Reflections on Qlik Connect 2025

Drew Clarke and James Fisher

Drew Clarke joined James Fisher on stage to make the case for going from dashbards to decisions or from data to decisions.

James took the story from there.

To get from data to outcomes, Qlik made the case that Agentic AI is best visualized as the ability to move, transform, and trust data which must precede access which then empowers the organization to analyze, decide, and act.

Crowd applause moments:

• Notably, the announcement of expansion in AWS elicited LOUD applause from the crowd.
• Similarly, the announcement of support for both structured and unstructured data.
• Multivariate time-series to provide forecasts with greater context
• Write Table: Writeback to Qlik Cloud Analytics
• Migration Tool: enabling the move to cloud (at no cost)

Crowd non-applause moments: Table recipes for data prep (seemed to go flat but got applause later during the demo)

Demo time with Dave Freschl

Dave Freschl rocked dual Dell and Apple laptops to perform demos as multiple customer personas (but that turned out to be more about presenter preferences).

• Qlik Answers UI/UX was able to draw insights from both structured and unstructured data which is vastly different experience than traditioal business intelligence UI/UX.
• During the user persona demo, context, heritage, sources, and user-friendly traceability was present in the UI/UX.
• Additionally, a feedback loop for product engineering telemetry was build into the UI/UX for faster iterations with improvements from the end users interacting and provide thumbs up / thumbs down feedback. “Let us know how we can improve”
• During the developer persona demo, a time-series and other multi-variate options were brought in from Qlik Predict.
• In effect, Qlik is providing an abstraction via no-code (WYSIWYG) with an AWS underpinning with GPUs that the developer need not be concerned with let alone be forced to configure to get things done.
• During the analyst persona demo, dashboard analysis and collaborative interactions using Write Table.
• Use of table recipe got applause this time likely because you had to see it to understand the powerful tools working together. (see above)

Reworld

Drew returned to the stage and shared the story of Reworld. Reworld wants to reduce the carbon footprint within the US. During a M&A journey with 90 facilities, Reworld use Qlik to bring together 7 financial systems.

Customer Feedback, Peer Reviews, and Magic Quadrants

Drew reiterated that customer feedback drives Qlik both directly and via customer feedback through the Qlik Partner Ambassador, Qlik MVP, and wider industry analyst communities. 

Pivoting from feedback, Drew opened the door to discussion of roadmap in three parts for getting from a stated business outcome to “Do Data Differently”

1. Data as a Product (Trust)
2. Open Data (Icerberg)
3. Agentic Experiences 

Demo time with Tim Garrod

Tim Garrod drove a demo of Qlik Talend Cloud with QVD (QlikView data) and showing a QVD as a first class citizen for data product owner personas with specific business SLAs and controls. The scenario illustrated the importance of data as a product.

Drew exlained why Upsolver and why now for Qlik. Iceberg solves problems but it is not enough by itself. Upsolver combined ETL with Data Lakes where you can query it like a Data Warehouse.

Tim came back to drive a Qlik Talend Cloud demo and discussion around how Qlik is enabling AWS consumption for ingestion performance, automated ongoing management (compaction), optimization, scale, and processing speed.

The Upsolver team found Iceberg and went all-in on it. This required really big data in terms of millions of events per second — and cloud scale ready.

Qlik needed the Lake House. Qlik needed cloud scale. Upsolver “solved” for both.

• Data ingestion (source then become Iceberg table aka zero ETL)

• Optimize Iceberg (adaptive iceberg table optimizations )

• Pipelines (iceberg live tables, declarative as gold/silver/bronze, always-on pipelines)

Qlik (and Upsolver) claims the ability to handle millions of events per second, 50% cost savings related to storage footprint, and a corresponding 5x improvement in query performance. Of course, choice and reusability is important too and a Snowflake Mirror or Databricks Mirror “External” source could be Qlik (Upsolver) under the covers through a mirror data commit.

In effect this is a Data Lake House as fast as your Data Warehouse.

Qlik Open Lakehouse goes GA in July but it is in preview as of the Qlik Connect 2025.

Tim’s next demo was Qlik Talend Data Integration to “Ask and Act” that leveraged built-in Agentic Experience to build out KPIs and create a data mart. Clippy++ quips aside… that’s pretty slick.

James returned to the stage with Drew to reiterate

Qlik Agenctic Experience + Qlik Open Lakehouse + AWS

Joining James and Drew is Mark Relph, Director of Data and Partner GTM at AWS.