VISEON: Data Assurance
Trusted Data for Everyday Workloads and AI
Know your data can be trusted, with the evidence to prove it.
Bad data rarely announces itself. It turns up in a board report, a regulatory return, or a model that has quietly drifted. VISEON Data Assurance scores your data against the nine DAMA quality dimensions, shows the evidence behind every number, and keeps watching so that problems surface when they happen rather than when audited. The result, your data is ready for AI workloads.
Assessed where your data already lives
No pipeline to build, no staging area, no copy of your data anywhere. For a warehouse or lakehouse table the checks run as SQL inside your own engine, and only the results come back. Connect Snowflake, Databricks, BigQuery, Redshift, Synapse, SQL Server, PostgreSQL, an Apache Iceberg lakehouse, parquet or Qlik apps/QVDs, cloud storage, or a spreadsheet.
Every row, not a sample
Failing-record counts are exact rather than extrapolated. When a number is challenged in a meeting, it holds up. Your entire data pipeline assured: tested, governed, and compliant.
Continuous, not a one-off audit
Between assessments, each source is watched for volume, schema and freshness changes, and an incident is raised the moment something moves. Your team hears once when an issue opens and once when it clears, not on every run.
Ask the AI Assistant in plain English
Connect an AI assistant to the platform through the Model Context Protocol (MCP) and ask what you actually want to know: which datasets are failing, what changed this week, why a score dropped.
Answers come from the platform’s own figures rather than a re-derivation, so the assistant and the screen never disagree. An assistant sees only what the person asking is allowed to see, and while it can re-run an assessment, it cannot edit a rule or a policy.
Built for governed environments
Personal data is reported as present and never stored as a value. You choose how much of a failing record is kept, and the cautious option is the default. Findings map to DAMA DMBOK, ISO/IEC 25012, ISO/IEC 29100, ISO/IEC 27701, and GDPR, and you can add your own internal framework.
Three ways to use it
Via a web portal, connected to AI-LLM via MCP, and as an extension inside Qlik Cloud. The same engine, same results, whichever your teams prefer.
See it run against your own data
Establish your position on data readiness for AI, MCP-ready governance over your private data estate from 3 days.