For 25 years, data quality was everyone'sproblem and nobody's priority. Brendan Grady, EVP and GM of Analytics & AIat Qlik, explains why the stakes just changed.

In this episode recorded on location at QlikConnect 2026, David Sweenor and Brendan discuss consequence management, whereenterprise agentic adoption really stands ("prior to stage zero"),Qlik's Trust Score for AI, the shift from dashboards to decision intelligence,and why open standards like MCP matter in an agentic world.

For 25 years, data quality was everyone's problem and nobody's priority. Brendan Grady, EVP and GM of Analytics & AI at Qlik, explains why the stakes just changed.

In this episode recorded on location at Qlik Connect 2026, David Sweenor and Brendan discuss consequence management, where enterprise agentic adoption really stands ("prior to stage zero"), Qlik's Trust Score for AI, the shift from dashboards to decision intelligence, and why open standards like MCP matter in an agentic world.

Key takeaways:

Data quality was never fixed because there were no consequences for getting it wrong. AI agents changed that equation.
Enterprise agentic adoption is in its earliest days. Customers are experimenting, but production-grade agents are rare.
Qlik's Trust Score for AI gives decision-makers a quantifiable measure of data quality before it reaches an agent.
"Dashboards are dead" as a destination, but the data and decisions they inform are more important than ever.
Data professionals should become data product owners and trusted guides as agents take on routine work.

Chapters:

0:00 Introduction at Qlik Connect 2026

1:14 Brendan's first job: Sound of Music tourguide

2:04 Lessons from the early analytics era

3:32 Why data quality has never been fixed

4:46 Consequence management in the agentic era

6:08 Where agentic adoption actually stands

7:46 Future-proofing against LLM shifts

8:24 The analytics engine and unknown unknowns

10:29 Structured vs. unstructured data

12:04 Hallucinations and trust scores

15:30 "Dashboards are dead"

Why Bad Data Didn't Matter Until Now | Brendan Grady