Core system migrations live or die on the accuracy of data mapping and transformation rules. Data Trust parses those rules, validates the staging databases they populate, and catches mapping errors before they propagate into production data — so “we think it migrated correctly” becomes something you can show.
T-Sigma Data Trust is a staged validation pipeline for data mapping and transformation rules. It validates ETL pipelines and staging databases during core system and platform migrations, catching mapping errors before they reach production data. It is delivered through T-Sigma Knowledge Base and shares its knowledge graph with the rest of the T-Sigma Suite.
A transformation rule is a promise: this legacy field becomes that new one, and nothing is lost on the way. When the promise is wrong, the error doesn’t announce itself. It propagates quietly into the system of record, and the first person to find it is usually a customer, a reconciliation team, or an auditor.
Data Trust moves that discovery to the beginning of the process, when a mapping error is a ticket instead of an incident.
Data Trust parses data mapping and transformation rules rather than trusting a spreadsheet to describe them correctly. What the rules actually say becomes something that can be checked.
The staged validation pipeline checks the staging databases those rules populate, so a mapping error surfaces while it is still cheap to fix and before it can reach production data.
Because Data Trust runs on the Knowledge Base graph, a data mapping isn’t an orphan row in a spreadsheet. It connects to the requirements, regulations, and test history it relates to — so the answer to “why does this field map there?” doesn’t leave when the person who knew it does.
Data Trust integrates with Azure DevOps, Jira, GitLab, and GitHub, so validation findings stay attached to the tickets, stories, and repositories where the migration work is already happening.
Data Trust is delivered through the staged validation pipeline in T-Sigma Knowledge Base. It has its own page because data accuracy is its own question — one regulated institutions are asked to answer separately from how their software is tested.
Core system migrations and platform modernizations, where the accuracy of data mapping and transformation rules decides whether the migration holds.
Moving a system of record to a new platform means every transformation rule has to be right. Data Trust validates the rules and the staging data before cutover.
A staged validation pipeline checks data mapping and transformation rules end to end, so a pipeline that runs is also a pipeline that’s correct.
Data mappings stay connected to the requirements and regulations behind them, so institutional knowledge about why a field maps where it does survives team turnover.
Start with one migration, one set of transformation rules, and a staging database. See what the pipeline surfaces before cutover.
Book a Data Trust WalkthroughData Trust proves the data is right. Attest independently certifies the AI agents that act on it — behavior, safety, and governance.
The knowledge graph and staged validation pipeline Data Trust runs on — where requirements, regulations, and data mappings connect.
Once the data is trusted, Test Studio keeps automated coverage current as the migrated system keeps changing.