Criterion #3:

Data normalization and AI-ready data

AI doesn’t fail because of algorithms — it fails because of data.

In most environments, machine data is noisy, inconsistent, and fragmented across sources.
Different formats, missing context, and inconsistent naming all make it harder to analyze — and even harder to trust.A modern data architecture should turn that raw telemetry into something usable: clean, structured, and enriched with the context teams (and AI) need to act on it.How does your data architecture stack up?

Normalize data at scale
Is data structured consistently across sources?

Reduce manual preparation
How much effort goes into preparing data before analysis?

Support AI-ready pipelines
Can your architecture support automation and advanced analytics?

We're really in the early stages of our AI journey. Splunk is the central data point for all of our data collection and integration from end-to-end enabling all of the machine data to come together. It has been and will continue to be part of our strategic plan and is very important to us as an organization."

Patricia Voight, CISO, Webster Bank and Webster Financial