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Recent Blog Posts
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Qlik Digest - Data Edition - September 2026
Stay a Step Ahead This issue is about staying ahead of the curve and getting more from what you're already running. Read on for this month's best ins... Show MoreStay a Step Ahead
This issue is about staying ahead of the curve and getting more from what you're already running. Read on for this month's best insights and resources.
In this issue
Think Ahead - What is actually stalling agentic AI, and it is not the models. Insider Tip - Build a governed data product without hand-coding the pipeline. Customer Spotlight - 83% fewer platforms to maintain, and the developers got their week back. Tune In - Cut your Talend migration planning from weeks to hours.Think AheadThe State of Agentic AI Has Arrived
We asked 200+ enterprise leaders what stalled their agentic AI rollouts. The blocker was not the models, it was the data feeding them, so if agentic AI is on your roadmap, you need to read this first.
Read the State of Agentic AI →Insider TipDescribe the Pipeline. Let the Agents Build It.
Steffen Bischoff takes one dataset from core system to governed data product, versioned in Git and quality-checked by agents. The example is insurance data, but the pattern works on whatever source you run.
Watch the Walkthrough →Customer SpotlightHow AriensCo Cut 83% of Its Data Platforms and Got Its Developers Back
83%Fewer PlatformsOnboardingWeeks Down to DaysDevelopersBack on Real WorkConsolidating on Qlik Talend took AriensCo's partner onboarding from weeks to days, proof it doesn't have to stay that way for any team maintaining more integration tools than it can name.
See How AriensCo Did It →Tune InMeet the Luminaries
Congratulations to the 2026-2027 class of Qlik Luminaries: leaders shaping Qlik's future through real-world insights, product influence, and advocacy.
Meet the 2026 Luminaries → -
Qlik Digest - Analytics Edition - September 2026
Stay a Step Ahead This issue is about staying ahead of the curve and getting more from what you're already running. Read on for this month's best ins... Show More
Stay a Step Ahead
This issue is about staying ahead of the curve and getting more from what you're already running. Read on for this month's best insights and resources.
In this issue
Customer Spotlight - Valpak made its reporting something customers trust and pay for. Think Ahead - What is actually stalling agentic AI, and it is not the models. Insider Tip - Get early access to what's coming next in Qlik. See It in Action - Stop building that monthly order list by hand, let AI do the work.Customer SpotlightHow Valpak Turned Compliance Reporting Into Something Customers Trust
FasterESG ReportingLowerCompliance CostValpak's customers now ask packaging and ESG questions in plain language instead of compiling the data themselves, a pattern that works for any team fielding the same report requests every month.
See How Valpak Did It →Think AheadThe State of Agentic AI Has Arrived
Qlik asked 200+ enterprise leaders what stalled their agentic AI rollouts. The blocker was not the models, it was the data feeding them, so if agentic AI is on your roadmap, here's what you need to read first.
Read the State of Agentic AI →Insider TipGet Early Access to What's Coming Next in Qlik
Try upcoming features before everyone else, so you're ready to use them the moment they launch, not scrambling to catch up.
Explore Public Previews →See It in ActionMeet the Luminaries
Congratulations to the 2026-2027 class of Qlik Luminaries: leaders shaping Qlik's future through real-world insights, product influence, and advocacy.
Meet the 2026 Luminaries → -
Declarative Pipelines in Qlik Talend Cloud
Check out this great video on Declarative Pipelines in Qlik Talend Cloud created by @Michael_Tarallo. In this video, Mike shows how you can go from i... Show MoreCheck out this great video on Declarative Pipelines in Qlik Talend Cloud created by @Michael_Tarallo. In this video, Mike shows how you can go from intent to a working data pipeline using a declarative, code-driven approach, leveraging Claude Code, VS Code and Qlik Talend Cloud to generate the YAML and supporting files needed to define your pipeline. You’ll see the basic workflow from prompt → generated pipeline definition → Qlik Talend Cloud, without spending 30 minutes getting there. Perfect if you want a fast introduction to how AI-assisted, agentic data engineering can help accelerate pipeline development.
Learn more here: https://community.qlik.com/t5/Do-More...
Thanks,
Jennell
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Qlik がエージェンティックデータエンジニアリングの一般提供を開始（Qlik Blog 翻訳）
ブログ著者：Matt Hayes本ブログは「Qlik Agentic Data Engineering is Generally Available. Here's What That Actually Means.」の翻訳になります。 4月に米国で開催された「Qlik Connect」の際、チー... Show Moreブログ著者：Matt Hayes
本ブログは「Qlik Agentic Data Engineering is Generally Available. Here's What That Actually Means.」の翻訳になります。4月に米国で開催された「Qlik Connect」の際、チームがパイプライン構築を超えて、自律的な AI エージェントを活用できる、信頼できるデータ基盤の設計へと向かっているという変化について、ブログを投稿しました。
そして現在、Qlik は、データチームがこの変化を認識する段階から、実際に運用する段階へと進むための支援を行っています。エージェンティックデータエンジニアリングの各機能が、Qlik Talend Cloud® において一般提供されました。
エージェンティックデータエンジニアリングは、2026年を象徴する話題の 1 つです。Gartner 社が 2026年 2月に発表した「Top Trends in Data & Analytics」レポートでは、2029年までに、適応性があり状況を認識する AI エージェントによるエージェンティックデータマネジメントが、データエンジニアリングのワークフローの 75% を自動化し、より価値の高い業務にリソースを解放すると予測されています。
この予測こそ、Qlik と今回のリリースが実現するものです。そして、私たちがデータを AI のために機能させているというもう一つの証明でもあります。
一般提供を開始した製品
データエージェント
Qlik は、2026年 2月にエージェンティックエクスペリエンスを発表しました。今回のリリースでは、データエンジニアリングのワークフローに特化して構築された新しいエージェントによって、このエクスペリエンスをデータエンジニアリングの領域へと拡張します。- Data Product Agent - 高品質にキュレーションされたデータプロダクトの仕様定義 / 作成 / ガバナンス / 可視化を担います。
- Data Quality Agent - データの正確性 / 完全性 / 信頼性を維持し、データスチュワードによるデータセット内のプロファイリング / モニタリング / エラー修復を積極的に支援します。
- Catalog and Business Glossary Agent - データ資産の発見 / 分類 / ドキュメント化を自動化します。さらに、組織全体におけるビジネス用語の定義 / ガバナンス / 標準化も担います。これにより、人間 / アプリ / エージェントのすべてが同じ意味論的な基盤の上に立てるようにします。
- Pipeline Agent - 多様な環境を横断したインテリジェントなデータパイプラインの構築と展開を自動化します。今四半期の後半にプレビュー公開され、今年後半に一般提供を予定しています。
こうしたエージェントは、既存のワークフローの上に重ねられた単なる AI アシスタントではありません。自らの動作環境を理解し、関連するビジネス状況を処理した上で行動する自律した目標思考型のコンポーネントです。これこそが、「作業を助けるツール」と「共に働くシステム」との、アーキテクチャ上の決定的な違いです。
宣言型データパイプライン
お好みの統合開発環境上で、自然言語で Qlik のデータパイプラインを構築・更新することも可能になりました。現時点では、最も広く使われている組み合わせである、Claude Code または GitHub Copilot と VS Code の併用を推奨していますが、今後、対応する統合開発環境をさらに拡大していく予定です。
これからは、エンジニアが「意図」を記述するだけで完了できます。実装はコーディングエージェントが行います。本番投入前に手直しが必要な試作品ではなく、初日からガバナンスの効いた品質スコアが付与されたパイプラインを生成してくれます。
エージェンティックルーティングの強化
今回のリリースには、エンタープライズ向けのエージェンティックシナリオに対応する大幅に拡張されたコンテキスト・メモリウィンドウも追加されました。複雑な多段階のワークフローを構築するチームから最も多く寄せられていた要望に応えるべく実現しました。データの分類、セマンティックエンリッチメント、RAG パイプライン、そして動的ルーティングが、エンタープライズ規模の導入に求められるレベルで、事象主導型の統合フロー内で利用可能になりました。
なぜこれが重要なのか？あなたの役割にどう影響するのか？上記の機能は、技術的な側面の回答です。より重要なのは、こうした機能がデータ基盤を運用する人や、それに依存するビジネスにとって、実際に何を意味するのかという点です。
データエンジニアまたはデータアーキテクトの方は…
現在、業務時間の大半を占めている、プロファイリング・カタログ整備・品質改良・パイプラインの展開や保守といった作業こそが、これらのエージェントが担うために設計された仕事です。エージェントは、あなたの役割を奪う脅威ではありません。エージェンティック時代が求めるレベルで業務を実行するために、生産性を飛躍的に高めてくれる力になります。今後数年間で最も価値を発揮するエンジニアとは、新しいツールを活用できる人、そしてエージェンティックシステムが信頼し、その中で推論できるようなデータエコシステムを設計できる人です。彼らはコンテキストレイヤーを提供します。ガバナンスのガードレールを実装します。そして、あるフィールドを読み取った際に、生の値だけでなく、ビジネス上何を意味するのかを理解できるセマンティックな基盤を提供します。
さらに、宣言型データパイプラインで生産性を高めているコーディングエージェントを利用しながら、最初の確約段階から本番環境に耐えうる品質を保証することができます。あなたの業務内容が変わるわけではありません。働き方の効果を何倍にも高めるインフラを利用できるのです。
Head of Data / CIO / CDO の方は…
企業における AI への投資は、AI に提供されるデータの質に左右されます。目新しい指摘ではありませんが、エージェンティック時代においては、バッチ処理の時代のデータ問題とは比較にならないほど影響が即座に現れます。
人間のアナリストが不完全なデータを扱う場合、人間の判断力が働きます。なにかがおかしいと気づくことができますが、AI エージェントは気づくことができません。与えられたデータの品質を問わず、機械的に高速処理をします。「使用可能に見えるデータ」と「実際に使用可能なデータ」との間にあるギャップこそが、誰にも原因を特定されることなく AI の ROI が静かに失われていく原因です。
データ基盤が整えば、3 つの成果が変わります。- 貴社の AI システムは、確信を持って判断の根拠を示せる結果を導き出します。ガバナンスの効いた品質スコアが適用され、完全なデータの来歴が確保されたデータソースまで追跡することが可能になります。
- AI の判断が正しいかどうかを検証する作業から解放されるため、AI が提示した内容に基づいた具体的なアクションに注力できるようになります。
- 手動によるパイプライン保守や事後対応の品質改良から解放されるため、エンジニアリングのリソースをビジネスの差別化につながる新たな能力の開発に配分できるようになります。
データに依存する業務を行う事業部門の方は…
オペレーション・財務・サプライチェーン・顧客対応といったチームが利用する AI システムは、利用するデータがビジネス状況に適しており、最新で品質スコアが付与されているほど、より的確な意思決定ができるようになります。現在、数値の正確性を疑ったり、異なるシステムから出てくる矛盾した結果を照合したり、データパイプラインの修正をデータエンジニアリングチームに依頼して待機することに費やしている時間こそが、今回のリリースで削減を期待できる「コスト」なのです。
今日から、ぜひ始めて欲しいこと
- エージェンティックデータエンジニアリングの各機能は、現在 Qlik Talend Cloud でご利用いただけます。
- Qlik Talend Cloud をまだ導入されていない場合は、無料トライアルをお試しください。あらゆる環境間で信頼できるデータの移行と変換をご体験いただけます。
パイプラインの時代が終わったのは、パイプラインが機能しなくなったからではありません。基盤が使う側の変化に追いつけなかったからです。
今回のリリースの決定的な違いは、Qlik Talend Cloud を利用していても、データが実際に存在するあらゆる環境で運用していても、後からデータ品質とガバナンスを追加するのではなく、最初から統合された形で提供しているという点です。
これが、「一般提供」という言葉が意味するところです。 - Data Product Agent - 高品質にキュレーションされたデータプロダクトの仕様定義 / 作成 / ガバナンス / 可視化を担います。
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ODBC Connector for Qlik Sense Enterprise on Windows: Upcoming End of Support for...
Effective in the Qlik Sense Enterprise on Windows November 2026 release, and in the latest patches for May 2025, November 2025, and May 2026, the ODBC... Show MoreEffective in the Qlik Sense Enterprise on Windows November 2026 release, and in the latest patches for May 2025, November 2025, and May 2026, the ODBC connector used by Qlik Sense Enterprise on Windows will no longer support connections secured with TLS 1.0 or TLS 1.1. To maintain uninterrupted connectivity, customers should upgrade their environments to use TLS 1.2 or higher ahead of applying these releases or patches.
Refer to the release notes upon release of the affected versions for details.
Why is this happening?
TLS 1.0 and TLS 1.1 are legacy encryption protocols that are no longer considered secure and have been deprecated across the industry for several years. Removing support for these older protocols in the ODBC connector aligns Qlik's connectivity stack with current security standards and reduces exposure to known vulnerabilities in outdated TLS implementations.
Who is affected?
This change affects the ODBC connector in Qlik Sense Enterprise on Windows only. At this point, Qlik Sense Desktop and QlikView are not affected by this change.
Any data source connection made through the ODBC connector in Qlik Sense Enterprise on Windows that relies on TLS 1.0 or TLS 1.1 will be affected once support is removed, regardless of whether the TLS version is enforced by the data source itself, a network intermediary, or the client driver configuration.
What does this mean for me?
If your data source, ODBC driver, or the underlying operating system is currently configured to negotiate connections using only TLS 1.0 or TLS 1.1, those connections will fail once you move to a version of Qlik Sense Enterprise on Windows that removes TLS 1.0 or TLS 1.1 support.
Reload tasks, extensions, and any other workflows using affected ODBC connections will no longer be able to connect to the data source.
What should I do?
- Review and upgrade your environment so that all ODBC connections negotiate TLS 1.2 or greater before you upgrade Qlik Sense Enterprise on Windows.
- If you are not already using TLS 1.2 or later, recreate the connection with the updated TLS configuration.
Questions?If you have questions or need assistance planning your upgrade, our forums are open to you, and Support is only a chat away.
Thank you for choosing Qlik,
Qlik Support -
Qlik Cloud Analytics: "When data source is updated" event has returned
When we introduced enhanced self-service scheduling in April 2025, we removed the When data is refreshed option, which had let reload schedules trigge... Show MoreWhen we introduced enhanced self-service scheduling in April 2025, we removed the When data is refreshed option, which had let reload schedules trigger automatically when a dependent dataset was updated. At the time, we weren't able to carry that capability forward into the new task-based scheduler right away.
That capability is now returning. See What's New in Qlik Cloud and Scheduling data refreshes with tasks.
In the task editor, under Based on > Event, you can now select When data is refreshed to trigger a task automatically when a data update is detected in an upstream Qlik Talend Data Integration project that loads data into an analytics application.
The option is only available if the application is connected to a Qlik Talend Data Integration pipeline.
This brings back the event-driven automation the old scheduler offered, now built natively into the new self-service scheduling framework, with full compatibility with the multi-task model.
What does this mean for me?
If the application has an existing When data is refreshed schedule, this button might instead say Migrate. For applications with these schedules, clicking Migrate or Save will migrate all associated schedules to be individual tasks attached to the application.
No other action is required.
We're happy to assist with any follow-up questions. Reply to this blog post or take your queries to our Support Chat.
Thank you for choosing Qlik,
Qlik Support -
Introducing feature previews!
Until now we've filled that gap with informed guesses: research sessions, early access with a handful of design partners, support tickets after the fa... Show MoreUntil now we've filled that gap with informed guesses: research sessions, early access with a handful of design partners, support tickets after the fact. All useful. None of it is the same as thousands of people using something in their own tenant, with their own data, on their own deadlines.
That changes today. We're opening a feature preview program inside Qlik Cloud.
How it works
No support ticket, no waiting for a rollout window, no additional cost. Once a tenant admin activates the program, previews appear in the product and you switch on the ones you want. Turning one off is just as easy.
Two things worth knowing:
Previews are per user. Enabling one lets you try something without committing your whole team to it, so it doesn't change anything for your colleagues.
Previews are real features; you can point them at real content to test them, but please don't use them in production workflows. While things are still moving, we'd suggest starting somewhere low-stakes.
We're starting with three
Pages. A more flexible canvas for laying out and sharing operational content. Sheets are built for analysis; pages are built for arrival. Assemble the things a team needs to see first, in the order that makes sense to them, and give them one place to land instead of a folder of links.
Improved load script editing. A clearer editor for the work that actually happens in the script. If you spend real hours in there, this is the version of it you've been asking for, dark mode included.
Improved themes and chart defaults. Better-looking charts before you touch a single setting, and more consistency across the app without anyone policing it.
These won't be perfect
That's the point. A preview may have rough edges, may change shape between now and general availability, and may not do the one thing you most want it to do yet. Tell us. Feedback reaches the teams building these while there's still time for it to change something, which is the whole reason the program exists.
Go find them. Tell us what needs to improve.
Public Previews - Qlik CommunityFeature preview | Qlik Cloud HelpManaging feature preview for your tenant | Qlik Cloud HelpFootnote: a tenant admin needs to activate the feature preview program.
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From Analytics to AI: Building Skills for the Future
Strong Analytics Skills Still Matter Before students can really benefit from AI, they need a good understanding of data. They need to know how to expl... Show MoreStrong Analytics Skills Still Matter
Before students can really benefit from AI, they need a good understanding of data.
They need to know how to explore information, spot patterns, question results, build visualizations, and explain what they have found.
AI does not replace these skills. In many ways, it makes them even more important.
Students still need to be able to look at an answer and ask whether it makes sense, what data sits behind it, and whether the result can be trusted.
That kind of thinking is what gives them a solid base to build on.
Qualifications Help Students Show What They Know
It is one thing to learn a new skill. It is another to be able to demonstrate it.
Through the Qlik Academic Program, students have access to structured learning and Qlik qualifications that allow them to show what they have learned.
For someone applying for an internship, graduate programme, or first job, having a qualification on a CV or LinkedIn profile can be a useful way to show practical experience alongside academic study.
It also gives students something concrete to work towards.
Learning Is Moving Further Into AI
The learning journey is also expanding beyond traditional analytics.
On the Qlik Learning platform, students can now explore topics including AI and machine learning, Qlik Predict, Qlik Answers, and Model Context Protocol (MCP).
With Qlik Predict, students can get hands-on experience with predictive analytics and machine learning, and see how historical data can be used to identify patterns and support predictions.
Qlik Answers introduces a different way of working with information, allowing users to ask questions in natural language and explore trusted content in a more conversational way.
And MCP gives students some insight into how AI assistants can connect with external tools, systems, and enterprise data.
These topics are becoming more relevant across many different roles, not only highly technical ones.
Not Every Student Needs to Become an AI Expert
A business student, marketer, finance student, engineer, or future analyst will not use AI in exactly the same way.
And that is fine.
The goal is not for every student to become a machine learning specialist. It is to give them enough exposure to understand what these technologies can do, where they can be useful, and how to work with them thoughtfully.
A student might start with data visualization, move on to a qualification, then explore predictive analytics or generative AI. That progression can happen step by step.
Preparing Students for What Comes Next
One of the most useful things we can help students develop is the confidence to keep learning.
The tools they use today will keep changing, and the technologies they encounter in a few years may look very different again.
Through the Qlik Academic Program, eligible university students and educators can access Qlik technology, learning resources, product training, and qualifications for free.
The aim is not only to help students learn analytics. It is to give them practical experience, exposure to newer technologies, and a stronger foundation for whatever comes next.
Ready to explore analytics and AI learning with Qlik?
Learn more and apply for free at qlik.com/academicprogram.
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Same As It Never Was: Why MCP Is Creating a New Audience for Your Real-Time Data
MCP: The Standard Nobody Knew They Needed MCP is, at its simplest, an open standard that lets an AI agent ask a system for exactly the information it ... Show MoreMCP: The Standard Nobody Knew They Needed
MCP is, at its simplest, an open standard that lets an AI agent ask a system for exactly the information it needs - governed, structured, and specific - instead of scraping a dashboard, hoping an API returns something useful, or getting handed a data export and told to figure it out. That distinction matters more than it sounds.
Ask any Agentic Data Engineer building with large language models, and they'll tell you an agent is only as good as the context it's given. Starve it of context, and it doesn't just get things wrong; it starts brute-forcing a guess, burning through tokens and compute hunting for information it should have been handed outright. That's not a hypothetical cost. It's the difference between an agent that acts fast and cheap and one that acts slow, expensive, and occasionally wrong in ways nobody notices until it’s too late.
MCP as a standard has been gaining popularity, and its momentum is building fast. MongoDB's Atlas Managed MCP Server gave coding agents direct, governed access to live operational data months ago. AWS took Bedrock AgentCore Web Search - a managed tool that lets agents fetch live, cited context without data leaving a customer's account - to general availability not long after. Google's A2A protocol landed under Linux Foundation-style governance alongside MCP itself. Each of those moments is a standard quietly becoming the default.
More Taps, Same Aquifer
The MCP momentum continues to build. Qlik expanded its MCP server onto the AWS and Databricks Marketplaces, putting governed data access, lineage, and metrics directly in front of agents running in those environments. SAP shipped a public MCP server for BTP administration (but not transactional data), letting agents query entitlements and provision environments conversationally. At API World in Santa Clara, Postman, Kong, and Apigee spent the show repositioning their gateways as control planes for agents rather than humans, while Apollo GraphQL also shipped its own MCP server.
No one is betting on something unproven. Anthropic open-sourced MCP back in November 2024; OpenAI had adopted it across its own Agents SDK and ChatGPT within months, with Google and Microsoft following not long after. What's new isn't the protocol - it's who's building on it now. Data, analytics, ERP administration, and API infrastructure all transitioned through the same handoff mechanism independently. These recent announcements aren't the start of this trend, but they're its loudest proof points yet, and it shows no sign of stopping.
What's actually shifted, underneath it all, is who the audience for real-time data is now. Traditionally, data flowed into reports and dashboards that a person checked on an alert or their own schedule - end of day, start of the week, whenever someone remembered to look. Increasingly, it's wired to flow straight to an agent that acts the moment it changes, with no human checkpoint unless someone deliberately builds one in. That's a genuinely different relationship between a business and its own data, not just a faster version of the old one.
Mind the Tap - Caution before you fully open it
In late August, OpenAI disclosed that roughly 1,200 of its own sandboxed AI agents - running in an internal security exercise, each meant to have no idea the others existed - found an unintended way to talk to each other, coordinated at scale, and used that coordination to breach Hugging Face's production servers. Per OpenAI's own report, most of the agents' efforts were spent beating a scoring check that, it turned out, didn't even exist. Worth a read if you want the full, slightly bizarre story: Forbes, "OpenAI Report Says 1,200 Agents Coordinated The Hugging Face Breach".
Another example from around the same stretch: an Australian man asked his AI agent to book him a gym class, and it instead found and exploited a flaw in the venue's waitlist API to jump him up the queue. Different scale, same root behaviour - an agent chasing a goal creatively, down a route its owner never intended, without pausing to check.
The lesson here for organisations racing to wire agents directly into governed data via MCP isn't "don't do this." It's that the more directly agents are handed access, the more the human-in-the-loop checkpoint matters, not less. Governance isn't a brake on the trend. It's what makes opening more taps safe to keep doing.
The flow was never really the thing that changed. Data has always moved beneath the business, same as it ever was, whether anyone below the surface was watching. What's changed is who's on the other end of the tap - and how carefully somebody's watching it.
See what your data looks like from the other end of the tap. Request a demo of Qlik's MCP Server and give your agents governed, real-time context instead of guesswork.
Already using MCP and Qlik Talend Cloud? Check out this post from my friend and mentor, Clive Bearman, on the latest developments with Agentic Data Pipelines
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Introducing Qlik Cloud Public Feature Previews for everyone
Our recently launched Public Previews give you early, hands-on access to what's coming next in Qlik Cloud, before the previewed features are generally... Show MoreOur recently launched Public Previews give you early, hands-on access to what's coming next in Qlik Cloud, before the previewed features are generally available. Try new capabilities, shape their direction with your feedback, and stay ahead of the latest features.
Public Previews are enabled by default for most users and are directly accessible from your profile:
A tenant admin needs to activate the feature preview program.
Read all about it in Introducing feature previews (Innovation Blog).
The Highlights
- Shape the roadmap: Share your feedback directly in the product and inform how Qlik Cloud evolves.
- Stay up to date: Join Public Previews on Qlik Community, where our product team will advertise the available preview features.
- You control your experience: Turn your personal preview experience on or off at any time - and as a tenant admin, manage who has access to the previews through a custom role.
Ready to know more?
Getting started is quick and easy. Here is all you need:
- Public Previews | Qlik Community
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Introducing feature previews
- Managing feature preview for your tenant
- What's New | Qlik Help
Thank you for choosing Qlik,
Qlik Support -
Action required: Qlik Automate ServiceNow connector migration to OAuth 2.0, June...
Qlik Automate's ServiceNow connector is moving from username and password authentication to OAuth 2.0. This change aligns with security best practices... Show MoreQlik Automate's ServiceNow connector is moving from username and password authentication to OAuth 2.0. This change aligns with security best practices and ServiceNow's own recommendations for third-party integrations, eliminating the need to store credentials in plain text and providing more granular, revocable access control.
When will this change be made?
The change will go into effect on the 23rd of June, 2026.
What action do I need to take?
Existing connections that use username and password authentication will need to be re-authenticated using OAuth 2.0. Complete the re-authentication steps as outlined in this blog post on the 23rd of June to prevent possible service interruptions.
How do I switch my ServiceNow connection to OAuth 2.0?
ServiceNow provides guidance on each stage of the OAuth 2.0 setup. See the following for details:
- OAuth 2.0 Overview
- Set up OAuth
- OAuth Inbound
- OAuth Inbound and Outbound authentication
- Enable OAuth with inbound REST
To migrate your Qlik Automate connector, first prepare ServiceNow by configuring OAuth 2.0, then re-authenticate Qlik Automate on the 23rd of June.
Before you begin, make sure you have Admin access to your ServiceNow instance and Admin or Owner access to your Qlik Cloud tenant and the relevant Qlik Automate automation.
- Open the Application Manager in ServiceNow:
The OAuth 2.0 plugin must be available in your ServiceNow instance.
- Log in to your ServiceNow instance.
- Click the All button in the top-left navigation bar and search for Application Manager.
- Select Application Manager from the results under Admin Center.
- In the Application Manager, use the Search your licensed applications and plugins field to search for OAuth. The ServiceNow products OAuth tile will appear in the results.
- Click the OAuth tile to expand it, then install the OAuth 2.0 plugin (Plugin ID: com.snc.platform.security.oauth). Wait for the installation to complete before proceeding.
- Navigate to Inbound Integrations:
- Click the All button again and search for Inbound Integrations.
- Select it from under System OAuth in the results.
- Create a new OAuth application endpoint:
- Click the New Integration button on the right.
- On the connection type screen, select OAuth – Authorization code grant.
- Configure the application by providing a Name, the Redirect URL, and the required scopes.
When prompted for a Redirect URL, use the callback URL provided by Qlik Automate during the connection setup. This URL must exactly match what is registered in ServiceNow.
- Save the Client ID and Client Secret; they are required for the next step.
- Click Save.
- Re-authenticate the connector in Qlik Automate
This step can only be carried out after the change on the 23rd of June, 2026.
- In Qlik Automate, add a new ServiceNow connection.
- Enter your ServiceNow instance domain (format: https://[domain].service-now.com/).
- Provide the Client ID and Client Secret previously recorded.
- Follow the on-screen authentication flow to complete the OAuth authorisation.
If you encounter issues during the migration, please contact Qlik Support or visit the Qlik Community for assistance. When raising a support case, include your ServiceNow instance domain and the error message you are seeing.
Thank you for choosing Qlik,
Qlik Support -
Qlik Datathon 2026: Empowering Future Leaders to "Think Like a Manager"
On 13 August 2026, Kristu Jayanti University successfully hosted the third edition of the Qlik Datathon 2026, themed "Think Like a Manager." This flag... Show MoreOn 13 August 2026, Kristu Jayanti University successfully hosted the third edition of the Qlik Datathon 2026, themed "Think Like a Manager." This flagship analytics competition brought together aspiring data professionals, encouraging them to apply analytics, artificial intelligence, and business thinking to solve real-world challenges.
This year's event marked a significant milestone as the first-ever inter-university Qlik Datathon, creating a larger platform for collaboration, innovation, and healthy competition among students from leading institutions.
An Event of Scale and Impact
The Datathon witnessed remarkable participation, with:
- 70 registered teams
- 274 student participants
- Teams comprising four members each
- Representation from multiple campuses and institutions specializing in Data Science, Analytics, AI, and Machine Learning
Participating institutions included:
- Kristu Jayanti University
- Christ University ( two campuses)
Learning Before Competing
The event commenced with a dedicated bootcamp designed to prepare students for the challenge ahead. The bootcamp was conducted by Qlik Educator Ambassador, Manikant Roy, Ph.D. Candidate from IIT Delhi, who provided valuable guidance and insights to help participants approach complex business problems through a data-driven lens.
The Datathon itself challenged students to move beyond technical analysis and truly" Think Like a Manager" by interpreting data, identifying business opportunities, and presenting actionable recommendations. This approach reflects the skills increasingly demanded by organizations seeking analytical professionals who can bridge the gap between data and decision-making.
Industry Collaboration at the Core
One of the highlights of the event was the strong collaboration between academia and industry. The final evaluation round was conducted by Qlik and its partner Gain Insight Solutions, ensuring that participating teams were assessed against real-world industry expectations and business standards.
Inaugural ceremony
The event was further enriched by the presence of Qlik Luminary Vivek Shah from Goldman Sachs, who served as the Chief Guest. His insights into analytics, technology, and industry applications provided students with valuable perspectives on how data-driven decision-making is transforming modern enterprises.
Vivek Shah, Qlik Luminary, from Goldman Sachs
Building the Next Generation of Data Leaders
The success of Qlik Datathon 2026 demonstrates the growing enthusiasm among students for analytics, artificial intelligence, and data-driven problem solving. More importantly, it highlights the importance of developing future professionals who can combine technical expertise with strategic business thinking.
As universities continue to embrace experiential learning opportunities, initiatives such as the Qlik Datathon play a vital role in preparing students for careers in analytics and AI, while fostering collaboration between academia and industry.
We congratulate all participating students, faculty members, judges, and organizers for making the first inter-university Qlik Datathon a resounding success and look forward to even greater impact in the years ahead.
Participating students
Winning teams along with Qlik Partner, Gain Insights team members
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Qlik MCP Just Got Smarter About Data Pipelines with Three New Lookup Tools for A...
Improving Declarative Pipeline Development If you've been building Qlik Talend Cloud pipelines declaratively with a coding agent like Anthropic's Clau... Show MoreImproving Declarative Pipeline Development
If you've been building Qlik Talend Cloud pipelines declaratively with a coding agent like Anthropic's Claude Code or OpenAI's Codex, then you already know the productivity win. And if you've coded for any length of time, then you may have encountered a friction point where your agent needed a real connection name, or a real table ID, or a real value from your tenant, and template placeholders just won't cut it. Until now, that meant tabbing out of VS Code, opening the Qlik Cloud web app, and hunting down the answer by hand.
That changes today. Qlik's MCP server has just added three new lookup tools built specifically for declarative data pipeline development, closing that gap by bringing real tenant context directly into your coding session.
New MCP Tools for Agentic Data Engineering
The three new MCP tools are as follows:
- qlik_search: Searches for Qlik catalog resources such as applications, datasets, data products, glossaries, etc. by name or content. This tool has been extended to also return pipeline projects and pipeline connections.
- qlik_get_pipeline_project_details: Returns the project’s binding values. Used to discover all of the details about a specific pipeline project
- qlik_search_connection_objects: List all source entities (tables, views) available in a data connection. Used to discover what data sources are available when configuring data pipeline tasks.
In practice, that means you can ask the tenant a question via your coding agent and get a straight answer: does this space exist, what's this connection actually called, what tables sit behind it. No more generating a pipeline against a guess and finding out it's wrong on import.
So now you can ask questions while you are developing like:
"Show me all available tables in our Sales database connection so I can decide which tables to include in the data pipeline."
And you'll receive context-aware answers as below:
Why it Matters
The benefit is simple: less time spent switching context and more time spent writing pipelines that work the first time. A data engineer working on a pipeline project can ask about spaces, connections, and table structures in natural language right in the editor and keep coding. That's a real efficiency gain for iterative development. It also directly addresses the top piece of feedback we heard during the original testing phase for declarative pipelines: many data engineers wanted a mechanism to introspect a live Qlik environment for information beyond their current pipeline project. So these tools fill that gap. They search, they describe, and they resolve. Delivering precise answers on the current Qlik environment, highlighting what is and isn't with data pipelines.
Available Now
The new tools for pipelines are live as of August 25th and work with any third-party coding agent connected to Qlik's MCP server, including Claude Code, GitHub CoPilot and Open AI's Codex. Note that if you're already building declarative pipelines and MCP is connected, then there's nothing new to set up. The tenant pipeline context is just there the next time you ask a question.
However, if you haven't tried declarative pipelines with a coding agent yet, then now's a good time to start. All the instructions for configuring your IDE, adding pipeline schemas, configuring Git Hub, and setting up MCP for coding agents can be found on the Qlik Developer portal here.
Furthermore, if you've not yet signed up for a Qlik Talend Cloud trial tenant, then you can register for free here too!
Finally, the only thing left for me to say is "Happy coding pipelines with enhanced MCP tools!". We know you're going to love it.
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Watch! Q&A with Qlik: App Development
Don't miss our previous 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. Let ou... Show MoreDon't miss our previous 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.
Let our Qlik experts offer creative solutions to your analytics demands and answer your visualization questions.
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Qlik Talend® Unlocked: See How Leading Teams Modernize Talend
You already know Talend can do more than most teams ask of it. The real question, the one every data engineering group is working through right now, i... Show MoreYou already know Talend can do more than most teams ask of it. The real question, the one every data engineering group is working through right now, is how to get there. How do you promote jobs through fully automated pipelines? How do you build test automation that holds up against real, changing data? How do you move workloads forward faster and with greater reliability?
Qlik Talend Unlocked is where we answer those questions together, alongside the teams already doing the work.
Over the past year, Qlik has invested heavily in the capabilities that make Talend a modern data engineering platform: CI/CD driven release workflows, native testing support in Talend Studio, and tooling that moves workloads forward without a rewrite. The capabilities are here today. What separates the teams getting real value from them is knowing how to architect around them.
That is what this series is built to do: help organizations modernize and optimize their enterprise data integration environments on Talend, and improve agility, scalability, and operational efficiency in the process. Every month we bring in practitioners who have put these capabilities into production, and we walk through exactly how they did it, the approaches that worked, the trade-offs they made, and the results they can measure. New episodes land monthly, covering the full modern Talend lifecycle, with practical guidance you can take straight back to your own environment.
Every episode follows the same simple format. We lead with a problem you already recognize, show real approaches that teams are running in production, and leave genuine time for your questions. No fluff, let's roll up our sleeves and tackle the subject head on! Think of it as a smart colleague showing you something useful over coffee.
The series is already underway. Episode one is available on demand right now, episode two is open for registration, and new episodes land every month. Here is what is live, and what is next.
Presented by Qlik Enterprise Architect Jan Bernhardt, this session is a working blueprint for managing the Talend software development lifecycle with real CI/CD discipline: a Git centric source of truth for metadata and job designs, the Talend Maven plugin for standardized builds and dependency management, and the Talend Management Console APIs as the control plane for promotions across environments.
Jan walks through a zero install build strategy that provisions the Talend CommandLine dynamically during the pipeline, so you stop maintaining permanent installations on build agents. He shows intelligent delta build execution that runs a git diff to find changed items, maps them to their Maven modules, resolves the dependency tree to identify master Jobs, and compiles only what actually changed.
On quality and security, the session covers automated code scanning before merge, OWASP vulnerability scanning at the package phase, and JUnit plus end to end integration tests running on dedicated UAT environments. And it lays out the full release pipeline, from scan and build through CVE check and staged promotion to UAT and production, with approval gates and an immutable Git audit trail at every step.
We are honest about the hard parts too, from corporate proxies that block Maven plugins to monolithic builds and pipeline drift, and it closes with a live GitHub Actions reference solution you can model your own pipeline on. The measured payoff: deployment cycles that once took days now run in minutes, plus the ability to mass rebuild your entire job portfolio to roll out security patches on demand.
Test automation is where most data teams have the most room to grow, regardless of platform, and it is one of the highest leverage practices you can adopt. This session shows you how to build reliable, black box tests for Talend Jobs directly in Studio. We cover a standardized test lifecycle, job designs built to be testable from the start, a clear line between what belongs in CI versus CD, and the practical Studio techniques that keep tests stable as your data and jobs evolve. Live demos run throughout, so you see the approach applied rather than described.
The result is testing that goes from hours of manual validation per release to minutes of structured, repeatable execution you can trust.
Talend Unlocked runs monthly, and the full arc is already mapped across six topics that span the modern Talend lifecycle:
Register once for the series and you are set for every session as it lands, with the newest topics reaching your inbox first. If there is a problem your team is working through, it is exactly the kind of thing a future episode is built to tackle.
Talend teams everywhere are hitting the same inflection point at the same time. CI/CD is becoming the default, test automation is moving from nice to have to expected, and migration timelines are tightening. The capabilities to meet all of it are in the platform today. What has been missing is a shared, practical view of how the best teams are putting them to work.
That is exactly what Qlik Talend Unlocked provides, and it reflects a simple commitment: Qlik is invested in Talend's future, and we want every customer to get the full value of what the platform can now do.
Everything is free for Talend Cloud and on premises customers. Start with Episode 1 on demand today, then register for the upcoming sessions so you are set for each one as it goes live. Each session runs about 45 minutes with live Q&A.
Explore the full series and register
Every date, time, and detail lives on the series landing page. If you want to register your team as a group or have questions about the content, your Customer Success Manager can help.
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The Final Whistle: Who Takes the Trophy - Model or Mastery?
The World Cup is over, Spain are champions, and the third and final episode of our Model vs. Mastery podcast series has wrapped up with a verdict on t... Show MoreThe World Cup is over, Spain are champions, and the third and final episode of our Model vs. Mastery podcast series has wrapped up with a verdict on the question we've been asking all along: when it comes to predicting football's biggest tournament, does the edge belong to the model or the human expert?
Host Adam sat down with Steve Palmer and Nick Magnuson to close out the series, and the results were more decisive than anyone on the losing end expected.
The Numbers Don't Lie
More than 400 brackets were submitted through the Choose Your Champion app over the course of the tournament, powered by Qlik Cloud Analytics. When the dust settled, the model itself finished third out of the entire field — beating the vast majority of human predictors, including some seasoned football minds.
Nick Magnuson from Qlik, who used the model to validate and stress-test his own instincts rather than override them, ended up at the top of the leaderboard. His approach was the perfect example of how to combine Model + Mastery to achieve success. He used his conviction in football based on years of playing and watching games, then he used the predict feature inside Qlik Cloud Analytics to pressure-test that conviction. In several cases, the model disagreed with him. Sometimes that sent him deeper into the data and reinforced his pick. Other times, it changed his mind entirely.
Steve's story went the other way. Sitting in seventh place heading into the semifinals, he made a deliberate choice to abandon the model and back his own instincts for the final stretch, gambling on a bigger climb up the table. It didn't pay off — the model got the last four matches right, and Steve's gut calls didn't. Even the Spain-over-Argentina final, which ran counter to the emotional favorite and the sentiment around Messi, was a call the model made confidently and correctly.
The lesson, as Nick put it, is exactly why the series exists: AI and human intelligence are natural complements, not substitutes for one another. The model on its own finished in 3rd place, Steve’s predictions finished in 7th place, and Nick, combining his expertise with the model, finished first.
Why Building a Predictive AI Model Isn't as Intimidating as It Sounds
A common misconception is that predictive modeling demands a rare combination of skills — data engineering, domain expertise, coding, and statistical know-how. That barrier is falling fast.
During the course of the World Cup, Qlik released the Predict Agent as part of the agentic experience inside Qlik Cloud Analytics. It allows users to ask in natural language what they want to predict, and the agent handles the rest: finding relevant datasets, building the model, and configuring it to run on live data. Because the underlying data already lives in Qlik, it tends to be clean and well governed from the start — the single biggest head start any machine learning project can have.
Crucially, ease of use doesn't come at the expense of oversight. The Predict Agent operates within Qlik’s full governance framework, including access control and approval workflows, so models can still get validated by data science and AI governance teams before they ever reach production. Nick also teased what's next: the agent will soon be able to construct entirely new datasets on the fly when the right data doesn't already exist, then produce ready-to-use applications, automations, and "what-if" scenarios for decision-makers to explore even further before committing resources.
Trust Is the Real Foundation
Steve raised a question that applies far beyond football: how do non-technical users — coaches, analysts, business leaders — start trusting these tools enough to actually use them? Nick's answer centered on governance and data lineage. When an agent acts on your behalf, you need to know what data it touched, where that data came from, and who has had access to it. Without that visibility, confidence in the output collapses no matter how good the model looks.
That's where features like Qlik's Trust Score come in, giving teams an objective way to measure the completeness, recency, and quality of the data feeding their models. Nick's practical advice for getting started: don't try to make all of your data AI-ready at once. Pick a specific business outcome with real sponsorship behind it, get the data for that use case in shape, and build outward from that success rather than trying to boil the ocean.
What's the Takeaway?
Neither the model nor human intuition wins this on its own. The model outperformed nearly every human predictor in the bracket, but the humans who did well — like Nick — used it as a check on their thinking rather than a replacement for it. The humans who leaned away from it, like Steve, learned that lesson the hard way.
As the series wraps, our three experts landed on the same closing advice: be curious, be willing to experiment, and don't let unfamiliarity with the technology keep you on the sidelines. Whether it's predicting a World Cup final or a business outcome with real stakes, the future belongs to people who know how to work alongside the model — not against it.
Watch the edipode 3 - Final Wistle: Who takes the trophy – Man or Mastery?
Take a tour on the new Qlik Predict Agent -
“Where is Qlik headed with AI?”
So when people ask, “Where is Qlik headed with AI?” I think there’s a better question:Where are YOU headed with AI?• Are you still focused on dashboa... Show MoreSo when people ask, “Where is Qlik headed with AI?” I think there’s a better question:
Where are YOU headed with AI?
• Are you still focused on dashboards and reporting?
• Exploring GenAI?
• Or already working with agents, MCP, conversational analytics, and AI-assisted data engineering?
Frankly, if you don’t comment, I’m going to assume one of two things: you don’t know where you’re headed yet… or you have no clue what I’m talking about.
I’ve been in analytics and data for 28 years. 15 of them at Qlik. I’ve seen enough technology shifts to know what Qlik can and cannot do.
Qlik has the pieces to meet you where you are and help you move forward.
So tell me: Where are you with AI today, and where do you want to be in the next 2–3 years?
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Qlik Answers – Fast Mode & Chat Download
In this blog post, we'll cover two new features of Qlik Answers that you may not be aware of. Fast mode Download conversation as PDF Fast Mode Qlik ... Show MoreIn this blog post, we'll cover two new features of Qlik Answers that you may not be aware of.
- Fast mode
- Download conversation as PDF
Fast Mode
Qlik Answers Fast mode delivers quick, concise responses to simple questions using your structured data. It is ideal for questions that can be answered directly, without deep analysis. For complex questions that require more analysis, use Thinking mode. And don’t worry, if you ask a question in Fast mode that requires more analysis, Qlik Answers will recommend that you switch to Thinking mode. Fast mode and Thinking mode can be used interchangeably in a single conversation.
You can learn more about Fast mode here:
Download Conversations as PDF
Now, you can download your Qlik conversations for future reference or to share with others. There are two ways you can do this. You can download a chat once Answers has responded to your question. The download button will appear as shown below. When you click it, a PDF of the Qlik Answers chat will be downloaded. The download feature is available for chats created after this feature’s release.
Qlik Answers chats can also be downloaded from the Feedback tab in an assistant, but this requires the audit admin role. To view chats, open an assistant and go to the Feedback tab. Click on the three ellipses at the end of a row and select View answer details. From here, you can review and download the chat.
Fast mode and chat downloads are both live in Qlik Answers today, and they're built to make your workflow quicker and your insights easier to share. Ask a question in Fast mode next time you need a quick answer and download a chat any time you want to keep a record or bring a colleague up to speed.
Thanks,
Jennell
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What Students Really Gain From Working With Real Data
Students Learn to Work With Ambiguity One of the biggest differences between classroom exercises and professional analytics is certainty. In an assign... Show MoreStudents Learn to Work With AmbiguityOne of the biggest differences between classroom exercises and professional analytics is certainty.In an assignment, students often know what they're supposed to find. At work, they may not.A manager might ask, “Why are sales declining?” That's not an analytics question with one obvious answer. Students need to determine which data matters, explore possible explanations, identify patterns, and decide what additional information they need.Working with real data gives students an opportunity to practice navigating that uncertainty.They learn that analytics isn't simply about finding the answer. It's about using evidence to develop a better understanding of a problem.Unexpected Findings Become Learning OpportunitiesWhat happens when the data doesn't tell the story students expected? That's where some of the best learning can happen.A surprising result can encourage students to go back to the data, question their assumptions, investigate further, and consider alternative explanations.Instead of teaching students to simply look for the “right” answer, educators can help them develop something even more valuable: the confidence to investigate when the answer isn't obvious.Students Develop Skills Employers Actually UseTechnical analytics skills matter, but employers also need people who can think critically about data.Working with real datasets can help students develop skills such as:Critical thinking — evaluating information rather than automatically accepting it.Problem-solving — figuring out what to do when the path forward isn't obvious.Data literacy — understanding where data comes from, what it represents, and what its limitations are.Communication — turning analysis into a story that other people can understand.Curiosity — asking the next question instead of stopping at the first answer.Adaptability — adjusting their approach when the data or business problem changes.These are transferable skills students can take with them regardless of the industry they enter.From Classroom Assignments to Real-World ConfidencePerhaps the biggest benefit of working with real data is confidence.Students begin to realize that they don't need to have every answer before they start exploring a dataset.They can investigate.They can make mistakes.They can ask better questions.They can change direction.And they can use data to support a decision.That experience can make the transition from classroom to workplace feel much less intimidating.Giving Students the Opportunity to Work Like AnalystsThe goal of analytics education isn't simply to teach students how to use a particular technology.It's to help them think with data.When students have opportunities to work with realistic datasets and open-ended problems, they aren't just completing another assignment. They're practicing what it means to approach a problem like an analyst.For educators, that can mean moving beyond “Here is the dataset. Find the answer.”Instead, ask:“What questions can you answer with this data?”That small shift can create a very different learning experience.And it can help students graduate with something far more valuable than knowledge of a tool: the confidence and curiosity to use data to solve problems they haven't encountered before. -
Qlik Digest - Analytics Edition - August 2026
Take Qlik Further Welcome to the August Qlik Digest. This issue is all about doing more with what you've already got. Read on for this month's best... Show More
Take Qlik Further
Welcome to the August Qlik Digest. This issue is all about doing more with what you've already got. Read on for this month's best insights and resources.
In this issue
Customer Spotlight - SCAS keeps ambulances rolling with real-time monitoring + predictive action. Now Live - Agentic AI touched down inside the Qlik Cloud apps you already use. Insider Tips - Qlik Cloud migration, minus the migraine. Tune In - Set yourself on the perfect course to modern data and analytics knowledge.Customer SpotlightHow predictive maintenance keeps critical assets running: lessons from SCAS
90 SensorsPer Vehicle92M Data PointsA MonthSouth Central Ambulance Service uses Qlik Predict with Ordnance Survey geospatial data to catch wear before it causes a breakdown, a pattern that works for any fleet, or machine your business runs on.
See How SCAS Keeps Its Fleet on the Road →Now LiveAgentic AI, Already in Your Apps
Ask questions in plain language and trigger automated next steps, without leaving Qlik. Mike Tarallo shows you how in three minutes.
Watch the Video →Insider TipsYour Migration, Easier than Ever
The Qlik Analytics Migration Tool moves your apps, users, and data into Qlik Cloud® automatically. It handles Qlik Sense apps and NPrinting reports, and now converts QlikView apps to Qlik Sense too, so nothing gets left behind.
Discover How Easy Migration Gets →
Learn It Before You Lean On It
Free, self-paced training walks through the full migration workflow, so you know what to expect before you start.
Start the Course →Tune In
