Which large banks lead in data management?

Which banks disclose the most about their data platforms and governance?

The 30 highest of 90 banks with any data-management evidence on their page: numbers, dated moves, use cases, officers or a named platform about data platforms, lakehouses and warehouses, data governance and quality, data strategy and modernisation, or named data vendors. Columns: the named platform or vendor (or the first data number where none is named), the data officer or named data programme, the latest dated data move, and the evidence counts behind the score.

Each row quotes the bank's page on this site; the bank link opens the full record with every source. “Not disclosed” means the page records nothing on that point. Banks absent from the table have no evidence on this topic on their page.

Which data platforms and vendors do the large banks name?

Every data platform or vendor named in the qualifying banks' records, with the banks that name it. A name is a disclosure of use, not of scale: a bank may run one workload on Snowflake or its whole estate.

How is this ranking computed?

Score = 3 × disclosed numbers whose label or value concerns data platforms, lakehouses, warehouses, governance, quality, lineage, catalogues, data strategy or a named data vendor + dated timeline moves on the same subjects (capped at 4) + use cases on the same subjects (capped at 3) + 2 × named leaders whose role is chief data officer, head of data, data and analytics, data science or data engineering (capped at two) + 2 if the bank's flagship platform is described as a data or ML platform + 1 per named data product (Databricks, Snowflake, BigQuery, Redshift, Synapse or Fabric, Palantir, Informatica, Collibra, Alation, Teradata, Cloudera; capped at 3; a bare cloud provider does not count) + data mentions among the bank's key points and section rows (capped at 2). Ties by assets, then name. Only entries already on the bank's page count; each is sourced there.

Status reads the data use cases (in production, rolling out, pilot, announced) where there are any; otherwise whether an officer is named or a move is dated.

What are the caveats?

This ranks disclosure of data work, not data quality. The banks that talk most about their data platform are often mid-modernisation; a bank that finished a warehouse migration years ago and stopped describing it scores low. Several large banks name no data vendor by policy and are understated.

"Modern data ecosystem" has no agreed definition. The page counts the elements banks themselves cite (a lakehouse or cloud warehouse, a governance programme, a chief data officer, data products) and shows each as reported; it does not score architecture.

How does this differ from the AI index?

The AI index has no data component; this page is built from the data-platform, governance and chief-data-officer entries on the bank pages, which the index counts only as generic numbers, moves and leaders. The AI index and its method are on their own page; the technology leaders and generative AI guides are hand-written cross-bank tables with their own sourcing.