How to Evaluate an Enterprise AI Data Analyst Before Rollout

A polished demo can prove that a system works once. A rollout evaluation must show how it behaves repeatedly, for different users, after knowledge changes, and when the evidence is incomplete.

What should an enterprise AI data analyst evaluation measure?

Use six dimensions:

1. Answer validity: Is the conclusion supported by the source data and the intended interpretation?
2. Evidence quality: Can a reviewer trace claims to queries, rows, pages, tables, figures, and definitions?
3. Governance: Does each user see only the sources and rows they are allowed to access?
4. Behavior under uncertainty: Does the system clarify, abstain, or surface conflict instead of guessing?
5. Reproducibility: Can the team explain an answer after definitions or source versions change?
6. Operational readiness: Are latency, queues, cancellation, failures, feedback, and audit history manageable?

Do not combine these into one headline score too early. A system can score well on average while failing the single permission case that prevents deployment.

Step 1: Define the decision and risk boundary

Write down what the pilot is allowed to prove. Examples:

• employees can answer recurring regional performance questions without an analyst writing SQL;
• reviewers can find policy evidence in a controlled document collection;
• finance can reconcile a calculated variance with the explanation in a report;
• where DataFact AI Reports Preview is available, an AI-generated report can be reviewed and downloaded from a defined spreadsheet workflow after the task completes.

Also define what remains out of scope: write operations, automated decisions, unsupported sources, or regulated use without human review. A narrow boundary makes failures interpretable.

Step 2: Build a question set from real work

Collect questions from support queues, analyst requests, recurring reports, and review meetings. Remove sensitive content before using any shared Demo or vendor environment.

Balance the set across difficulty and failure modes:

Question class	Example purpose
Direct lookup	Checks basic retrieval or filtering
Aggregation	Checks metric, grouping, and time logic
Multi-table	Checks relationships and grain
Document table	Checks layout and header preservation
Figure or chart	Checks visual evidence retrieval
Ambiguous request	Checks clarification behavior
Access-sensitive	Checks table, row, or document scope
Cross-source	Checks document/database corroboration
Unsupported	Checks refusal or uncertainty behavior
Historical rerun	Checks definition and version reproducibility

DataFact includes a Test Set workflow. Its public documentation says a Test Set stores plain-text questions that can later be submitted together to a selected Agent. The current Test Set object does not include an expected-answer or scoring-rule field, and creating a set does not run it. Maintain your adjudication rubric separately.

Step 3: Define evidence before running the test

For each question, specify how a reviewer will judge the result:

• accepted business interpretation;
• authoritative metric definition or policy version;
• source tables, filters, and expected access scope;
• source document, page, and required qualifier;
• calculation or query used for independent verification;
• acceptable clarification or abstention behavior.

This prevents the evaluation team from changing the standard after seeing a persuasive answer.

Step 4: Run by user role, not only by question

Permissions are part of correctness. Run access-sensitive questions through accounts representing the roles that will use the system.

DataFact’s public documentation notes that users in the same workspace can see different datasets, schemas, tables, controls, and returned rows. A successful connection test does not prove that every user receives the same data. Your evaluation should therefore include:

• same question, different row scopes;
• a user who lacks a required table;
• a user who can see the answer but not the underlying document;
• removal of access after a citation has been created;
• an empty result caused by scope rather than absence of source data.

Record both overexposure and unexplained underexposure.

Step 5: Score the whole answer, not just the prose

For database analysis, inspect:

• question interpretation;
• generated SQL;
• selected tables and joins;
• metric definitions and filters;
• result rows and calculations;
• user-specific access clauses;
• written conclusion and chart labels.

For document analysis, inspect:

• selected document and version;
• page and region;
• table, figure, or passage structure;
• omitted qualifiers;
• how the answer represents conflicting or insufficient evidence.

DataFact’s evidence guide emphasizes that a citation helps inspection but does not prove the answer is fair or complete. The evaluator must still decide whether a specific source supports a specific claim.

Step 6: Track failure severity

Use a severity model that reflects business risk:

• Critical: unauthorized data exposure, invented source, materially wrong answer presented without uncertainty.
• High: wrong metric, join, period, policy version, or document that could change a decision.
• Medium: incomplete answer, weak citation, missed clarification, or misleading chart label.
• Low: wording, formatting, minor latency, or a recoverable interaction issue.

Also track the outcome type: correct, correct after clarification, safe refusal, unhelpful refusal, incomplete, or wrong. “Answered” is not the same as “succeeded.”

Step 7: Test change and rerun behavior

Enterprise knowledge changes. Update a metric definition, replace a policy, alter source access, or correct a document chunk. Then rerun the relevant subset.

Ask:

• Can the new answer be explained from the change?
• Can an earlier result still be reconstructed under its original definitions?
• Is the active version clear?
• Do citations resolve after source updates?
• Are unrelated questions stable?

DataFact states that builds of an analysis model freeze version snapshots, enabling comparison and rollback of definitions. Validate the exact lifecycle in your intended deployment.

Step 8: Evaluate operations and human effort

Quality includes the work required to keep the system useful. Measure:

• time to connect and scope a source;
• effort to maintain definitions and examples;
• time to review a query or citation;
• queue time and end-to-end response time;
• ability to cancel or resume requests;
• clarity of parsing, indexing, and generation status;
• time to diagnose a failed request;
• usefulness of feedback and audit history.

The public DataFact Demo is a read-only sample space that exposes Live Q&A, finished AI Reports, and Feedback Tuning. It is useful for learning the interaction model, but it is not a substitute for a pilot on your data and roles.

A rollout gate template

Set explicit thresholds before the pilot starts. For example:

Gate	Example acceptance rule
Access	Zero unauthorized source or row exposure
Material correctness	Zero unresolved critical errors
Evidence	Every material claim has inspectable support or is marked as interpretation
Ambiguity	High-risk ambiguous questions clarify or safely abstain
Reproducibility	Selected historical cases can be explained after a definition change
Operations	Owners can diagnose and recover from common failures

The exact thresholds belong to your risk owner. Do not copy a generic percentage into a high-stakes workflow.

Frequently asked questions

How many questions should an AI analytics pilot include?

Use enough questions to cover the real intents, user roles, source types, and failure modes in scope—not an arbitrary benchmark size. Start with a focused, adjudicated set and expand it whenever evaluation or production use reveals a new failure class.

Should we use expected answers?

Yes, where an authoritative answer can be calculated or cited. Also record the accepted interpretation, source, and review method. DataFact’s current Test Set stores questions only, so keep expected answers and scoring rules in your evaluation process.

Is SQL execution accuracy enough?

No. A query can execute and still use the wrong metric, join, grain, period, or access scope. Review semantic and governance correctness as well as the returned value.

How should citations be scored?

Score whether the citation opens, points to the right source location, contains the necessary context, and supports the exact claim. Do not score citation presence as proof of correctness.

When is a pilot ready to expand?

Expand when critical risks are closed, owners can explain failures, evaluation is repeatable, permissions work by role, and the maintenance process is sustainable. Continue monitoring after rollout because sources, definitions, and user behavior change.

Make the evaluation reusable

The best pilot artifact is not the demo recording. It is the reusable question set, evidence standard, role matrix, failure taxonomy, and rerun process that will test every future change.

Explore the DataFact public Demo, review the evaluation documentation, or read the governed text-to-SQL guide before designing your pilot.