No trust,
No Agentic Analytics.

Analytics Governance is the discipline of ensuring that BI content - dashboards, reports, KPIs, semantic layers and AI generated insights - is accurate, certified, performant, and continuously monitored. It operates at the consumption layer of the data stack: the point where data becomes a decision or action. Analytics governance is distinct from data governance, which secures data at the source. Analytics governance secures what happens to that data once it reaches the business.

Data governance and data catalog vendors such as Collibra and Alation focus on data before it reaches the BI layer: quality, lineage, definitions and access at the source. Wiiisdom governs what happens next, the moment data is merged, filtered and shaped into a dashboard, a report, a KPI or an AI-generated answer. A perfectly governed dataset can still produce a wrong number on a CFO's screen, or a wrong answer from an agent. That last mile is where decisions are made, and it is the part of the stack Wiiisdom is built for. The two disciplines are complementary: Wiiisdom extends your data governance program to the point of decision.

It means two things, and Wiiisdom does both. Governance of the agents: the answers AI agents return are reconciled against your certified sources before go-live and again every time something changes, with the evidence logged. Governance by agents: Wiiisdom's own governance agents watch reports, dashboards, KPIs, semantic models and data agents continuously, detect what breaks, explain why, recommend the fix and act within the limits you set, so governance keeps pace with content and answers that change every day. Wiiisdom is the first platform built to do both, across BI and Agentic Analytics.

By reconciliation, not by opinion. Each golden question is paired with a governed reference query on your certified source, and an expected answer signed by its business owner. At test time, Wiiisdom re-executes the reference query, asks the agent the same question the way a business user would, and compares the two value by value. It also checks which source the agent used: a right number from the wrong table is still a failure. Evaluations run again every time the semantic model, the instructions or the runtime changes, and every failure comes with its root cause.

It's useful while building agents. But every platform that ships its own judge is also the party being judged. Model risk, internal audit and QA need a verdict that did not come from the system being judged. Wiiisdom doesn't build or host your agents, keeps the run owner separate from the person editing the tests, logs the question, expected answer, returned answer and verdict on every run, and covers your whole BI stack from one place.

It means managing dashboards, reports, semantic models and metric definitions with the rigor of software: changes tracked and reviewed, automated checks before every release, controlled promotion from development to test to production, and the ability to roll back to a known-good state. Wiiisdom brings that discipline to BI platforms without asking BI teams to rebuild their workflow from scratch.

Yes. A data governance program, often built on platforms like Collibra or Alation, ensures your data is clean, documented and controlled at the source. It does not validate the dashboards, reports, semantic models and agent answers built on that data, which is where most errors reach the business. Wiiisdom covers that layer: release gates before production, continuous certification in production, predictive monitoring of performance and cost, and evaluation of AI answers. Most organizations that invest in data governance discover that ungoverned BI content remains their largest source of risk.

Analytics governance is a prerequisite for reliable Agentic Analytics. AI agents and natural-language query tools consume dashboards, KPIs, and semantic models to generate answers. If those assets are uncertified or ungoverned, AI propagates errors at machine speed across every user and every query. Governing the BI layer before deploying AI is the fastest, safest path to Agentic Analytics adoption. It is also the most economical: governed BI reduces the compute cost of running AI directly on raw data.

On the technology side, Microsoft Power BI and Microsoft Fabric, Tableau (Server and Cloud) and SAP BusinessObjects, and the agents built on them, starting with Microsoft Fabric data agents, shipping December 2026, then ThoughtSpot Spotter and Snowflake Cortex, with Databricks Genie on the roadmap. On the people side, BI and analytics teams, CDAOs and governance leaders, model risk and internal audit, and analytics centers of excellence.

Wiiisdom connects to your BI platform via API. There is nothing to deploy on-premise. Most teams complete initial configuration in under a week and have live governance workflows running within two weeks. Every implementation is guided by a dedicated onboarding specialist.

Yes. Wiiisdom is used by organizations operating under some of the world's strictest regulatory requirements, in financial services, pharmaceuticals, insurance and the public sector, and by regulators themselves. Certification records, audit trails and logged agent evaluations produce the evidence auditors ask for, without manual reconstruction.

Wiiisdom is used by 600+ enterprise organizations where BI is paramount to how the business runs: enterprise reporting that must be reliable, self-service BI that must scale without losing control, and, increasingly, Agentic Analytics where trust will be decisive. These customers typically operate large-scale or multi-BI environments, face high stakes for analytical accuracy, and need to govern more assets without adding more manual effort. Wiiisdom is built for BI and Analytics teams looking for a unified governance center for all their data-driven solutions, from dashboards and reports to semantic models and AI-generated insights.