment. Qlik Predict automatically dropped several more low-importance features during training, leaving a clean, focused feature set. The F1 did not change a lot (stayed at ~0.50), but the SHAP chart now showed the model leaning on exactly the signals we want:

  1. elo_diff
  2. rank_diff
  3. is_neutral
  4. h2h_team_a_advantage
     etc...

  

   

  A few other calls that mattered:

  • Filtering to competitive matches only. A friendly between a top side's B squad and a mid-tier opponent tells you almost nothing about what happens in a World Cup group stage game.
  • Exponential decay on World Cup pedigree. A deep run in 1970 still counts, but less than one in 2022.
  • Removing rows with too many missing features. FIFA rankings don't go back to the 90s for every team, so some rows had to get dropped.

   

  3. The apply dataset

  Training gives you a model and to use it, you need an apply dataset with new rows you want predictions for.

  For Choose Your Champion, I generated every possible pairing of the 48 qualified teams, which comes out to 1,128 unique matchups. Each row has the same 27 features as the training dataset, but computed as a current snapshot: each team's Elo today, their current FIFA ranking, their most recent 10-match form, and so on.

  I fed that into the deployed model and got back a probability distribution for every matchup: P(team_a_win), P(draw), P(team_b_win).

  

  The web app

  The web app is a React front-end that connects to the Qlik tenant over anonymous access via @qlik/api, so users never see a login screen or have to authenticate against a tenant. The bracket UI pulls predictions from the Qlik Sense data model, so whenever a user opens a matchup, they're looking at data straight from Qlik.

  For the historical World Cup section, I used a mix of @qlik/embed components when I needed a quick, ready-to-use chart, and custom nebula.js + picasso.js visualizations when I needed more control over the styling to match the app's look and feel. Both approaches work against the same underlying Qlik Analytics app, so everything stays consistent and governed in one place.

  

   

  A few takeaways

  If you're thinking about building something similar, a few things worth keeping in mind:

  Spend the time on feature engineering. The difference between a model that predicts noise and one that predicts football is almost entirely in the features. Qlik Predict handles algorithm selection and tuning well, but it can only work with what you feed it.

  The integration is where Qlik Predict pays off. Once a model is deployed, scoring a new dataset and pulling scores back into a Qlik Cloud Analytics app takes one load script. No Python services to maintain, no separate MLOps platform to stand up, no JSON plumbing between systems. That end-to-end data prep, modeling, predictions, and analytics all living in one platform is the thing that made this project come together fast!

  Go fill out your bracket

  The World Cup starts June 11, so there's plenty of time to get your bracket in and earn your spot on the leaderboard before kickoff. If you're curious about how any of this was built, leave a comment or reach out to me directly!

  And if you want to learn more about Qlik Predict and start using it, visit: https://www.qlik.com/us/products/qlik-predict 

  P.S: I have attached both Training and Apply datasets if you'd like to use them in your own Qlik Predict experiment.

  
  Thank you!

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  By Ouadie 2026-04-24 / 03:09 PM

  0 8 1082
• 
  

  Support Updates

  Action required: Update your Qlik Automate Salesforce connector authentication o...

  Salesforce is rolling out mandatory security updates to how connected apps handle OAuth authentication.   What does this mean for me? To keep your Qli... Show More

  Salesforce is rolling out mandatory security updates to how connected apps handle OAuth authentication.

   

  What does this mean for me?

  To keep your Qlik Automate Salesforce connector working after Tuesday, 5th May 2026, you'll need to take a quick, one-time action: update a setting in your connection and re-authenticate to Salesforce.

  This cannot be done automatically, nor can it be done before the release. A manual step is required on or after the 5th of May.

   

  Why is this happening?

  Salesforce has updated its OAuth security requirements for connected apps. You can read Salesforce's full announcement here: Mandatory Security Updates for Connected Apps.

  Qlik Automate has updated the Salesforce connector to comply with these new security requirements. However, because of the nature of this change, existing connections cannot be migrated automatically.

   

  What action do I need to take?

  Follow these steps after the release on Tuesday, the 5th of May:

  1. Open your Salesforce connection settings, navigate to the Salesforce connection in Qlik Automations, and open its configuration.
     
     
  2. Add your domain. In the connection settings, locate the Enhanced_domain field (an existing input field) and enter your Salesforce org's custom domain.
     
     Make sure to include the "my" suffix if your domain has it. By using this, the 'environment' input is ignored.
     
     Format: https://{enhanced_domain}.salesforce.com.
     
     If you're unsure of your domain, you can find it in your Salesforce org under Setup → My Domain.
     
     
     
  3. Re-authenticate. Once your domain is entered, you'll be prompted to log in to Salesforce again and authorize the updated app. Complete the login flow to re-establish the connection.
     
     
  4. Verify the connection. Run a test automation that uses the Salesforce connection to confirm everything is working correctly.

   

  What happens if I don't act?

  If you don't complete these steps after the release date, your Salesforce automations will stop working and return a 401 Unauthorized error. No data will be lost, but you will need to complete the steps above to restore access.

   

   

  If you have any questions, we're happy to assist. Reply to this blog post or take your queries to our Support Chat.

   

  Thank you for choosing Qlik,
  Qlik Support

   

  Show Less

  By Sonja_Bauernfeind 2026-04-24 / 08:00 AM

  0 0 669
• 
  

  Qlik Learning

  Qlik Learning is now available in French!

      We’re excited to announce a major step forward in making Qlik Learning more accessible, personalized, and impactful; translations are now availab... Show More

  

   

   

  We’re excited to announce a major step forward in making Qlik Learning more accessible, personalized, and impactful; translations are now available for all users. Our first stop is France. 

  Your feedback drives this launch. One of your top requests was learning in your preferred language, and today we’re making that happen. 

  To accelerate availability, we’re leveraging machine learning–based translation technology to quickly bring essential content to more learners around the world. We’re kicking things off with our first set of courses in French, followed by Japanese, Italian, and many more languages coming soon. 

  🌐 Getting Started 
  Changing your language is simple: 

  • Select your preferred language from the option at the bottom of any Qlik Learning page. 

     

     

   

  ⚠️ What to Expect 

  • Not all content is currently available in every language.  

  • The Topics menu displays only content available in your selected language.  

  • As a result, English content may appear more comprehensive at this time.  

  • We are actively expanding and enhancing translated content across the platform.  

  • Translations will continue to improve as we refine quality and address remaining gaps.  

   

  🛠️ Additional Notes 

  • Machine translations may vary in accuracy and should not be considered fully definitive.  

  • To maintain consistency across, certain terms remain untranslated (for example, product names, Qlik-specific terminology, and syntax).  

   

  We truly appreciate your feedback and patience as we continue to evolve. To provide feedback, click the stars 

  at the top of this page and share your feedback with us.

   

  We hope you enjoy Qlik Learning in the language that works best for you! 

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  By Bilge_Kara 2026-04-23 / 01:25 PM

  0 0 107
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  Qlik Academic Program

  From Dashboards to Decisions: What the Agentic Era Means for LATAM Universities

  Something quietly remarkable happened in the first quarter of 2026. While the public conversation was still asking whether AI agents w... Show More

  Something quietly remarkable happened in the first quarter of 2026. While the public conversation was still asking whether AI agents would really change business, Gartner reported that 40% of enterprise applications are expected to integrate task-specific AI agents by the end of 2026, up from less than 5% a year ago. G2's August 2025 survey of enterprise buyers found that 57% of companies already had AI agents in production, not as chatbots, but as autonomous systems executing workflows, monitoring compliance, and coordinating decisions across business functions.

  For those of us who work at the intersection of data, analytics, and higher education, this isn't a distant trend. It's a curriculum question.

  The shift is not from manual to automated. It's from tools to teammates.

  For two decades, the defining promise of business intelligence has been "self-service analytics", empower every user to query, visualize, and explore data themselves. In the agentic era, the paradigm changes. AI agents are not a new tool in the analyst's toolkit; they are analysts. They plan multi-step tasks, call APIs, reason across data sources, and increasingly execute actions without waiting for a human prompt.

  At Qlik Connect 2026, the message was direct: enterprises are closer to agentic AI than they think, because the foundation they already built, governed data, trusted metrics, clear business logic — is exactly what agents need to operate reliably. In February, the general availability of Qlik's Model Context Protocol (MCP) Server made it possible for third-party assistants, including Anthropic's Claude and OpenAI's ChatGPT, to access governed enterprise data through Qlik's APIs rather than scraping dashboards. The dashboard is no longer the endpoint. It's one of many surfaces where a decision gets made.

  The new skills gap is not technical. It's architectural.

  Here is the uncomfortable reality: Gartner projects that over 40% of agentic AI projects will fail by 2027, not because the models aren't capable, but because legacy systems, poor data architectures, and weak governance can't support autonomous execution. Deloitte's 2026 State of AI in the Enterprise report, based on a survey of 3,235 leaders across 24 countries, found that only 25% of organizations have moved 40% or more of their AI pilots into production, and just 21% have a mature governance model for autonomous AI agents.

  The scarce capability is no longer "who can build a dashboard." It is:

  • Who can design a KPI dictionary an agent can trust?
  • Who understands bounded autonomy, where humans set the rules and agents execute within them?
  • Who can distinguish explainable reasoning from confident hallucination?
  • Who can architect a data product that an agent can consume without a human in the middle?

  These are not niche skills reserved for data engineers. They are the new baseline for anyone graduating into a workforce where, by 2028, Gartner estimates 15% of day-to-day decisions will be made autonomously.

  The LATAM opportunity

  This is where Latin America has a genuine strategic window. Our universities often face the critique of "catching up" on technology adoption. In the agentic era, that framing is misleading, the agentic shift resets the starting line for everyone. Institutions, anywhere in the world, that graduate students fluent in data governance, explainable AI, and human-agent collaboration will be the ones supplying the talent that enterprises are already scrambling to hire.

  According to DataCamp's 2026 State of Data & AI Literacy Report, 88% of enterprise leaders say basic data literacy is important for day-to-day work, 60% report a data skills gap in their organization, and organizations with mature literacy programs are nearly twice as likely to see strong AI returns. The companies that will hire our graduates next year are telling us, in plain terms, what they need.

  What universities can do now

  Three practical moves that don't require launching a new degree program:

  1. Teach governance alongside analytics. Every data course should include a module on trust, lineage, and explainability, not as an afterthought, but as the foundation that makes agents usable.
  2. Shift from tool training to platform fluency. Students don't need to master one vendor; they need to understand how governed data, semantic layers, and agent orchestration fit together.
  3. Build industry-embedded learning paths. Programs like the Qlik Academic Program exist precisely because real agentic work happens on real data with real stakes. Classroom theory is not enough.

  The agentic era will not be defined by which models win. It will be defined by which people, and which regions,learned to work alongside them first.

  ---

  By giving students, professors, and universities free access to analytics software, learning content, and certifications, the Qlik Academic Program helps education stay aligned with the data trends shaping 2026 and prepares learners for the jobs of tomorrow.

  Join our global community for free: Qlik Academic Program: Creating a Data-Literate World

   

  Share

   

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  By TalitaBM 2026-04-22 / 08:53 AM

  0 2 119