AI analytics lead: a role-scoped agent
Metric definition, dashboard review, insight extraction, anomaly explanation, and PII-aware summaries — read-only against your own warehouse.
The AI analytics lead role in OperativeOps is a role-scoped agent for analytics work. It queries the warehouse and BI tools you grant it, explains anomalies and drafts SQL, and is read-only by design — it cannot write to your data.
What the analytics agent does
- Defines and documents business metrics — naming conventions, calculation logic, and data lineage for every KPI your team tracks.
- Reviews dashboards for accuracy, consistency, and comprehensibility — flagging metric definitions that differ across tools.
- Extracts insights from data in your warehouse, surfacing trends, segment differences, and leading indicators.
- Explains anomalies in plain language — an MQL drop, a revenue spike, an unexplained churn cohort.
- Produces a weekly metrics digest for leadership — top-line health, leading indicators, and exceptions to watch.
- Generates PII-aware summaries — aggregates before including any customer-level data in a report.
Sample interactions
Our monthly active users dropped 12% this month. What should I look at?
Anomaly breakdown — 12% MAU drop. First, segment by acquisition cohort: is the drop uniform, or concentrated in a specific acquisition period (which would point at onboarding or activation)? Second, check usage by feature — a decline in one high-frequency feature often explains a broad MAU fall. Third, compare by geography and plan type: a concentrated drop points at a product change, a pricing change, or an outage in that segment. Fourth, check your event tracking — has any instrumentation changed this month? A 12% drop is material enough that data integrity should be ruled out before the business explanation. Send me the cohort breakdown and I will help you interpret it.
We have three different definitions of 'active user' across Snowflake, Looker, and our BI tool. How do we fix this?
This is a metric governance problem, not a technical one — and it is extremely common. Recommended approach: (1) convene a metric definition meeting with Product, Engineering, and Analytics; the goal is one agreed definition. (2) Document the winning definition in a metrics catalogue — include the formula, the grain, and excluded cases. (3) Implement it as a dbt model, so there is a single source of truth all downstream tools read from. (4) Deprecate the divergent queries with a note pointing at the canonical model. Usually two weeks of focused work. Want me to draft the catalogue template?
Can you summarise last quarter's performance for the board without exposing individual customer data?
Board performance summary — Q1 2026 (all figures aggregated, no individual customer records). Revenue: €[X], up [Y]% QoQ. Net revenue retention: [Z]%. New customers: [N], of which [N] in DACH. Gross churn: [C]%. Leading indicators: expansion pipeline €[X], NPS [score], support ticket volume down [%]. I have excluded all individual-level data; if you need segment breakdowns by country or plan, I can produce them with a ≥10-customer minimum group size to avoid re-identification. Note: this summary draws only on aggregate CRM and billing data — no personal data is included.
Integrations
Queries your warehouse for metric computation and anomaly analysis, respecting your column masking policies.
Reads event and product data for funnel analysis, cohort breakdowns, and usage trend reports.
Reads LookML models, dashboard definitions, and metric logic to check consistency and correctness.
Reads dashboard question SQL and flags divergent metric definitions across your BI tooling.
Posts weekly metrics digests, anomaly alerts, and data quality flags to the channels you choose.
Reads dbt model definitions and documentation to trace metric lineage and find upstream data quality issues.
The permission boundary
What it can read: the schemas, tables and BI models you expose — no more. Access is per-session and scoped to what you define, so a table outside the grant is not merely discouraged, it is unreachable. Column-level masking policies in Snowflake or BigQuery are enforced by the warehouse, and the agent inherits them rather than working around them.
What it can do: query, explain and draft. Metric definitions, anomaly diagnoses, cohort breakdowns, SQL with comments your engineers can review before deploying, and leadership digests. Numbers come from your sources — the agent reads them rather than estimating, and where a metric is missing from the connected data it says so instead of approximating. Every query and output goes into the append-only audit ledger.
What it is prevented from doing: writing. No INSERT, no UPDATE, no schema change, no modification of any model — read-only by design, so your data engineering team keeps full control of the infrastructure. It is also prevented from surfacing individual-level records in outputs shared beyond the requesting user; aggregates use minimum group sizes so re-identification is not possible from the report.
Frequently asked questions
What data does the analytics agent see?
Only what you explicitly connect and authorise. OperativeOps is self-hosted, so queries run within your own network perimeter. Access is not persistent — the agent queries on demand, per session, within the scope you define, and you control which tables and schemas are reachable at all.
How does it handle PII?
It works with aggregated data and does not surface individual-level records in any output shared beyond the requesting user. Column-level masking policies configured in Snowflake or BigQuery are enforced by the warehouse itself. Where an analysis would require personal data, the agent flags that and asks for confirmation rather than proceeding.
Does it have hallucination guards on numbers?
It does not generate numbers — it reads them from your connected sources. It does not estimate, infer, or fabricate metric values, and if a metric is absent from the connected data it says so rather than approximating. Numeric outputs carry the source query or dashboard that produced them.
Where does the analysis run?
Inside your infrastructure. The agent issues queries to your warehouse within your own network and results never leave it, unless you have configured an external model provider — with a local model backend, nothing leaves at all. Raw data is not retained between sessions.
Can it write SQL?
Yes — as text for a human to review. It drafts queries for metric computation, ad-hoc analysis, and dashboard creation, with comments explaining the logic, which your data engineers review, modify, and deploy. It executes SELECT only; it cannot run a write query.
Does it support an air-gapped data warehouse?
Yes. In a self-hosted deployment with an on-premises model backend, the agent can query an air-gapped Snowflake, a BigQuery equivalent, or any JDBC-compatible warehouse inside your private network. No query results or schema metadata leave the perimeter. Get in touch for an integration architecture review.