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 More

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 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.

fast mode.png

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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Design

“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 More

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 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 Academic Program

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 More

Students Learn to Work With Ambiguity

One 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 Opportunities

What 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 Use

Technical 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 Confidence

Perhaps 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 Analysts

The 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.

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Qlik Digest

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 Spotlight

How predictive maintenance keeps critical assets running: lessons from SCAS

90 Sensors

Per Vehicle

92M Data Points

A Month

South 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 Live

Agentic 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 Tips

Your 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

Data & Analytics Learning Voyage

Set yourself on the perfect course to modern data and analytics knowledge. On-demand Qlik Connect presentations, ready whenever you are.

Watch Now

Agentic AI and the Evolution of the Data Engineer

Today's data has to be governed, current, and ready for agents to act on. Join for a real-world conversation on agentic data engineering.

Register Now

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Qlik Digest

Qlik Digest - Data Edition - August

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 - See how Schneider Electric halved extraction costs.

Think Ahead - 5 strategies to make agentic AI your new superpower.

Insider Tips - See your full environment first: Qlik Talend Cloud® migration made easy.

Tune In - Agentic AI and the Evolution of the Data Engineer.

Customer Spotlight

How Schneider Electric stopped waiting weeks for data, and what it means for your pipelines

Costs Halved

Extraction

Near-Real Time

Data Delivery

Weeks to 1 Sprint

Turnaround

Qlik Replicate pulls straight from transaction logs, so teams in 100+ countries get trusted data in near-real time, and extraction costs dropped by half.

Get the Schneider Electric Story →

Think Ahead

Ready to Move from AI Experiments to Real Outcomes?

A practical guide to five strategies for turning agentic AI into real outcomes.

Get the eBook →

Insider Tips

Qlik Talend® Cloud Migration? Easy Mode: On

Migrating to Qlik Talend Cloud® is now easier than ever. Full visibility before you move a thing, and automated phased migration. All you bring is the decision. Then it's less time managing servers, upgrades, and infrastructure, and more time focused on actual work.

Get the Migration Toolkit →

Enable Yourself to a Faster Migration

Free, self-paced lessons walk through the full migration workflow, so you know what to expect before you start.

Start the Course →

Tune In

Reliable Data Pipelines, Built to Last

Learn how to automate black-box testing in Talend Studio, so releases move faster with fewer surprises.

Register Now

Agentic AI and the Evolution of the Data Engineer

Today's data has to be governed, current, and ready for agents to act on. Join for a real-world conversation on agentic data engineering.

Register Now

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Support Updates

Security Statement: npm Supply Chain Compromise, Qlik Package Impact

Initial Publication: August 4, 2026Edit August 5, 19:10 UTC: Impact section updated.Edit August 6, 06:11 UTC: Formatting changes only.Edit August 6, 0... Show More

Initial Publication: August 4, 2026
Edit August 5, 19:10 UTC: Impact section updated.
Edit August 6, 06:11 UTC: Formatting changes only.
Edit August 6, 06:35 UTC: Investigation concluded, removed banner.

Certain open source packages released by Qlik® have been impacted by an ongoing, industry-wide npm supply chain compromise ("Shai-Hulud") affecting keyv, cacheable, and related packages. As of this writing, the campaign has affected many organizations beyond Qlik, including over 440 unique packages and 2,200+ package versions across the npm ecosystem. A Qlik npm publishing credential was harvested from an internal build system after it installed a package with a poisoned transitive dependency, and was used to publish unauthorized, malicious versions of several Qlik-owned open source packages to the public npm registry. This appears to be a consequence of the broader worm's automated spread across the npm ecosystem, rather than a targeted attack on Qlik. The investigation into full scope and impact remains active, and Qlik has taken active steps to mitigate the impact.

Impact

We have confirmed that no Qlik products, including Qlik Cloud, Talend Cloud, and our client-managed offerings, were impacted.
No compromised code was released into any Qlik product.

31 packages in the @qlik npm scope had malicious versions published to the registry, all published and detected within the same 10:46 to 10:53 UTC window on August 4, 2026:

Package	Version
@qlik/api	2.14.2
@qlik/browserslist-config	3.0.2
@qlik/carbon-core	2.1.1
@qlik/carboncopy	1.1.6
@qlik/design-tokens	1.3.13
@qlik/dts-bundler	2.0.3
@qlik/embed-react	2.5.3
@qlik/embed-runtime	1.6.4
@qlik/embed-svelte	1.1.4
@qlik/embed-web-components	1.7.3
@qlik/eslint-config	2.0.20
@qlik/eslint-config-base	0.1.1
@qlik/eslint-config-react	0.1.1
@qlik/eslint-config-svelte	0.1.1
@qlik/eslint-config-vue	0.1.1
@qlik/nebula-table-utils	2.6.9
@qlik/oxfmt-config	0.1.6
@qlik/oxlint-config	0.7.2
@qlik/prettier-config	1.0.3
@qlik/react-native-simple-grid	1.5.5
@qlik/runtime-module-loader	1.5.1
@qlik/sdk	0.28.1
@qlik/sprout-design-docs	1.0.2
@qlik/sprout-gesture	0.0.13
@qlik/sprout-icons	0.12.3
@qlik/sprout-react	6.45.3
@qlik/sprout-react-table	0.16.7
@qlik/tsconfig	1.0.3
qlik-chart-modules	1.1.1
qlik-modifiers	0.10.1
qlik-object-conversion	0.17.2

What Qlik has done

Revoked the compromised npm publishing credential and disabled automated publishing pipelines (GitHub Actions), including across the qlik-oss organization
Confirmed that npm removed the malicious versions from the public registry; purging internal mirrors/caches
Added precautionary blocks on our cloud firewalls
Rotating exposed secrets and keys, in a controlled sequence, after confirming affected hosts are clean
Completed indicator-of-compromise sweeps across related GitHub organizations; no indicators of persistence found
Continuing a full investigation across our repositories and build infrastructure

What you should do

If you installed any of the affected package versions listed above:

Treat any npm, GitHub, or cloud credentials present in that environment as potentially compromised
Scan for compromise before rotating credentials; do not rotate first
Reinstall using npm install --ignore-scripts once a clean version is confirmed available

More information

For independent tracking of the scale of this campaign across the npm ecosystem, see Socket's write-up: keyv and cacheable compromise.

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4205

Qlik Learning

Harness the power of AI with this brand-new certification!

Not sure where to begin or what you need to know to take the exam? We’ve got you covered! We created this How do I take a Qlik Certification exam? vid... Show More

Not sure where to begin or what you need to know to take the exam? We’ve got you covered! We created this How do I take a Qlik Certification exam? video that explains the exam process as well as the Qlik AI Specialist Certification Exam Details path to help you on your way to becoming certified.

Certification exam details
Duration: 120 minutes
Number of questions: 50
Passing score: 73%

This fifty question certification exam covers topics such as:

Introduction to Artificial Intelligence
Business applications for Artificial Intelligence
Fundamentals of Qlik Answers
Fundamentals of Qlik Machine Learning
Fundamentals of Insight Advisor

Preparation

To prepare for this exam, you can take the Qlik AI Certification Exam Preparation course.

Badge

After passing this certification exam, you are awarded the Qlik AI Specialist Certification badge. To learn more about the criteria for earning this badge, visit our Credly badging page.

Ready to take the exam? Log into Qlik Learning and get started today!

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1138

Product Innovation

SAP Say "You Better Stop" using ODP-RFC - Here's What Qlik Customers Need to Do

In 1979, The Clash covered a track called "Wrong 'Em Boyo" — a London Calling deep cut built around a simple warning: "You better stop." They were sin... Show More

In 1979, The Clash covered a track called "Wrong 'Em Boyo" — a London Calling deep cut built around a simple warning: "You better stop." They were singing about the perils of gambling, not SAP data pipelines. But if you're still running ODP-RFC without a migration plan, those three words should be ringing true right now.

What Changed, and When

For years, the ODP-RFC interface has been a widely used method for extracting data from SAP systems into analytics platforms, data warehouses, and lakehouses. It worked well. Many organizations built their entire data pipeline architecture around it - and that includes a significant number of Qlik customers globally.

In February 2024, SAP updated SAP Note 3255746, making it explicit that ODP-RFC is intended exclusively for data transfer between SAP applications. Use by third-party tools to extract data from systems running on-premise or in private-cloud setups with components like SAP BW, SAP BW/4HANA, or PI_BASIS was classified as unpermitted. We believe customers continued to use ODP-RFC because it was effective in their SAP data replication processes and it was functionality that SAP was already using internally for SAP-to-SAP data transfers.

SAP is offering a temporary revert option that allows customers to roll back to unrestricted ODP-RFC access - but that window closes in December 2026, and SAP is explicit that doing so is at the customer's own risk. The deadline is real. The clock is running.

Which Qlik Products Are Affected

Not everything is impacted equally. The two products most directly affected by the June patch are:

Qlik Replicate - SAP ODP Endpoint
Qlik Talend Cloud - SAP ODP Connector

If you have tasks running on either of these using the ODP-RFC interface, those pipelines will fail after applying the patch, unless you migrate to an alternative connection path first.

SAP has published a self-assessment utility (SAP Note 3439624) that will flag any ODP-RFC calls from Qlik connectors as "unpermitted." It's worth running it now to understand your exposure within your environment before doing anything else.

Other Qlik products that include ODP functionality - including Qlik Analytics (Qlik Sense / QlikView) and Talend Studio - are also in scope, though the immediate operational impact varies by use case. For Talend Studio customers specifically, a separate Qlik Community article covers what you need to know.

Qlik Gold Client does not use the ODP-RFC interface and is unaffected.

What You Can Do Today

Qlik supports several alternative connection paths that don't rely on ODP-RFC, the right one for your organisation depends on your SAP landscape, performance requirements, and timeline.

ODP-OData is fully supported by both Qlik Replicate and Qlik Talend Cloud today. It works, and migrating to it is the most straightforward near-term path for many customers. One trade-off worth knowing: OData is HTTP-based, which can mean lower throughput compared to ODP-RFC in high-volume scenarios. For many use cases that's acceptable. For others, it's a consideration worth modelling before you commit.

Beyond OData, Qlik supports a range of other SAP-native connection methods - SAP Extractors, database-level CDC, and more - depending on your system configuration. There is no single right answer, which is why we'd strongly encourage any affected customer to reach out to your Qlik representative before applying the June patch, so we can map the right migration path together.

For the full technical detail on your options, the Qlik Community Support Update is the definitive reference.

Something New on the Horizon

Qlik is constantly adapting to change for the benefit of our customers. The product team is heads-down actively developing a new SAP connector - an alternative to both ODP and OData - targeting general availability in late Q3 2026.

This new endpoint is designed to be fully compliant with SAP's runtime license requirements while offering CDC capability for SAP HANA environments. It will be made available in both Qlik Replicate and Qlik Talend Cloud, and for customers on S/4HANA, it's shaping up to be a meaningful addition to the toolkit.

We'd rather show you something finished than promise you a roadmap. But for those of you navigating the ODP transition and wondering what comes next: something is coming.

We will be making this new endpoint available through a Private Preview in early September. If you would like to be considered for early access, please complete the following form:

Private Preview Registration (SAP HANA RFC Endpoint)

What to Do Next

If you're using ODP-RFC today, start here:

Run SAP's self-assessment utility (SAP Note 3439624) to understand your current exposure
Audit your Qlik tasks - identify which rely on the SAP ODP endpoint across your Qlik products
Do not apply the June patch without a plan - Talk to your Qlik representative first, there's no upside in cutting your pipelines off without a plan in place
Watch this space - more is coming on Qlik's new SAP connector before the end of Q3

While you have to stop using ODP-RFC. You don’t have to stop using Qlik with SAP, and you don't have to navigate this one alone.

For the full technical detail, visit the Qlik Community Support Update on SAP ODP-RFC Changes, the Talend Studio-specific guidance, and Qlik's SAP integration documentation on help.qlik.com.

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Support Updates

Upcoming QlikView component deprecation: QlikView OCX, Plugin for Cloud, Source ...

As Qlik moves to further modernize its existing product line, the following legacy QlikView components will be deprecated and are no longer included i... Show More

As Qlik moves to further modernize its existing product line, the following legacy QlikView components will be deprecated and are no longer included in the upcoming QlikView September 2026 release:

QlikView OCX: The QlikView OCX Component will no longer be maintained, and no further releases will be made available as of the September 2026 release.
QlikView IE Plugin for Cloud: While connecting to QlikView documents in Qlik Cloud will continue to function as expected with previous IE Plugins for Cloud, no new versions of the client will be released.
The IE Plugin for On-Premise installations will remain supported and will be included in the September 2026 release.
QlikView Source Control: Connecting QlikView Desktop to a Source Control system will no longer be supported or updated as of the September 2026 release.
QlikView WorkBench: QlikView Workbench, used in Visual Studio to build QlikView integrations, will no longer be supported or updated as of the September 2026 release.

What about previous versions?

Older supported releases of the listed features (such as QlikView September 2025) remain fully operational and will be maintained in accordance with Qlik's Release Management Policy.

We're happy to assist with any follow-up questions. Reply to this blog post or take your queries to our Support Chat.

And if you're ready to migrate to Qlik Cloud, get started with Migrating from QlikView, or contact your Qlik account team to arrange a more hands-on consultation.

Thank you for choosing Qlik,
Qlik Support

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601

Product Innovation

Introducing the Next Evolution of Agentic Analytics in Qlik

We are extending our agentic architecture with new analytics agents and capabilities that deepen AI reasoning, broaden access, and drive more effectiv... Show More

We are extending our agentic architecture with new analytics agents and capabilities that deepen AI reasoning, broaden access, and drive more effective action.

This launch builds on the agentic experience we introduced earlier this year and continues our vision of Qlik as the trusted intelligence layer for AI, helping you move beyond analytics consumption toward a system where AI reasons, predicts, acts, and assists—grounded in trusted data and powered by the Qlik analytics engine.

Here is what’s new.

Drive Action from Insight with Automate Agent

With the new Automate Agent, you can now use natural language to trigger actions and workflows directly from your analytics experience.

The agent executes workflows across Qlik and downstream systems based on user requests or AI-driven recommendations. It determines the appropriate automation, prompts confirmation when needed, and executes tasks end-to-end.

Instead of stopping at an insight, you can move immediately to execution, helping your teams respond faster and close the loop between analytics and outcomes.

See What’s Likely to Happen with Predict Agent

Our new Predict Agent lets you ask forward-looking questions in plain language and generate predictions without requiring data science expertise.

Built on Qlik Predict, the agent guides you through the full data science lifecycle from understanding the problem, building and validating models, to generating predictions and interpreting results—with full explainability.

Make it easier for more people to use predictive analytics to make proactive and better-informed decisions.

Screenshot 2026-06-24 at 5.32.37 AM.png

Get Faster, Richer Answers from Qlik Answers

We are introducing several new improvements to your Qlik Answers that improve speed, expand input types, and increase control over AI interpretation.

Fast Mode:

Get faster answers for more simple questions.

Fast mode will deliver concise, low-latency responses for quick exploring and faster-paced workflows while still benefiting from the accuracy and trust of the Qlik analytics engine.

Screenshot 2026-06-09 at 12.08.51 PM.png

Multimodal Capability:

Coming soon

Qlik Answers will be able to read images, charts, and documents.

By understanding and reasoning over visual content, Qlik Answers will unlock insights from a wider range of business materials, reducing manual effort of interpreting non-text data.

Semantic Customization:

Coming soon

Provide context for your data to guide Qlik Answers.

Customize semantic information by adding definitions for fields and master items directly within the logical model, to give Qlik Answers a better understanding of what your data means. This creates a transparent and correctable process to review, edit, and enrich business definitions and context to prevent misinterpretations before they influence answers or impact users.

Unlock powerful insight through MCP with new advanced reasoning tools

Coming soon

New engine-driven advanced reasoning MCP tools deliver mathematically rigorous analytics directly to AI agents and assistants. These capabilities extend analysis beyond basic querying and enable advanced decision intelligence driven by Qlik’s analytics engine.

Analyze the Gray: Discover meaningful patterns hidden in excluded data and understand what may be missing from the current view

Prove Causality: Estimate the causal effect of one variable on another and evaluate alternative scenarios

Optimize Decisions: Determine the best allocation of resources under real-world constraints

Simulate Risk: Explore thousands of possible outcomes and understand probabilities and confidence ranges

Predict Outcomes: Train and deploy machine learning models directly against governed Qlik data

Explore Alternatives: Re-run history under different conditions to understand the impact of actions not taken

The next frontier in AI won’t be determined by which LLM you choose, but by how much analytical power you have under the hood. These capabilities will enable assistants and agents to reason more effectively using governed data and engine-driven analytics that drive more informed, explainable, and actionable decisions.

The Next Step in Agentic Analytics

With this June release, you can now go further with AI across every stage of decision-making. This helps transform analytics into trusted intelligence your teams can rely on.

Get started and learn more:

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5549

Community News

Some Mid-Year Updates!

Introducing Qlik Locals We are excited to introduce Qlik Locals, the new name for our meetup groups. As part of this update, meetup.com has officia... Show More

Introducing Qlik Locals

We are excited to introduce Qlik Locals, the new name for our meetup groups.

As part of this update, meetup.com has officially been retired as our user groups platform, and all local meetup activity has moved directly into the Qlik Community. You can now find Qlik Locals under the Events tab in the Community navigation. This makes it easier to discover local events and connect with members in your area, all without leaving the Community.

There are now 35+ local groups available to join. Each group’s captains share event announcements, posts local updates, and helps keep conversations going between events. You can also RSVP to events directly within each group on the Community platform.

If you were previously a member of your local group on Meetups, be sure to join its new home as Qlik Locals. Moving forward, all local events and announcements will be shared there.

Join your local group today so you don’t miss any upcoming events or announcements!

Choose Your Champion (World Cup Predictions Campaigns)

We launched a Choose Your Champion experience where members could submit their own World Cup predictions and see how they compare to Qlik Predict’s own predictions. It was a fun way to put your picks to the test and see how they stacked up against the model.

After making predictions, members could head over to the discussion tab to compare picks with other Community members, debate matchups, and share thoughts as the competition unfolded.

Did you participate? Join the discussion and let’s hear your reactions!

Three Questions. Three Pairs of Qlik Sneakers.

Qlik’s new “Make Your Data Work for AI” campaign tells the story of a fictional shoe retailer navigating a sudden viral demand spike through the perspectives of three different people: the person who built the data foundation, the person who scaled the insight, and the person who had to act before the opportunity disappeared.

To continue the conversation, we’ve created three Community discussion threads that will open over the next few weeks, each of them inspired by one of those key moments. Is it better to link directly?

As a bonus, each discussion thread comes with the chance to receive a pair of exclusive Qlik sneakers. Here is how it works:

Three discussion threads, three pairs of Qlik sneakers, and one winner per thread.

Every comment on a thread is one entry to win that thread’s pair of sneakers.

A random drawing happens at the end of each week.

Whether you have a story to share or simply want to join the discussion, we would love for you to jump in and be a part of the conversation!

Just In Case You Missed It… Qlik Digest is Back!

Qlik Digest is back. Every month it will bring you the insights, product knowledge, and real-world stories that keep you one step ahead, because staying current shouldn't feel like hard work.

Read the Analytics Issue here.

Qlik Learning Voyage

Our Qlik Learning Voyage is a six-week, on-demand webinar series that explores practical strategies for building trusted data, improving decision-making, and putting AI to work across analytics and operations.

Since it’s available on demand, you can start it anytime that works best for you.

Whitepaper: The Evolution of the Data Engineer

Modern data engineering is AI powered and engineer led. This whitepaper explores how data engineers are helping organizations build trusted, AI-ready data foundations.

On-Demand Webinar: Introducing Agentic AI That’s Built for the Builders

Catch up on this on-demand webinar to explore six new AI capabilities now generally available in Qlik and start applying agentic data engineering to your team’s workflows.

Watch the webinar: Introducing Agentic AI that's Built for the Builders 

Thank you for continuing to make the Qlik Community a place where members can connect, learn, and help one another succeed. Whether you are joining a local group, participating in discussions, or exploring new resources, we appreciate everything you do to make this Community what it is.

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Design

Select Visible Points on a Map in Qlik Cloud using Spatial Indexes

If you have worked with point maps in Qlik, you are probably familiar with how selections normally work. You zoom into an area, use the lasso tool to ... Show More

If you have worked with point maps in Qlik, you are probably familiar with how selections normally work. You zoom into an area, use the lasso tool to draw around the points you want, confirm the selection, and repeat the process if you want to explore somewhere else.

Qlik Cloud now supports another option for point layers called Auto select visible:
Instead of manually selecting points, you move around the map and Qlik updates the selection based on the area you are viewing. Zoom into Philadelphia and the locations in that area are selected. Pan over to another city and the selection changes with the map.

There is a little more happening behind the scenes, though. Before you turn on Auto select visible, you first need to create a Spatial Index for your location data. In this post, I’ll walk through what that means and how you can try it yourself with a simple example.

What changed?

The idea of selecting visible locations is not completely new to Qlik. Auto Select Visible existed in the GeoAnalytics map extension before it became available in the native Qlik Cloud map.

What changed recently is that you can now use this behavior directly in the native Qlik map chart. You no longer need the old GeoAnalytics map extension, but you still use GeoOperations to create the Spatial Index that the map relies on.

The easiest way to think about the workflow is that there are two parts. During reload, GeoOperations prepares the Spatial Index and adds it to your data model. Once the app is loaded, the native Qlik map uses that index as the user pans and zooms.

What is a Spatial Index?

A Spatial Index is a way of organizing geographic data so Qlik has a more efficient way to understand where points are located.

Imagine a grid placed over your map. Each location belongs to one or more cells in that grid, depending on the level of detail. Qlik creates several levels of these cells so it has smaller cells for detailed views and larger cells for wider geographic views.

When GeoOperations creates the index, you will see fields such as SpatialIndex, SpatialIndexLevel2, SpatialIndexLevel3, and so on. These values represent cells in the spatial grid.

This is useful because Qlik does not need to treat every map movement as a completely new geographic calculation across all of your points. It already has a spatial structure that tells it how those locations are grouped geographically.

Why does this need to happen in the load script?

The reason is that the map setting and the Spatial Index have different jobs. The Spatial Index prepares your location data during reload. "Auto select visible" then uses that prepared information while interacting with the map.

So the Spatial Index is created once when the app reloads instead of being rebuilt every time somebody pans or zooms the map.

GeoOperations connection

You need GeoOperations to create the Spatial Index, but you do not have to build the script manually.

Screenshot 2026-08-07 105002.png

If you create a Qlik GeoOperations connection, you can select Spatial Index from the available operations, choose your fields, and let Qlik generate the script for you. This is probably the easiest option if you are using the feature for the first time.

You can also write the GeoOperations ScriptEval call directly in the Data load editor.

Screenshot 2026-08-07 105356.png

Try it with a simple example

Attached to the post is a QVF that uses a demo dataset containing SiteID, SiteName, Metro, State, Latitude, Longitude, MonthlyRevenue, and Customers.

Step 1: Load the locations

First, upload the CSV into DataFiles in your Qlik Cloud app or space. Then load it using the following script:

Sites:
LOAD
    SiteID,
    SiteName,
    Metro,
    State,
    Num#(Latitude, '0.000000', '.', ',') as Latitude,
    Num#(Longitude, '0.000000', '.', ',') as Longitude,
    Num#(MonthlyRevenue, '0.00', '.', ',') as MonthlyRevenue,
    Num#(Customers, '0', '.', ',') as Customers,
    GeoMakePoint(
        Num#(Latitude, '0.000000', '.', ','),
        Num#(Longitude, '0.000000', '.', ',')
    ) as SitePoint
FROM [lib://DataFiles/qlik_spatial_index_demo_20k.csv]
(txt, codepage is 28591, embedded labels, delimiter is ',', msq);

The important part here is SitePoint. Our CSV stores latitude and longitude separately, so GeoMakePoint combines them into the geographic point field we will use on the map and pass to GeoOperations.

Step 2: Create the Spatial Index

Once the Sites table is loaded, we can pass it through the GeoOperations Spatial Index operation.


[SiteSpatialIndex]:
LOAD *
EXTENSION GeoOperations.ScriptEval('
    SELECT
        SiteID,
        SpatialIndex,
        SpatialIndexLevel2,
        SpatialIndexLevel3,
        SpatialIndexLevel4,
        SpatialIndexLevel5,
        SpatialIndexLevel6
    FROM SpatialIndex(
        gridSize="0.002",
        gridWidthHeightRatio="1.5",
        nLevels="6",
        levelFactor="4"
    )
    DATASOURCE dataset INTABLE
        keyField="SiteID",
        pointField="SitePoint",
        crs="auto"
', Sites);

The two fields that matter most here are keyField and pointField. SiteID connects the generated index back to the original Sites table, while SitePoint tells GeoOperations which geographic field should be indexed.

For this example, I am also using a gridSize of 0.002. I originally tested the default value of 0.04, but at a detailed street-level zoom it grouped too many nearby locations into the same grid cell. Reducing it to a lower number gave the demo much more precise selections when zooming into individual points.

After the reload, your data model will contain the original Sites table and the SiteSpatialIndex table, associated through SiteID.

Step 3: Create the map

Now add a native Map chart and create a Point layer. Use SiteID as the dimension and SitePoint as the location field.

Once the point layer is working, go into Map settings and change Zoom behavior from Auto zoom to Auto select visible. A Spatial index option will appear underneath it. Select SpatialIndex.

Screenshot 2026-08-07 103850.png
Screenshot 2026-08-07 103859.png

At this point, start zooming and panning around the map. Qlik will update the selection as the visible area changes.

Auto zoom versus Auto select visible

It is also worth understanding how this differs from the default Auto zoom behavior.

With Auto zoom, you make a selection somewhere in the app and the map moves to show the selected locations. Auto select visible works in the opposite direction. You move the map yourself, and the map view drives the selection.

Why might I see more selected sites than visible points?

Spatial Index selections work with grid cells, not individual screen pixels. If several locations fall inside the same spatial index cell, those sites can all become associated with the selection even when only one point appears inside a tightly zoomed map view.

That is where gridSize becomes important. A larger grid size creates larger cells, which is useful when users normally work at wider geographic views. A smaller grid creates more detailed cells and gives you more precise behavior when users zoom down to individual streets or locations.

I attached the sample CSV and complete load script so you can try the same setup in your own Qlik Cloud tenant.

Thank you for reading!

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Product Innovation

Connect All the Things: Qlik Talend Cloud's Latest Connector Releases - and a Mi...

Back in 1985, Tears for Fears sang "Everybody Wants to Rule the World." They weren't thinking about data integration pipelines - but they might as wel... Show More

Back in 1985, Tears for Fears sang "Everybody Wants to Rule the World." They weren't thinking about data integration pipelines - but they might as well have been. Because if you're a data engineer or an analytics leader in 2026, it’s not world domination you are after; it's domination of your data world: you want all your data, wherever it lives, moving wherever it needs to go.

This round of latest connector releases takes us a meaningful step closer to that.

What's New

We’ve been hard at work natively integrating Stitch's full SaaS connector catalogue into Qlik Talend Cloud, giving you access to 100+ pre-built connectors for popular sources including Salesforce, Jira, Marketo, Zendesk, and more.

Since May, we have released twelve more Stitch-powered connectors in Qlik Talend Cloud, some new and some improved, covering a broad sweep of the modern SaaS stack. Whether your data lives in a DevOps pipeline, an e-commerce platform, a marketing automation tool, or a customer engagement suite, there's something here for you.

The additional Stitch-powered connectors are:

Circle CI - continuous integration and delivery pipelines
NetSuite - ERP and financial management
SendGrid - email delivery and engagement
Shopify - e-commerce platform
Contentful - headless CMS
Delighted - customer experience and NPS
Iterable - cross-channel marketing
Branch - mobile attribution and linking
Segment - customer data platform
Microsoft Graph - Microsoft 365 data and services
Microsoft Teams - collaboration and communications
Sendwithus - transactional email

That's a wide net. From pulling CI/CD telemetry out of Circle CI to surfacing customer sentiment from Delighted, to unifying your Microsoft 365 data through MS Graph - the range reflects just how distributed the modern data landscape has become. Remember the saying “there’s an app for that”. Well, think of Qlik Talend Cloud when you want to move data from 'that app'.

The Milestone That Actually Matters

Here's where it gets interesting - and I'd argue this is the more important story with this update.

July 1^st is an important milestone in Qlik Talend Cloud. This update is not just about extending the connector list. It also completes something we’ve been working on for a while. Because now All of the connectors, including every previous Stitch-originated SaaS connector that is available in Qlik Talend Cloud today and in the future, will support the full range of data movement use cases: Replication, Pipelines, and Qlik Open Lakehouse.

Previously, Stitch-based connectors were limited to the Replication use case. That was fine as far as it went, as that was exactly the use case Stitch was built for - but it left a gap in today's modern world. If you wanted to route data into a pipeline or land it directly into an open lakehouse architecture with iceberg tables, these connectors weren't available for the job.

That gap is now bridged.

What does that mean in practice? It means that whatever architectural path you're on - whether you're running straightforward replication into a warehouse, building out event-driven pipelines, or building a modern data architecture on open formats like Iceberg and open standards with Qlik Open Lakehouse - your Stitch-powered connectors are ready to travel the whole journey with you. No asterisks. No "supported for this use case only."

Why Stitch Connectors in Qlik Talend Cloud?

A quick word on the "under the hood" picture, for anyone coming to this fresh.

Stitch is a SaaS-native data ingestion technology from Qlik’s Talend acquisition, purpose-built for cloud-based source connectivity. The connectors delivered through this program are taking the best of Stitch technology and making it better in Qlik Talend Cloud, designed to connect to the APIs of modern SaaS applications - the kind of tools that power marketing, sales, operations, development, and finance teams - and bring that data into your data movement infrastructure securely, reliably and at scale.

The connectors are delivered continuously in the cloud, so you don't have to wait for a software release cycle to access new sources. When a new connector ships, it's available in your source and targets list when building your projects. These twelve are the latest in a steady cadence that's been building the breadth of Qlik's SaaS connectivity for some time now.

What's Next

The cadence continues. For a complete list of connectors, check out the Connector Factory on Qlik.com and our documentation in Qlik Help. These sites will be continuously updated as new connectors are released.

If you're looking for a specific connector that isn't listed on the Qlik Connector Factory, then you can request it by clicking the request button on the bottom of that page.

The best place to track what's coming - and to register your ideas to help shape Qlik - is the Qlik Community Ideation Forum.

If you're ready to start moving data from any of these latest new connectors, the Qlik Talend Cloud documentation has everything you need to get set up.

Qlik Help: Qlik Talend Cloud Connectors →

Not tried Qlik Talend Cloud yet? You should. Try for free here

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Support Updates

Upcoming Maintenance for Qlik Cloud - AWS – Europe (Ireland): August 2026

Qlik Cloud tenants hosted in the Europe (Ireland) region will undergo scheduled maintenance in August 2026 to upgrade the database supporting the unde... Show More

Qlik Cloud tenants hosted in the Europe (Ireland) region will undergo scheduled maintenance in August 2026 to upgrade the database supporting the underlying authentication service. The upgrade strengthens our ability to deliver a secure and reliable experience.

The maintenance window is expected to last 30 minutes.

Other regions are unaffected.

What is the expected impact?

If you are already signed in: You can continue working as usual.

If you need to sign out or sign in: Logging in and out are temporarily unavailable during the maintenance window. If you attempt to do so, you may encounter the following message:

{"errors":[{"title":"Unexpected server error","code":"UNEXPECTED","status":"500"}]}

This is expected and does not indicate a problem with your account.

What do I need to do to prepare?

No action is required.

What follow-up actions are required?

None.

When will the maintenance take place?

The maintenance is scheduled for:

Region	Maintenance Start	Maintenance End
Europe (Ireland) (eu-w-1)	Wednesday 12 August 2026 21:30 IST (Ireland) UTC: 12/08/26 – 20:30	Wednesday 12 August 2026 22:00 IST (Ireland) UTC: 12/08/26 – 21:00

To track further updates during the scheduled Qlik Cloud Maintenance, please visit our Qlik Cloud Status page. This blog post will be updated with additional information where necessary.

Thank you for choosing Qlik,
Qlik Support

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Product Innovation

Qlik Open Lakehouse on Google Cloud Platform

Private Preview · Google Cloud Qlik Open Lakehouse comes to Google Cloud Sign up for the Private Preview and get hands-on with real-time Iceberg ta... Show More

Private Preview · Google Cloud

Qlik Open Lakehouse comes to Google Cloud

Sign up for the Private Preview and get hands-on with real-time Iceberg tables in your own Google Cloud project — no lock-in, no re-architecting.

Sign up for the Private Preview!

If you're running analytics on Google Cloud, you already know the tension. BigQuery is fast and familiar, but the more data you pour into it, the more your ingestion and storage bill climbs, and the more your data ends up shaped around one engine. You want the performance without the lock-in, you want your data to stay yours, and you want to free up some budget for more valuable workloads.

That's exactly the problem Qlik Open Lakehouse solves — and we're getting ready to launch a Private Preview on Google Cloud this September that you can be part of.

Qlik Open Lakehouse gives you a fully managed way to ingest real-time data straight into Apache Iceberg tables that live in your own Google Cloud Storage buckets. No proprietary formats, no copies, no re-architecting your data layer. Query those same tables from BigQuery, Trino, or any Iceberg-compatible engine, and pay a fraction of what you'd normally spend moving and storing that data.

If you've followed our work on AWS, this will feel familiar — and that's the point. We're porting a proven architecture to Google Cloud, not building a science experiment.

How it works: a walk through the architecture

Open Lakehouse builds the bronze layer of your medallion architecture, ingesting and landing real-time data as optimized Apache Iceberg tables, with the heavy lifting executed in your own Google Cloud project. From there, push-down transformations in Qlik Talend® Cloud pipelines create your silver and gold layers directly on BigQuery. Here's the path your data takes, stage by stage.

	1 · Ingestion at the edge Your sources land through two paths. For change data capture and bulk loads from databases, SAP, and mainframe systems, the Qlik Data Movement Gateway sits in your managed environment and handles CDC and bulk ingestion. For streaming and event data, you connect Kafka topics or Google Cloud Storage directly. Both routes feed the landing stage without your data ever leaving your control. Later in the Private Preview, we'll also add support for Qlik Replicate as a source, so if you're already a Replicate customer, you can onboard data from your existing CDC sources into Iceberg without migrating a single pipeline or switching tools.
	2 · Compute in your project Two components run on Google Compute Engine inside your environment. The Network Integration Agent runs on a standard Compute Engine VM and brokers secure connectivity between the Qlik Talend Cloud tenant and your resources. The Open Lakehouse Cluster — the engine that writes and optimizes your Iceberg tables — runs on a Compute Engine managed instance group built on Spot VMs. That Spot-based design is deliberate: it's where a large part of the cost savings comes from, because you're running the ingestion and optimization workload on Google Cloud's most cost-efficient compute instead of always-on, premium-priced infrastructure.
	3 · Open storage you own Everything lands in Google Cloud Storage, split across a landing bucket for incoming data, a config bucket for pipeline metadata, and table storage where your data comes to rest as Apache Iceberg tables. These buckets are in your account. The data is written in an open table format from the start, so there's never a moment where it's trapped in something only Qlik can read.
	4 · A catalog any engine can use Iceberg tables are registered in the Google Lakehouse runtime catalog, so your tables are discoverable and governable through Google Cloud's native catalog rather than a Qlik-proprietary one. That's what makes zero-copy querying real: BigQuery reads the same tables through ELT, and so can Trino or any other Iceberg-compatible engine, all pointing at one physical copy of the data.
	5 · A managed bronze layer, ready to build on The pipeline moves through managed stages — landing, storage, and mirror — to produce clean, governed Iceberg tables without you hand-coding each hop. You get Type 1 and Type 2 slowly changing dimensions or append-only patterns out of the box, so history and current-state tables are handled for you. We'll also add zero-copy mirroring to BigQuery during the Private Preview, so you can surface your Iceberg data straight in BigQuery. If you want to replace an existing native table, you can, since downstream queries keep working exactly as before.

The result: real-time data flows from your sources into governed, query-ready Iceberg tables in your own Google Cloud Storage, optimized continuously, queryable from BigQuery and beyond — and you never gave up control of a single byte.

Built on proven AWS foundations

We didn't start from zero for Google Cloud. Qlik Open Lakehouse first shipped on AWS, where it's already running these same patterns: CDC and streaming ingestion, Spot-based compute for cost-efficient optimization, Iceberg tables in customer-owned object storage, and open querying across engines.

Bringing it to Google Cloud is a deliberate step toward feature parity across clouds. The architecture mirrors what's proven on AWS, adapted to Google Cloud's building blocks: Compute Engine in place of EC2, Google Cloud Storage in place of S3, BigQuery and the Google Lakehouse runtime catalog as the native query and catalog layer. Same engine, same principles, GCP-native execution.

This matters because of what it says about our direction. We believe you should be free to choose where your data lives, which engine queries it, and to change your mind later without a migration project. Delivering the same open lakehouse capability across AWS and Google Cloud, with more to come, is how we make that freedom real rather than a slide in a pitch deck.

Why it matters

Everything above maps to three things we care about:

TRUST

Build governed data products in Qlik Talend Cloud on top of your Iceberg tables. Your data stays in open Apache Iceberg format, in your own account.

FREEDOM

Query from BigQuery, Trino, or any Iceberg-compatible engine, and swap or add engines later without re-architecting. No lock-in to a single vendor or format.

COST OPTIMIZATION

Spot VM compute and zero-copy, single-storage architecture mean you spend far less to move and hold your data at scale.

Join the Private Preview

We're accepting participants now, and we're prioritizing organizations already standardized on Google Cloud and working with BigQuery or Apache Iceberg in their accounts.

Request your spot in the Private Preview →

Opt in through the form and the Qlik team will reach out to schedule a welcome call. Already signed up? Sit tight — we'll be in touch.

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Japan

【9/10（木）- 11（金）開催】Snowflake World Tour Tokyo に出展します！

Qlik は、Blue Square Partner として、ブースとシアターセッションで参加します！エンタープライズ AI 戦略において、Snowflake 上のデータ基盤整備は急務となっています。本展示では、基幹システムからのリアルタイムのデータパイプライン構築、データ抽出コストを最適化でき... Show More

Qlik は、Blue Square Partner として、ブースとシアターセッションで参加します！

エンタープライズ AI 戦略において、Snowflake 上のデータ基盤整備は急務となっています。本展示では、基幹システムからのリアルタイムのデータパイプライン構築、データ抽出コストを最適化できる Iceberg レイクハウス、分析とデータエンジニアリングをシームレスにつなぐ MCP サーバーをご紹介します。統合からインサイト創出まであらゆるデータから価値を生む AI 時代の最新アプローチをご体験いただけます。

ぜひ、Qlik ブース（Blue Square ＃25）・Qlik シアターセッションにご来場ください！

今すぐ申し込む

Qlik シアターセッション概要

9/10（木）シアターB 15:35-15:50

AI エージェントに Qlik のすべてを－ MCP Server で広がる Snowflake 活用

Qlik MCP Server は、Qlik のすべてを AI エージェントに解放する仕組みです。Snowflake Native App を通じて、CoWork や Cortex Agents から自然言語でのアプリ探索やダッシュボード操作、Declarative Pipelines では Snowflake へのパイプライン構築を実現します。分析とデータエンジニアリングを 1 つの AI の入り口でつなぐ新しい形を紹介します。

クリックテック・ジャパン株式会社
ソリューション技術部シニアソリューションアーキテクト
阿部智師

9/11（金）シアターB 14:35-14:50

Iceberg レイクハウスが変える、エンタープライズデータ統合・活用のコスト常識

SAP やメインフレームなど複雑な基幹システムからのCDC によるリアルタイム統合、ノーコードでのデータ品質・変換ルール適用、AI SQL Assistant によるクエリ生成、Trust Score とリネージで裏付けられた信頼できる AI-Ready なデータ基盤構築をデモでご紹介します。加えて、Iceberg レイクハウスが実現する大量データの抽出におけるコストの最適化、2.5 倍のデータ鮮度、Snowflake へのゼロコピー連携もご紹介。コスト常識を塗り替えます。

クリックテック・ジャパン株式会社
ソリューション技術部プリンシパルソリューションアーキテクト
中嶋翔

今すぐ申し込む

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Product Innovation

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 More

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 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

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Explore Qlik Gallery

Recruiting Assistant

Recruiting Assistant iA American Warranty Group This Qlik Sense application helps internal recruiters efficiently review and identify qualified ... Show More

Recruiting Assistant

This Qlik Sense application helps internal recruiters efficiently review and identify qualified job applicants. It analyzes resume content and displays a word cloud of key skills, qualifications, and terms, allowing recruiters to quickly spot candidates with relevant experience. When a candidate is selected, their resume is displayed directly within the application for detailed review. Used by the recruiting team, the app reduces manual screening time, improves candidate discovery, and enables faster, more informed hiring decisions.

Discoveries

Best-fit candidates faster: Recruiters can quickly identify applicants whose resumes include the most relevant keywords for a specific role. Skill gaps in the applicant pool: The app may reveal when certain required skills or qualifications are missing or uncommon across candidates. Reduced manual screening effort: By narrowing candidate review through keyword patterns, the app helps recruiters spend more time evaluating strong matches instead of scanning every resume manually.

Impact

The application significantly reduces the time recruiters spend manually reviewing resumes by quickly highlighting candidates with relevant skills and experience. It improves the quality and consistency of candidate screening, helps uncover qualified applicants who might otherwise be overlooked, and enables faster hiring decisions. By providing immediate visibility into resume content and key qualifications, the app increases recruiting efficiency and supports better talent acquisition outcomes.

Audience

HR and hiring managers

Data and advanced analytics

The app has reduced the time recruiters spend screening resumes by quickly identifying candidates with the most relevant skills and experience. This has enabled faster, more consistent candidate evaluations and helped uncover qualified applicants who may have been overlooked during manual reviews.

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Support Updates

Qlik Stitch: Facebook Ads Insights API enablement effective August 6th, 2026

Effective August 6th, 2026, the following breakdowns need to be enabled for Ad Accounts using the Stitch Facebook Ads connector: breakdowns=impressio... Show More

Effective August 6th, 2026, the following breakdowns need to be enabled for Ad Accounts using the Stitch Facebook Ads connector:

breakdowns=impression_device (and any combination including impression_device)
breakdowns=hourly_stats_aggregated_by_audience_time_zone
breakdowns=frequency_value

This change is driven by Facebook. For details, see Ads Insights API -- Breakdown Availability Changes | developers.facebook.com.

Thank you for choosing Qlik,
Qlik Support

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Japan

【8/25（火）ブース, 9/11（金）オンライン講演】小売未来 Days 2026 に出展します！

Qlik は、ブースおよびオンライン講演に参加します！小売未来 Days 2026は、「未来のヒントが集まる、小売・流通業界の最前線を学ぶ」をテーマに開催する、業界最大級のイベントです。業界のトップランナーや実務家、専門家が登壇し、小売・流通業界の多様な課題に対し、DX、リテールメディア、EC・... Show More

Qlik は、ブースおよびオンライン講演に参加します！

小売未来 Days 2026は、「未来のヒントが集まる、小売・流通業界の最前線を学ぶ」をテーマに開催する、業界最大級のイベントです。業界のトップランナーや実務家、専門家が登壇し、小売・流通業界の多様な課題に対し、DX、リテールメディア、EC・OMO、物流改革、需要予測、AI活用など、未来の成⻑に欠かせないテーマを網羅的に解説します。

今すぐ申し込む

8/25（火）ブース出展 11:00 - 18:30
　大崎ブライトコアホール（東京都品川区北品川5丁目5-1 大崎ブライトコア3F）

9/11（金）オンライン講演 14:20 - 14:40
サッポログループのデータ基盤構築とQlik採用の背景

今すぐ申し込む

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Business

Recent Blog Posts

How predictive maintenance keeps critical assets running: lessons from SCAS

Agentic AI, Already in Your Apps

Your Migration, Easier than Ever

Learn It Before You Lean On It

Data & Analytics Learning Voyage

Agentic AI and the Evolution of the Data Engineer

How Schneider Electric stopped waiting weeks for data, and what it means for your pipelines

Ready to Move from AI Experiments to Real Outcomes?

Qlik Talend® Cloud Migration? Easy Mode: On

Enable Yourself to a Faster Migration

Reliable Data Pipelines, Built to Last

Agentic AI and the Evolution of the Data Engineer

Impact

What Qlik has done

What you should do

More information

What about previous versions?

What is the expected impact?

What do I need to do to prepare?

What follow-up actions are required?

When will the maintenance take place?

Qlik Open Lakehouse comes to Google Cloud

How it works: a walk through the architecture

1 · Ingestion at the edge

2 · Compute in your project

3 · Open storage you own

4 · A catalog any engine can use

5 · A managed bronze layer, ready to build on

Built on proven AWS foundations

Why it matters

Join the Private Preview

Qlik は、Blue Square Partner として、ブースとシアターセッションで参加します！

Discoveries

Impact

Audience

Data and advanced analytics

Qlik は、ブースおよびオンライン講演に参加します！