AI success starts with clean data, not just better models

Summary

Kraken’s data transformation work argues that successful AI depends on clean, unified, accessible data rather than model quality alone. Serving more than 90 million customer accounts across 27 countries, the platform uses Databricks to distribute data securely and at scale, while clients need documentation, join logic and business context to make it useful. Unification reduces the analyst bottleneck, builds trust in shared numbers and enables self-service analytics, including conversational querying through Databricks Genie. The discussion also describes metadata as a live model input: Unity Catalog and Delta Sharing let Kraken share context alongside data instead of relegating it to PDFs or separate web pages. Reported client examples include call-center dashboards updated every few hours with predictive models and faster tariff experimentation, while organizations with stronger data skills and culture are positioned to adopt agentic AI more quickly.

Context

Kraken’s clients face low-quality, siloed data, fragmented systems, undocumented fields and unclear join logic. These problems consume data teams’ time, undermine trust in shared numbers, block self-service analytics and make it difficult for AI systems to interpret enterprise data. The work is motivated by reducing the time clients need to use Kraken’s data and increasing its business value.

Approach / What changed

Kraken uses Databricks as its internal data platform and provides secure, scalable data distribution to clients. The approach combines unified, accessible data with documentation, metadata, business context and ownership. Unity Catalog and Delta Sharing allow context to be shared alongside data, while tools such as Databricks Genie support conversational access and self-service analytics.

Takeaways

• Low-quality, siloed data is described as the biggest blocker to extracting value from other investments; unification enables analytics, AI and faster decision-making at scale.
• Metadata needs to become a live input for AI rather than a PDF or separate reference page. Unity Catalog and Delta Sharing let Kraken share context alongside the data.
• A client moved from painful monthly call-volume reporting to dashboards updated every few hours with a predictive model, while clean data also supported faster tariff test-and-learn cycles.

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