NCAA Digital Transformation - Business Intelligence (BI) System — Software Requirements Specification (SRS)
Table of Contents
- 1 Document Information
- 2 Project Overview
- 3 User Requirements
- 4 Technical Requirements
- 5 External Dependencies
- 6 Release Planning
- 7 Risks Assumptions
- 8 Market Specific Considerations
- 9 Sign Off
- 10 Detailed Feature Requirements
- 11 Additional Context
1 Document Information
| Field | Value |
|---|---|
| Project Name | NCAA Digital Transformation - Business Intelligence (BI) System |
| Version | 1.0 |
| Date | 2025-11-12 |
| Project Manager | TBD |
| Platforms | ['Web', 'Cloud Infrastructure', 'API Services'] |
| Budget | $190,000 |
| Module Code | BI_SYSTEM |
| Parent Project | NCAA Digital Transformation - Ngorongoro Gateway |
2 Project Overview
2.1 What Are We Building
2.1.1 System Function
The NCAA Business Intelligence (BI) System serves as the central analytical and decision-support platform for the entire organization. It consolidates operational, administrative, and conservation data across all directorates, sections, and units—transforming dispersed information into actionable insights that enhance efficiency, accountability, and strategic planning.
2.1.2 Users
- Board of Directors: Strategic oversight and institutional performance monitoring
- Commissioner & Deputy Commissioners: Executive decision-making and organizational governance
- Departmental Heads: Directorate-level analytics (Conservation & Tourism, Corporate Services, Cross-cutting Units)
- Field Officers & Managers: Operational analytics and real-time performance tracking
- Finance & HR Teams: Budget utilization, staff performance, and resource allocation analytics
- ICT & Data Teams: System administration, data governance, and technical monitoring
2.1.3 Problem Solved
Fragmented data across departments leading to delayed reporting, inconsistent decision-making, manual data exchange, time-consuming report generation, and limited inter-departmental visibility. The BI system eliminates data silos, unifies decision-making, and introduces a culture of measurable performance across all NCAA departments.
2.1.4 Key Success Metric
100% unified data visibility across all departments, 95% reduction in manual reporting time, automated and standardized reporting processes, instant analytics availability, predictive and AI-driven decision-making capabilities, full accountability through shared dashboards and role-based access.
2.2 Scope
2.2.1 In Scope
- Enterprise-wide data integration from all NCAA systems (Gateway, Mobile App, Fleet, Surveillance, Finance, HR, Safari Portal)
- ETL (Extract, Transform, Load) pipeline for automated data ingestion and transformation
- Centralized data warehouse (PostgreSQL/Cloud-based) for all institutional data
- Departmental dashboards and performance analytics for all directorates
- Predictive analytics engine powered by Nasera AI
- Automated reporting and compliance module for statutory and management reports
- Data governance and security framework with role-based access control
- API-based integration framework for internal and external systems
- Real-time data synchronization with offline node support
- Cross-departmental reporting connecting Conservation, Tourism, Finance, HR, Procurement, Legal, ICT
- Comprehensive audit trails for transparency and compliance
2.2.2 Out Of Scope
- Development of new source systems (focuses on integration of existing systems)
- Direct field data collection (relies on existing systems for data capture)
- Replacement of existing departmental systems (augments and integrates with them)
- Manual data entry interfaces (emphasizes automated data flows)
- Standalone analytics tools outside the unified BI framework
3 User Requirements
3.1 Enterprise Data Integration
| Feature Code | I Want To | So That I Can | Priority | Notes |
|---|---|---|---|---|
| FT-BI-INTEGRATION | Integrate data from all NCAA systems including Gateway, Mobile App, Surveillance, Fleet Management, Finance, HR, and Safari Portal | Have a single, reliable source of truth for all organizational operations and eliminate data silos | Must | API-based bidirectional connectivity with token-based authentication and encryption. Supports both internal and selected external systems. |
| FT-BI-ETL | Automate data ingestion, cleaning, and transformation through ETL pipelines | Ensure data quality, standardization, and timely availability for analytics without manual intervention | Must | Python ETL scripts with Airflow orchestration and RESTful API connectors. Maintains metadata catalogs for governance. |
3.2 Departmental Analytics
| Feature Code | I Want To | So That I Can | Priority | Notes |
|---|---|---|---|---|
| FT-BI-DASHBOARDS | Access customized dashboards for each directorate with relevant KPIs and visualizations | Monitor departmental performance, track key metrics, and make data-driven decisions | Must | Covers Conservation & Tourism, Corporate Services, and Cross-cutting Units with drill-down capabilities. |
| FT-BI-CROSSDEPT | View cross-departmental reports that connect data from multiple directorates | Understand inter-departmental relationships and organizational-wide performance | Should | Unified reporting framework connecting all NCAA directorates and units. |
3.3 Predictive Analytics
| Feature Code | I Want To | So That I Can | Priority | Notes |
|---|---|---|---|---|
| FT-BI-PREDICT | Access predictive analytics for visitor trends, revenue forecasts, and resource allocation | Plan proactively and make strategic decisions based on data-driven forecasts | Must | Powered by Nasera AI's integrated data-science models with seasonal trends and forecasting capabilities. |
| FT-BI-PRESCRIPTIVE | Receive prescriptive recommendations for resource optimization and operational improvements | Take action based on AI-driven insights and best practice recommendations | Should | AI-powered recommendations based on historical patterns and organizational goals. |
3.4 Reporting Compliance
| Feature Code | I Want To | So That I Can | Priority | Notes |
|---|---|---|---|---|
| FT-BI-AUTOREPORT | Generate automated statutory and management reports for oversight bodies | Ensure timely submission and compliance with internal and national reporting standards | Must | Reduces manual reporting cycles by 95% with integrated audit trails for compliance. |
| FT-BI-AUDIT | Access comprehensive audit trails for all data transactions and system decisions | Maintain transparency, accountability, and compliance with NCAA operational standards | Must | Every transaction and dataset change is logged with timestamp and user information. |
3.5 Data Governance Security
| Feature Code | I Want To | So That I Can | Priority | Notes |
|---|---|---|---|---|
| FT-BI-RBAC | Control data access based on user roles and responsibility levels | Ensure data security and that users only access information relevant to their roles | Must | Role-based access control with encrypted communication and multi-factor authentication. |
| FT-BI-GOVERNANCE | Manage data validation, versioning, and integrity verification | Ensure data quality and compliance with NCAA and national data protection standards | Must | Built-in data governance tools with validation rules and access control protocols. |
3.6 System Accessibility
| Feature Code | I Want To | So That I Can | Priority | Notes |
|---|---|---|---|---|
| FT-BI-REALTIME | Access real-time data and analytics through web and mobile interfaces | Make timely decisions based on current operational status | Must | Secure authenticated access via web and mobile-optimized interfaces. |
| FT-BI-OFFLINE | Continue data collection and synchronization during network connectivity issues | Maintain continuous operations in low-connectivity environments | Must | Node-based synchronization ensures continued access and updates even in remote areas. |
4 Technical Requirements
4.1 Performance
4.1.1 Dashboard Load Time
< 3 seconds for standard dashboards
4.1.2 Data Refresh Rate
Real-time for critical metrics, 5-minute intervals for standard analytics
4.1.3 Query Response Time
< 2 seconds for standard queries, < 10 seconds for complex analytics
4.1.4 Etl Processing Time
< 2 minutes for incremental updates, < 30 minutes for full daily processing
4.1.5 Concurrent Users
Support for 200+ concurrent users across all departments
4.2 Platforms Supported
4.2.1 Web Browsers
Chrome 90+, Firefox 88+, Safari 14+, Edge 90+
4.2.2 Operating Systems
Windows 10+, macOS 11+, Linux (Ubuntu 20.04+)
4.2.3 Mobile Platforms
iOS 12+ and Android 8+ (responsive web interface)
4.2.4 Cloud Infrastructure
AWS, Google Cloud, or Azure with scalable architecture
4.3 Data Storage
4.3.1 Primary Database
PostgreSQL 13+ or cloud-based (AWS Redshift, Google BigQuery)
4.3.2 Data Warehouse Capacity
Scalable cloud storage with minimum 5TB initial capacity
4.3.3 Backup Frequency
Hourly incremental backups, daily full backups
4.3.4 Data Retention
7 years for transaction data, 3 years for operational logs
4.3.5 Archival Strategy
Automated data archival to cold storage after 2 years
4.4 Security Requirements
4.4.1 Encryption At Rest
AES-256 encryption for all stored data
4.4.2 Encryption In Transit
TLS 1.3 for all API communications
4.4.3 Authentication
OAuth 2.0 with JWT tokens, multi-factor authentication for admin access
4.4.4 Authorization
Role-based access control (RBAC) with granular permissions
4.4.5 Api Security
Token-based authentication, SSL encryption, API-level rate limiting
4.4.6 Compliance
NCAA ICT policies and Tanzania national data governance standards
4.5 Integration Requirements
4.5.1 Api Architecture
RESTful APIs with JSON data format
4.5.2 Api Authentication
Token-based with HTTPS encryption
4.5.3 Data Sync Frequency
Real-time for critical systems, 5-minute intervals for others
4.5.4 Supported Integrations
- Ngorongoro Gateway
- NCAA Mobile Application
- Fleet Management System
- Surveillance System
- Safari Portal
- Finance Systems
- HR Systems
- Nasera AI
5 External Dependencies
5.1 Third Party Services
| Service Name | Purpose | Criticality | Alternatives |
|---|---|---|---|
| Cloud Infrastructure Provider | Hosting data warehouse and BI platform | High | AWS, Google Cloud, or Azure |
| Power BI / Metabase | Dashboard visualization and analytics | High | Tableau, Looker, or custom React-based dashboards |
| Apache Airflow | ETL pipeline orchestration | Medium | Apache NiFi, Luigi, or custom Python schedulers |
5.2 Internal Systems
| System Name | Integration Method | Data Frequency | Criticality |
|---|---|---|---|
| Ngorongoro Gateway | RESTful API | Real-time | High |
| NCAA Mobile Application | RESTful API | Real-time | High |
| Nasera AI | RESTful API + Direct Database Access | Real-time | High |
| Fleet Management System | RESTful API | 5-minute intervals | Medium |
| Surveillance System | RESTful API | Real-time | Medium |
6 Release Planning
6.1 Phase 1
6.1.1 Name
Discovery & Architecture
6.1.2 Duration
4-6 weeks
6.1.3 Deliverables
- Data source identification and analysis across all NCAA systems
- Data warehouse design and schema definition
- API integration architecture and security framework
- ETL pipeline design and data flow documentation
6.2 Phase 2
6.2.1 Name
Core Platform Development
6.2.2 Duration
6-12 months
6.2.3 Deliverables
- ETL pipeline development and automated data ingestion
- Data cleaning and transformation algorithms
- Dashboard and report development for all directorates
- Advanced analytics modules (predictive and prescriptive)
- User access and security framework implementation
- API development for system integrations
6.3 Phase 3
6.3.1 Name
Deployment & Training
6.3.2 Duration
2-3 months (ongoing)
6.3.3 Deliverables
- System deployment on cloud infrastructure
- User training and documentation for all departments
- Post-launch support and optimization
- Continuous monitoring and performance tuning
7 Risks Assumptions
7.1 Risks
| Risk | Mitigation | Probability | Impact |
|---|---|---|---|
| Data quality issues from legacy systems | Implement comprehensive data validation and cleaning in ETL pipeline | Medium | Medium |
| Resistance to data-driven culture change | Comprehensive training program and change management support | Medium | Low |
| API integration delays with external systems | Phased integration approach with fallback to manual data imports | Low | Medium |
| Cloud infrastructure costs exceeding budget | Implement cost monitoring and optimization, negotiate reserved instances | Low | Medium |
7.2 Assumptions
- All NCAA source systems will expose or develop APIs for data integration
- Departmental staff will be available for training and knowledge transfer
- Cloud infrastructure provider will maintain 99.9% uptime SLA
- NCAA ICT team will provide ongoing support for system maintenance
- Data governance policies will be established and enforced across all departments
8 Market Specific Considerations
8.1 Tanzania Context
- Alignment with Tanzania's Digital Economy Strategic Framework
- Support for government digital transformation initiatives in tourism sector
- Compliance with Tanzania Data Protection Act and ICT regulations
- Integration capabilities with national tourism databases and regulatory platforms
8.2 Conservation Sector
- Best practices from international conservation organizations (IUCN, WWF)
- Integration with global conservation monitoring systems
- Support for UNESCO World Heritage Site reporting requirements
- Collaboration framework with Tanzania National Parks Authority (TANAPA)
8.3 Low Connectivity Adaptation
- Node-based synchronization for distributed gate operations
- Offline data caching with automatic sync on reconnection
- Low-bandwidth optimized API communications
- Local processing capabilities at remote locations
9 Sign Off
9.1 Prepared By
SkyConnect Development Team
9.2 Reviewed By
TBD - NCAA ICT Department
9.3 Approved By
TBD - NCAA Management
9.4 Date
2025-11-12
9.5 Version
1.0
10 Detailed Feature Requirements
10.1 Ft Bi Integration
10.1.1 Feature Name
Enterprise-Wide Data Integration
10.1.2 Description
Comprehensive API-based integration framework connecting all NCAA internal and external systems
10.1.3 User Stories
- As a data administrator, I want to configure API connections to all source systems so that data flows automatically into the BI platform
- As a department head, I want to see data from my department integrated with other directorates so that I understand cross-functional relationships
- As an executive, I want a unified view of all organizational data so that I can make strategic decisions
10.1.4 Acceptance Criteria
- All internal systems (Gateway, Mobile, Fleet, Surveillance) successfully integrated via API
- Finance, HR, and Safari Portal data synchronized at least every 5 minutes
- External systems integration capability with token-based authentication
- Error logging and retry mechanisms for failed API calls
- API monitoring dashboard showing integration health status
10.1.5 Test Cases
| Test Id | Description | Preconditions | Steps | Expected Result | Priority |
|---|---|---|---|---|---|
| TC-BI-INT-001 | Verify real-time data sync from Gateway system | Gateway system operational with active transactions | 1. Create new entry in Gateway 2. Wait 30 seconds 3. Check BI dashboard | New entry visible in BI dashboard within 30 seconds | High |
| TC-BI-INT-002 | Verify API authentication and security | API endpoints configured | 1. Attempt API call without token 2. Attempt with invalid token 3. Attempt with valid token | Calls 1 and 2 rejected, call 3 successful | High |
| TC-BI-INT-003 | Verify data sync during connectivity loss | Node-based system with offline capability | 1. Disconnect network 2. Create entries 3. Reconnect network 4. Check sync | All offline entries synchronized within 2 minutes of reconnection | Medium |
10.2 Ft Bi Etl
10.2.1 Feature Name
Automated ETL Pipeline
10.2.2 Description
Comprehensive data extraction, transformation, and loading pipeline with quality assurance
10.2.3 User Stories
- As a data engineer, I want automated ETL pipelines so that data is ingested and transformed without manual intervention
- As a data analyst, I want clean and standardized data so that my analytics are accurate and reliable
- As an administrator, I want to monitor ETL processes so that I can identify and resolve issues quickly
10.2.4 Acceptance Criteria
- Automated data ingestion from all configured sources
- Data validation and cleaning rules applied to all incoming data
- Data transformation to standard formats and schemas
- Metadata catalog maintenance for all datasets
- ETL monitoring dashboard with error alerts
- Incremental processing < 2 minutes, full daily processing < 30 minutes
10.2.5 Test Cases
| Test Id | Description | Preconditions | Steps | Expected Result | Priority |
|---|---|---|---|---|---|
| TC-BI-ETL-001 | Verify data validation rules enforcement | ETL pipeline configured with validation rules | 1. Submit data with invalid formats 2. Submit data with missing fields 3. Submit valid data | Invalid data rejected with error messages, valid data processed successfully | High |
| TC-BI-ETL-002 | Verify incremental data processing performance | ETL pipeline operational | 1. Submit 1000 new records 2. Measure processing time 3. Verify data in warehouse | Processing completes in < 2 minutes, all records in warehouse | High |
| TC-BI-ETL-003 | Verify metadata catalog updates | Metadata catalog system active | 1. Add new data source 2. Run ETL pipeline 3. Check metadata catalog | New data source documented in catalog with schema and lineage information | Medium |
10.3 Ft Bi Dashboards
10.3.1 Feature Name
Departmental Dashboards and Analytics
10.3.2 Description
Customized interactive dashboards for each NCAA directorate with drill-down capabilities
10.3.3 User Stories
- As a Conservation Director, I want to see visitor flow, revenue, and ecological indicators in one dashboard
- As a Corporate Services Director, I want to track budget utilization, staff performance, and procurement cycles
- As a department manager, I want to drill down into specific metrics to understand underlying trends
10.3.4 Acceptance Criteria
- Separate dashboards for Conservation & Tourism, Corporate Services, and Cross-cutting Units
- Customizable KPI widgets for each directorate
- Drill-down capability from summary to detailed views
- Interactive visualizations (charts, graphs, maps)
- Export functionality for reports and presentations
- Dashboard load time < 3 seconds
10.3.5 Test Cases
| Test Id | Description | Preconditions | Steps | Expected Result | Priority |
|---|---|---|---|---|---|
| TC-BI-DASH-001 | Verify Conservation & Tourism dashboard displays all KPIs | User logged in with Conservation role | 1. Navigate to dashboard 2. Verify visitor flow widget 3. Verify revenue widget 4. Verify ecological indicators | All widgets display current data with accurate values | High |
| TC-BI-DASH-002 | Verify drill-down functionality | Dashboard displaying summary data | 1. Click on revenue summary 2. Select specific gate 3. Select date range | Detailed revenue breakdown displayed for selected gate and date range | High |
| TC-BI-DASH-003 | Verify dashboard performance with 50 concurrent users | Load testing environment configured | 1. Simulate 50 users accessing dashboards 2. Measure load time 3. Check system resources | All dashboards load in < 3 seconds, system remains stable | Medium |
10.4 Ft Bi Predict
10.4.1 Feature Name
Predictive Analytics Engine
10.4.2 Description
AI-powered forecasting for visitor trends, revenue, and resource allocation
10.4.3 User Stories
- As an operations manager, I want to see predicted visitor numbers for next month so that I can plan staffing accordingly
- As a finance director, I want revenue forecasts so that I can prepare budget projections
- As a resource planner, I want to know optimal resource allocation based on historical patterns
10.4.4 Acceptance Criteria
- Seasonal visitor trend predictions with 80%+ accuracy
- Revenue forecasting for 1, 3, and 6 month horizons
- Resource allocation recommendations based on predictive models
- Integration with Nasera AI for model training and inference
- Confidence intervals displayed for all predictions
- Model retraining on monthly basis with new data
10.4.5 Test Cases
| Test Id | Description | Preconditions | Steps | Expected Result | Priority |
|---|---|---|---|---|---|
| TC-BI-PRED-001 | Verify visitor trend prediction accuracy | Historical data for at least 2 years available | 1. Generate prediction for last month 2. Compare with actual data 3. Calculate accuracy | Prediction accuracy > 80% for monthly visitor numbers | High |
| TC-BI-PRED-002 | Verify revenue forecast generation | Predictive model trained and deployed | 1. Request 3-month revenue forecast 2. Review forecast details 3. Check confidence intervals | Forecast generated with values for each month and confidence intervals displayed | High |
| TC-BI-PRED-003 | Verify model retraining process | New month of data available | 1. Trigger model retraining 2. Monitor training progress 3. Validate new model | Model retrained successfully, accuracy maintained or improved | Medium |
10.5 Ft Bi Autoreport
10.5.1 Feature Name
Automated Reporting and Compliance
10.5.2 Description
Automated generation of statutory and management reports with audit trails
10.5.3 User Stories
- As a compliance officer, I want automated report generation so that I meet all regulatory deadlines
- As a board secretary, I want management reports ready before meetings without manual compilation
- As an auditor, I want complete audit trails so that I can verify all reported data
10.5.4 Acceptance Criteria
- Automated generation of monthly, quarterly, and annual reports
- Customizable report templates for different stakeholders
- Scheduled report delivery via email or dashboard
- Complete audit trails with timestamp and user information
- 95% reduction in manual reporting time
- Reports comply with NCAA and national reporting standards
10.5.5 Test Cases
| Test Id | Description | Preconditions | Steps | Expected Result | Priority |
|---|---|---|---|---|---|
| TC-BI-REP-001 | Verify automated monthly report generation | End of month data available | 1. Trigger monthly report 2. Review report content 3. Verify data accuracy 4. Check audit trail | Report generated with all required sections, data accurate, audit trail complete | High |
| TC-BI-REP-002 | Verify scheduled report delivery | Report schedule configured for Board meetings | 1. Configure weekly report 2. Wait for scheduled time 3. Check email delivery | Report delivered automatically at scheduled time to configured recipients | High |
| TC-BI-REP-003 | Verify audit trail completeness | Multiple data transactions completed | 1. Access audit trail interface 2. Filter by date range 3. Review transaction logs | All transactions logged with timestamp, user, and change details | Medium |
11 Additional Context
11.1 System Architecture
11.1.1 Data Source Layer
Collects data from all digital platforms and legacy systems via secured endpoint APIs with token-based authentication, SSL encryption, and data validation
11.1.2 Etl Pipeline
Automated data ingestion and transformation powered by Python ETL scripts, Airflow orchestration, and RESTful API connectors
11.1.3 Data Warehouse
Centralized repository on PostgreSQL or cloud-based (AWS Redshift/Google BigQuery) optimized for high-speed analytics
11.1.4 Analytics Visualization
Power BI and Metabase dashboards integrated with Nasera AI for natural language queries
11.1.5 Security Access
Role-based access control (RBAC), encrypted communication, API rate limiting, multi-factor authentication
11.1.6 Node Synchronization
Distributed Gate Nodes sync with BI system through secure APIs for real-time updates in low-connectivity environments
11.2 Integration Approach
11.2.1 Data Ingestion
All internal and external systems expose secured endpoint APIs transmitting encrypted data to BI server
11.2.2 Transformation Storage
Data cleaned, aggregated, and stored in BI warehouse for real-time and historical analysis
11.2.3 Processing Analytics
BI engine processes datasets and feeds dashboards and predictive models
11.2.4 Ai Enhancement
Nasera AI interprets trends, detects anomalies, enables natural language queries
11.2.5 Distribution
Dashboards and reports delivered securely via authenticated web and mobile interfaces
11.3 Key Benefits
11.3.1 Data Accessibility
From fragmented and delayed to centralized, real-time access via APIs - achieving unified visibility
11.3.2 Decision Making
From periodic reports to predictive and AI-driven insights - enabling data-informed decisions
11.3.3 Data Exchange
From manual uploads to automated API-based synchronization - achieving 100% automation
11.3.4 Reporting
From time-consuming and inconsistent to automated and standardized - achieving 95% time reduction
11.3.5 Transparency
From limited inter-departmental view to shared dashboards with role-based access - achieving full accountability
11.4 Total Budget Breakdown
11.4.1 Discovery Architecture
$40,000 (Data source analysis and warehouse design)
11.4.2 Data Engineering Etl
$30,000 (ETL development and data transformation)
11.4.3 Bi Analytics Platform
$90,000 (Dashboards, advanced analytics, user access)
11.4.4 Deployment Training Support
$30,000 (System deployment, training, post-launch support)
11.4.5 Total
$190,000