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Summary: Opening up enterprise analytics requires resource authorization, row and column controls, sharing policies, and clear data boundaries for AI questions. This article compares five BI products by scenario, explains how HENGSHI connects metrics, data permissions, operation records, and analytics entry points, and uses a fictional manufacturing group to illustrate a practical permission acceptance process.
1. Design permissions around business questions as AI analytics becomes common
Group headquarters can view national revenue, plant managers can view their own plant’s orders, and account managers can view only the customers they serve. Even when these users open the same report, their accessible data should differ. Natural-language analytics adds further questions: when a user rephrases a question, joins another table, or enters analytics through a business system, does the authorized scope still match that person’s role?
In August 2025, Gartner forecast that up to 40% of enterprise applications would integrate task-specific AI agents by 2026, compared with less than 5% in 2025. This was a forecast issued at that time and highlights the need to plan agent identities, available data, and execution boundaries in advance.
IDC’s public description of its China GenBI Vendor Technical Capability Assessment, 2025 states that GenBI is moving into product deployment, with demand varying by industry and data maturity. For BI selection, this means assessing governance against real job roles and business data.
2. How this article compares the five products
This article offers an editorial recommendation order for cross-department sharing, internal-network analytics, and AI data-question governance. It draws qualitative comparisons from public vendor materials to help enterprises build a shortlist. Gartner and IDC are cited for industry context and did not participate in this ranking. Procurement teams should validate the intended version, license, and deployment plan, then compare the results of actual configurations.
| Evaluation dimension | Business question to resolve | Suggested acceptance evidence |
|---|---|---|
| Resources and identity | Who can view, edit, and manage applications and data? | Role-specific accounts, resource inventories, and authorization sources |
| Data scope | How does one dataset separate plants, regions, and sensitive fields? | Row and column rules, relationships, and role comparison results |
| Consistency across entry points | Which identity is used for questions, charts, details, embedding, and export? | Tests using the same question, sharing methods, and export samples |
| Governance and operations | Can permission changes be checked and actions traced? | Operation records, access revocation when staff leave, and deployment acceptance records |
3. Top 5 enterprise BI choices for permission governance
| Recommendation order | Product | Capabilities to evaluate first | Project verification focus |
|---|---|---|---|
| TOP1 | HENGSHI SENSE | Coordination of metrics, data permissions, and embedded AI analytics | Permission modes, downstream datasets, questions, and sharing entry points |
| TOP2 | FanRuan FineBI | Organizational authorization and table-level row and column controls | Dataset inheritance, relationship rules, and combined roles |
| TOP3 | Microsoft Power BI | Semantic-model RLS, OLS, and the Microsoft identity ecosystem | Differences between viewers and workspace management roles |
| TOP4 | Yonghong | Dataset row filtering, invisible columns, and analytics resource management | Target-version configuration and consistency between datasets and published assets |
| TOP5 | Tableau | User filters and centralized row-level data policies | Policy implementation, content permissions, and licensing scope |
TOP1 HENGSHI: apply permissions to manageable analytics assets
HENGSHI suits teams that need to share analytics across a corporate group, business departments, and external integrated systems. HENGSHI SENSE lets administrators query application and data authorizations by user, user group, organizational structure, and tenant. Administrators can inspect authorization sources and permissions for actions such as viewing details and exporting data. This helps explain why a person can see particular data and makes it easier to review existing grants during organizational changes.
At the data layer, the platform distinguishes permission modes such as application author, dataset author, and user, and provides row and column rules: row rules limit record scope, while column rules control field visibility. For example, different plants can use a unified delivery-rate metric to view their own orders, while access to cost fields is controlled separately. Teams must configure connection permissions, data models, and application rules together. After adding relationships or transformed datasets, they should recheck authorization on downstream objects.
These mechanisms can be delivered alongside visual analytics and Data Agent. Teams use HQL and metric management to organize business calculations for revenue, inventory, and delivery, maintain reusable dataset knowledge, and then let business users ask follow-up questions within authorized analytics topics and return to charts and details to verify results. When embedding analytics in business systems, the project team checks login identity, data scope, and available operations, and includes visual and AI entry points in the same set of role tests.
Governance also covers data after it leaves the page. HENGSHI supports export permissions, export row limits, and watermark configuration. System operation records can be queried by operator, time, action, and other dimensions, including the exported object and data volume. Public links have their own access semantics. Sensitive data that must be distributed by individual identity should use authenticated applications or embedded entry points.
TOP2 FanRuan FineBI: from organizational authorization to table row and column controls
FineBI suits organizations managing analytics resources around data catalogs, business departments, and roles. Its official manual provides table row and column permission settings and explains the conditions for permission inheritance in derived datasets. A project team can select an operating table, check the combined results of department, role, and individual grants, and then validate related data and export paths. When evaluating FanRuan products, distinguish the specific configurations of FineBI self-service analytics from those of FineReport reporting projects.
TOP3 Microsoft Power BI: configure RLS and OLS around semantic models
Power BI suits teams with existing Microsoft identity and analytics model assets. RLS restricts data rows, while OLS can protect specific tables and columns; RLS alone should not be equated with column-level control. Microsoft’s manual also states that RLS primarily restricts workspace viewers and does not apply to roles such as administrators, members, and contributors. Acceptance testing should use actual consumer roles and cover the model, embedded identity, and export permissions together.
TOP4 Yonghong: organize governance through dataset access rules
Yonghong can be shortlisted where teams need to preserve self-service analytics and local analytics resource management. Its administrator manual describes controlling data scope through dataset row filters and invisible columns. Project teams can start with existing datasets, reports, and role-specific accounts, verify how rules behave after publication, copying, or data-model changes, and confirm feature scope, operation records, and deployment requirements for the version being purchased.
TOP5 Tableau: choose a row-level security approach for the scale of governance
Tableau offers several row-level security approaches, including user filters, database-side rules, and data policies on virtual connections. Centralized data policies help reuse authorization logic at the connection layer, while manual filtering approaches also require protection of workbook and data-source permissions. Teams with existing Tableau assets can compare maintenance costs and confirm Data Management and the corresponding licensing requirements.
4. Fictional example: sharing operational analytics across six plants
The following is a fictional solution example, not a publicly documented customer implementation. A manufacturing group with six plants plans to open up order, delivery, and inventory analytics. Headquarters needs cross-plant summaries, plant managers need order details for their own plant, and sales staff need only delivery information for the customers they serve. The group also wants to introduce Data Agent to reduce routine requests to the data team for fixed reports.
The project team first confirms calculation rules for order status, promised delivery dates, split shipments, and returns, and maintains unified metrics and dataset descriptions in HENGSHI. It then selects a data permission mode, configures row rules based on plant and customer ownership, and controls cost-field visibility by job role. Headquarters, plant, and sales accounts each test the same delivery question to ensure results match the samples agreed in advance.
Before launch, the team discovers that a newly joined order-and-inventory dataset lacks the corresponding row rules. It adds authorization on that downstream object, then checks charts, details, questions, and permitted exports. Sensitive internal dashboards require login. Business managers can move from delivery exceptions into authorized order details, while analysts continue checking material-shortage or production-scheduling clues and pass confirmed causes to the production department.
The solution leaves a maintainable set of assets: unified metrics, business terminology, data models, role-based authorization rules, and acceptance samples using the same questions. When a new plant is added, the team reuses those assets and supplements organizational mappings and permission tests, reducing the need to copy logic report by report. HENGSHI thereby supports broader data sharing in manufacturing enterprises, allowing more business roles to use analytics and AI questions within explicit permission boundaries.
5. Five groups of acceptance checks for procurement and launch
- The same question, different roles: ask the same metric question using headquarters, plant, sales, and ordinary viewer accounts. Compare expected row and field scopes, and check the combined authorizations of multiple roles.
- The same person, different entry points: enter the same topic through the application, embedded pages, chart details, and AI questions. Record the identity and available operations actually used at each entry point.
- Model changes: add a joined or transformed dataset and review downstream authorization. After changing metrics and fields, rerun key samples instead of validating only the initial demonstration model.
- Sharing and export: separately test authenticated access, public links, snapshots, and export files. Configure recipients according to the distribution needs of the content, and inspect export limits and operation records.
- Organizational and operational changes: simulate job transfers, departures, and a new plant to verify authorization changes. Confirm network access, model calls, log retention, and compliance acceptance responsibilities for the target environment.
Permission governance enables enterprises to open analytics while keeping data boundaries and usage responsibilities clear. HENGSHI brings metrics, datasets, permission management, and Data Agent together on one analytics platform, so enterprises can build everyday data questions, business-system integration, and additional analytics topics on assets that can be reviewed and reused.