BI evaluation checklist: metrics, permissions, refresh and cost

What should a BI evaluation prove?

A useful business intelligence evaluation proves that the intended people can answer an agreed business question using the right data, see only what they are permitted to see and understand how fresh the result is. It should also identify who maintains the model and what the complete service costs. Choose those acceptance conditions before comparing dashboard appearance. The exercises in this guide are proposed checks for your own evaluation, not completed product tests.

1. Define the decision and the audience

Start with a decision such as investigating delayed orders, explaining a change in subscriptions or reviewing department spending. Name the people who create models, build reports, explore data and only view results. Include external customers if analytics will appear inside an application. These roles can require different commercial entitlements. A small authoring team does not establish the cost or access rights of a large viewing audience. Explore the business intelligence directory once the audience is clear.

2. Write a metric contract

Give each shortlisted product the same definition of one important metric. Record the grain of the data, the date field and time zone, inclusion and exclusion rules, currency treatment, handling of late corrections and an approving owner. For a proposed order-count exercise, include a cancelled order, a duplicate line item and an order that crosses the reporting boundary. Decide the expected result before the demonstration. A correct-looking total is insufficient if two analysts used different definitions. Looker vs Tableau explores model ownership and visual analysis.

3. Separate data preparation from reporting

Draw the actual route from source system to usable result: extraction, transformation, storage, business model and published report. Name the owner and failure handling at every step. A BI tool may include preparation capabilities without replacing every integration or storage service. Compare data integration tools and data platforms where these remain separate parts of the proposed system. Reconcile one late source correction all the way through the report.

4. Test a restricted viewer

Use two isolated test identities with different permitted records. Open the same report, follow direct links, change filters, drill into detail and try the required exports. Record what each identity can access. Hiding a chart or navigation item is not sufficient evidence of a data boundary. Inspect the database connection and effective permissions as well as the application role. For a self-managed shortlist, Metabase vs Apache Superset separates software access from hosting and permission responsibilities.

5. Distinguish a dashboard, a report pack and planning

A dashboard supports exploration; a report pack may require fixed pages, recipient-specific filters and scheduled delivery. A planning process may also require controlled inputs, versions and approval. Specify the actual deliverable. Cognos vs Power BI examines reporting and recipient rights, while SAP Analytics Cloud vs Power BI separates analytics from a purchased planning workflow. Showing budget values does not by itself prove that users can maintain and approve a plan.

6. Set a freshness and failure requirement

Specify the latest acceptable data time for the decision. Distinguish source freshness, ingestion completion, model refresh and the time a viewer opens a report. In an authorised sandbox, interrupt a refresh or withhold a source update. Check what the viewer sees and whether an owner can identify the stale period. Ask who can rerun the process safely and how corrections affect already distributed output. A visually responsive report can still be showing yesterday’s incomplete data.

7. Cost a matched workload

Prepare one audience and workload sheet for every vendor: model builders, report authors, regular viewers, occasional customers, viewing frequency, refresh cadence, storage and formatted-report volume. Add the source-platform bill, integration, implementation, support and operational work. Model a defined peak as well as the normal month. Quick Sight vs Power BI explains why reader sessions and user licences are different units. Domo vs ThoughtSpot examines data activity and analytics consumption. Request a quote against the same brief.

8. Evaluate AI answers against known questions

If conversational analytics matters, use a small question set with an agreed interpretation and expected evidence. Include an ambiguous date phrase, an excluded record and a question outside the user’s permitted data. Ask the evaluator to show the metric, filter and query or explanation behind the answer. Record when the service asks for clarification, declines or produces a result requiring correction. Do not infer reliability from one fluent response. Confirm feature entitlement, regional availability and any additional provider charges separately.

9. For embedded analytics, test the application journey

A customer-facing report needs an identity path, a tenant boundary and a defined behaviour for loading, empty data, errors and expired sessions. Test copied links and exports with separate customer identities. Name the team that owns the application and the team that owns the model. Sisense vs Looker supplies an embedding evaluation brief. A successful administrator demonstration does not establish the experience or isolation of a customer account.

10. Require a maintainable handover

Have a second analyst update and reverse one metric change using documented assumptions. Record where versions, access rules, connection settings and recovery instructions are maintained. For complex operational analysis, include the scripts, extensions and local preparation used by the domain specialist; Spotfire vs Tableau covers investigation and publishing. Finish with named owners, evidence for each requirement, unresolved gaps and the full proposed contract scope. Keep preference scores separate from requirements that must pass.

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