We have recently completed a migration of our core analytical data warehouse from a legacy on-premise system (Teradata) to Claw, a modern cloud data platform. The technical migration of the ETL pipelines, using a combination of Airflow for orchestration and custom Spark jobs for transformation, was successful by all standard metrics. The data has been validated: row counts align, checksums on key fact tables match, and our standard suite of data quality tests in dbt passes without error.
However, we are encountering a subtle and critical issue. The business intelligence and data science teams, who rely heavily on Claw's integrated AI/ML features for automated insight generation and anomaly detection, are reporting that the conclusions drawn from this migrated data are demonstrably incorrect. For example, a model flagging "unusual regional sales drops" is now triggering on regions with perfectly normal, seasonally-adjusted historical patterns. The underlying aggregated sales numbers are correct when queried directly, but the AI's interpretation of them is flawed. This suggests a disconnect between the raw data and the feature vectors or statistical baselines the AI system is using.
Our current hypothesis centers on how the AI system may have ingested or interpreted the historical data during the migration window. We are investigating:
* **Temporal Context:** The legacy system stored dates in a proprietary ordinal format, which we transformed to standard `DATE` types. Could the AI be interpreting the entire migrated history as a single, recent event, losing all historical seasonality?
* **Data Distribution Shifts:** While row-level values are preserved, the physical clustering and ordering of data in Parquet files on Claw's object storage is entirely different. Could the AI's sampling mechanism for baseline training be skewed by this new physical layout?
* **Metadata Mismatch:** We migrated table schemas and data, but not necessarily auxiliary objects like statistics, primary/foreign key constraints (which were not enforced in the legacy system), or column histograms. The AI may rely on such metadata for efficient feature engineering.
Has anyone else navigated a similar "data is correct but derived insights are not" scenario post-migration, particularly into a platform with baked-in AI? We are looking for methodological approaches to debug this layer of the stack.
Our immediate next steps are to isolate a single AI-generated insight and manually trace back the features used, but any guidance on auditing the interaction between stored data and an opaque AI service within a platform like Claw would be invaluable. Specifically:
* How can we validate the *input features* being fed to the AI models, not just the source data?
* Are there known pitfalls regarding timestamp interpretation or data ordering during bulk historical loads?
* Should we consider "re-training" or resetting the AI's understanding of baseline metrics after a full historical load?
— hannah
Data is the new oil – but only if refined
Check if Claw's AI is using migrated data to retrain its models. Some systems treat new data as a baseline shift and retrain automatically, which can break seasonality detection. I've seen this happen after a warehouse migration where date timestamps had a new format, causing the model to misinterpret the time series.
Your data quality checks are good for content but not for context. The AI might be seeing slightly different statistical distributions because of a changed NULL handling default or a new column order in the source data.
metrics not myths