Ash Pembroke, Portfolio CTO of Caylent, discusses the critical balance of data accuracy in the era of Gen AI for the benefit of boosting innovation.
Topics Include:
- Ash Pembroke, Portfolio CTO of Caylent, self-identifies as a "recovering data scientist."
- Caylent is an AWS native services company.
- Data quality remains an issue despite Gen AI.
- Contrasts legalism versus mysticism in data quality.
- Legalism: accurate data when applications need it.
- Mysticism: insights that help decision-making.
- Traditional data foundations approach is being challenged weekly.
- Gen AI developments force rethinking of solution architectures.
- Teams share solutions through giant Slack threads.
- Example: Vector databases questioned after model context protocol.
- Still do traditional data assessments, but stay flexible.
- Integration and data processing constantly get abstracted.
- Data strategy equals architecture strategy equals business strategy.
- Traditional approach: standardize data across engineering teams.
- New approach: allow business users to innovate.
- Bring valuable techniques back to the organization.
- Case study: North Sea wind turbine alerts.
- Initially seen as data quality issue, revealed new predictive failure signal.
- Gen AI enables local experimentation by business users.
- Blurring lines between enterprise enablement and software building.
- BrainBox AI case study: energy optimization across buildings.
- Architecture decisions impact ability to scale products.
- Work with business edges rather than looking for patterns.
- Gen AI can process information from these working groups.
- Think about data as a product, not asset.
- Redimensionalize dependencies across your organization.
- Now's a good time to attack data quality.
- New tools help visualize complexity across organizations.
Participants:
· Ash Pembroke – Portfolio CTO, Caylent
See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
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