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An enterprise BI proof of concept (POC) should use real business tasks to evaluate results, permissions, performance and delivery. HENGSHI SENSE provides modeling, HQL, metrics, data integration, visualization, Data Agent and open integration. Ten checks can help a project team assess their suitability for its target environment.
An effective POC gives candidate solutions the same key problems and retains verifiable records. Agree on data scale, accounts, refresh batches, deployment resources and acceptance criteria before starting. Comparing one system with an administrator account and warm caches against another with restricted users and cold queries produces unreliable conclusions.
1. Agree on Tasks, Environment and Acceptance Criteria
Use authorized, de-identified and representative data, retaining features that affect results, such as duplicate keys, null values and historical modifications. Configure business roles and realistic workloads in an isolated test environment, increasing concurrency gradually. Inject faults only into approved test connections, tasks and data.
Define where response time is measured. Initial feedback, query completion, full page load and completed export files are different measures. Fifty users, ten million rows or a two-second response may be inputs and targets for a particular project, but they must be accompanied by environment and task details. They are not universal product commitments.
2. Ten Checks That Turn Selection Questions into Evidence
1. Metric Reconciliation Across Entry Points
Query core metrics in HENGSHI dashboards, conversational analysis and the interfaces actually used, holding definition, period, scope, data timestamp and identity constant. Retain HQL, filters and results. Explain whether differences arise from definitions, permissions, refreshes or computation.
2. Role-Based Page and Export Permissions
Prepare headquarters, regional and ordinary user accounts. Check resource access, data scope, column permissions and actions such as export. Use real identities for APIs and embedded entry points too. Inspect new models and processed outputs according to the current permission mode and inheritance settings; field visibility alone is insufficient.
3. The Selected Multi-Source Modeling Path
Use data from different sources, such as orders and customers, in joint HENGSHI analysis. Verify types, codes, granularity and join cardinality. Select applicable paths such as same-source direct connection, a unified engine or data integration. Judge results, freshness, source load and maintenance cost rather than simply whether an intermediate database is involved.
4. Representative Query and Export Performance
Run trend, grouping and detail tasks against data close to the intended scale, then test the required export volume and formats. Record correctness, elapsed time, resource use and timeout location. HENGSHI provides paths including direct connection and acceleration; capacity and path selection require validation under actual deployment conditions.
5. Concurrent Queries, Refreshes and Conversational Analysis
Establish a single-task baseline, then simulate agreed arrival patterns and interaction intervals while increasing load gradually. Record duration distributions, failure rates, queuing and database and model-service resources. Distinguish cold queries, cache hits and refresh periods, and observe how long tasks affect others.
6. Completed Work and Recovery Results After a Fault
Simulate timeouts or connection failures in approved test tasks, retain execution records and inspect actual target data. After recovery, verify backfill or reruns according to incremental fields, business keys and output strategies. Insert, Upsert, truncation and rebuilding have different effects; recovery must match the actual configuration.
7. Dependencies, Refreshes and Publication After Definition Changes
Modify a test metric and check pages, conversational analysis and interfaces that reference it. Determine whether data must be reprocessed. A published HENGSHI application is a snapshot; authoring-area changes require republishing under the defined process. Acceptance records should include the version used and the result readers actually see.
8. Real Login and Authorization in Embedded Delivery
Choose HENGSHI iframe, JS SDK or API integration for the target product. Verify login identity, tenant scope, page interactions and error responses. Different entry points serve different tasks, so acceptance should follow actual embedding needs. Filter parameters express analytical conditions and cannot replace trusted authentication and authorization.
9. Upgrade Compatibility and Recovery Rehearsal
Follow the target version’s upgrade documentation in a test environment. Check data connections, models, metrics, complex pages and integrations. Record the downtime window, backups, recovery steps and durations, and confirm configuration and dependency compatibility. Downtime requirements should be agreed through business needs and implementation planning.
10. Evidence to Diagnose a Slow Task
Reproduce a representative slow task and correlate browser requests, execution records, database evidence and system debugging logs. Conversational analysis also requires checking model and tool execution; exports may involve proxies, gateways and rendering. The goal is an actionable cause supported by evidence. Available stage-level visibility depends on the actual environment.
3. Consolidate the Ten Results into One Acceptance Table
| Check | Main acceptance basis | Records to retain |
|---|---|---|
| Metrics | Correct results under matching conditions; explainable differences | Definitions, queries and reconciliation |
| Permissions | Access and operations match each identity’s authorization | Accounts, permission settings and page/file results |
| Multiple sources | Correct data with acceptable freshness and load | Models, execution paths and refresh records |
| Queries and exports | Agreed tasks and volumes meet requirements | Timing, formats, results and resources |
| Concurrency | Duration and failure rates meet targets under load | Workload model, percentiles and bottlenecks |
| Recovery | Completed writes are known; no omissions or unintended duplication after recovery | Task states, reruns and reconciliation |
| Changes | References, data and published versions reflect the new definition | Impact lists, retests and publication records |
| Embedding | Real identities and required functions work | Integration configuration and actual operations |
| Upgrades | Key capabilities work and recovery is effective | Upgrade, backup and recovery rehearsals |
| Troubleshooting | Causes can be explained with an actionable direction | Request, execution and system evidence |
4. Hypothetical POC: One Regional Business Analysis Task
The following is a hypothetical solution example. A group wants to use orders, refunds and targets in regional business reviews. The project team establishes suitable models and metrics in HENGSHI, builds an attainment dashboard, explores stores and periods through ChatBI, and uses Data Agent to help refine the analytical page.
The business owner checks results and explanations, regional accounts validate scope, and implementers verify refreshes, exports and portal integration. The team then changes a metric definition in the test environment, republishes it and checks the result users see. Organizing performance, fault and upgrade checks around this complete task helps determine whether the mechanisms work together.
5. Use POC Results to Support the Final Decision
Record core business scenarios, failed checks, causes and follow-up owners in the selection conclusion. Distinguish product capabilities, missing configuration, implementation cost and external dependencies. Where necessary, request a reproducible correction from the supplier. Have business users independently complete a task after data and permissions have been prepared, and record required training and maintenance effort.
HENGSHI combines data preparation, metric calculation, conversational analysis, authoring and open delivery. A POC should establish whether those capabilities produce maintainable results under the enterprise’s actual conditions, informing rollout scope, resource investment and implementation plans.