MyDot Consultancy

Featured image: AI-generated conceptual illustration. Sources checked 26 October 2026.

An analytics agent can produce a convincing explanation of a table that answers the wrong question. The property, dates, dimensions, metrics and filters determine what the report means. Make those choices visible before the narrative, so a business reviewer can inspect the query rather than trusting a polished summary.

This workflow is a read-only reporting assistant. It translates a business question into a reviewable query, retrieves the data, runs explicit calculations and drafts an interpretation with limitations. It should not change tracking configuration, campaign budgets or business targets as part of reporting.

Turn the request into a query contract

Imagine a hypothetical marketing manager asking how organic acquisition performed last month. That request leaves several choices open: which property, which definition of acquisition, which metric and which comparison period? The agent should resolve those choices rather than silently picking familiar defaults.

Write the contract in ordinary language and in the exact technical fields used by the API. Identify the property, date range, timezone context, dimensions, metrics, filters and ordering. If the manager intends a particular channel definition, preserve it explicitly.

Google’s Data API reporting guide describes reports built from dimensions and metrics, with filtering and pagination. Those mechanics make the query inspectable. They do not decide which measure best represents the manager’s business objective.

Use metric definitions rather than familiar labels

Users, sessions, events and key events describe different things. A request for traffic should not automatically become whichever metric the agent remembers first. Link the selected fields to the official dimensions and metrics reference and record any custom definitions used by the organization.

If a metric has changed meaning in the business’s reporting practice, document the transition. Do not compare a new definition with an old dashboard value as though they were identical. The query contract should expose that mismatch before the agent writes a growth story.

For rates and averages, specify the denominator and calculation method. Do not casually average row-level rates or sum unique-user counts across overlapping groups. Use the appropriate aggregation and explain what the result represents.

Retrieve completely enough for the claim

Handle pagination and row limits deliberately. If the report contains only the first page of results, do not describe it as the complete distribution. Preserve the query and response metadata with the result so an analyst can reproduce or diagnose it later.

Check the date window. A partial current period is not directly comparable with a completed prior period without explanation. When data is still arriving or a source is unavailable, label the result preliminary or incomplete according to the actual condition.

The agent should distinguish an empty result from a failed request. No matching rows may be a valid outcome of a filter. An authorization error or incompatible query is a technical limitation. Those conditions should not all become “performance was zero.”

Inspect the reporting caveats before interpreting

Google’s reporting-data expectations guide discusses sampling, approximate unique counts and thresholding. The workflow should inspect relevant response metadata and carry material limitations into the report instead of presenting every number as exact.

If a privacy threshold suppresses a small row, absence is not necessarily zero activity. If a result is sampled, the report should not hide that fact. The significance of the limitation depends on the question, but the agent should not decide it is irrelevant without a stated basis.

When the API and interface disagree, compare definitions, filters, dates and reporting behavior before alleging a data error. Save the evidence needed for an analyst to investigate. A narrative explanation is not a substitute for reconciling the query settings.

Calculate first, explain second

Use code or a trusted analytical tool for differences, rates and totals. Keep the formula and source values available. The model can explain the result, but it should not improvise arithmetic over a long text response and then hide the calculation.

Separate observation from interpretation. “The selected metric declined in this cohort” is an observation. “The campaign caused the decline” is a causal claim requiring additional evidence. The reporting assistant should propose follow-up questions when the data cannot establish the cause.

For the hypothetical organic-acquisition report, a change could reflect audience behavior, measurement configuration or the chosen comparison window. The agent can list those possibilities without selecting a convenient explanation as fact.

Use a report format that exposes assumptions

• The business question and approved query contract.
• The resulting table or chart with units and period labels.
• The calculation definitions and comparison basis.
• Relevant sampling, thresholding or coverage limitations.
• A small set of supported observations.
• Open questions and proposed follow-up analysis, clearly labeled.

Keep detailed technical fields in an appendix or expandable section if the audience does not need them in the main narrative. The important point is that they remain accessible. A concise executive summary can still be auditable.

Test the translation from question to query

Use ambiguous requests, partial periods, empty filters, paginated results and metadata indicating limitations. Ask an analyst to inspect both the query and the written answer. A correct interpretation of the wrong query should fail the evaluation.

Also test a request outside the user’s property access. The agent should report the access boundary, not switch to another property with a similar name. Read-only reporting still requires identity and scope controls.

Measure whether the workflow improves analysis

Track query corrections, unsupported claims and analyst review effort. Costs include API requests, repeated exploration, model processing and maintaining metric definitions. A fixed recurring report may be better served by a conventional dashboard; an agent is more useful when questions vary and the query needs explanation.

A trustworthy GA4 reporting assistant shows its work at the level that matters: data source, query, calculation and limitation. Once those are visible, the narrative can help a manager understand the result instead of distracting them from how it was produced.

Sources and further reading

Google Analytics: Create a report. Report structure, filtering and pagination; checked October 26, 2026.

Google Analytics: Dimensions and metrics. Field definitions for query construction; checked October 26, 2026.

Google Analytics: Reporting data expectations. Sampling, approximation and thresholding caveats; checked October 26, 2026.