Building and Validating a Scalable Data Quality Framework
Join an exclusive peer discussion for senior data and AI leaders.
This small-group session offers a confidential space for senior professionals to exchange insights, share challenges, and learn from one another. While the session is designed exclusively for DataIQ clients, we occasionally welcome select guests who bring valuable perspectives to the conversation.
DataIQ clients: Please secure your place via the DataIQ Hub.
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Questions to be discussed:
- What frameworks and measurement approaches have proven effective for assessing data quality across multiple dimensions, and how can accountability be enforced through contracts and defined roles?
- What approaches have worked (and failed) in breaking down silos and connecting data quality across systems, domains, and governance models to ensure consistent KPIs and ownership?
- How DQ is implemented as part of the platform and automated? What is the role of AI and AI agents in scaling and speeding up the DG workflow, specifically in a Data Mesh oriented structure?
- How have organisations validated and refined data quality frameworks in practice before committing to enterprise-scale tooling?
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Accelerator format:
These are 60-minute panel discussion type events, that allow up to 10 participants to work through a set of questions from the host participant. We curate every panel to ensure the right working group is in place to provide ideas, suggestions and perspectives towards the case.
In each session you can expect:
- Prequalified group of experts in the area
- A round of introductions to each participant (up to 10 people)
- An in-depth discussion with inputs from cross-industry participants
- Concrete take-aways and action plan for moving forward
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How to apply if you are not a client:
If you are currently not subscribed to the DataIQ service, but would like to join this session, you can apply to be considered for participation by filling in the form below.
DataIQ clients: Please secure your place via the DataIQ Hub.
