- International development
- Siebel CRM
- Data warehousing
Turning Siebel Data into a Reporting Data Warehouse for a United Nations Organization
How BEEU TECH helped a United Nations organization move data from its Siebel application into a secure data warehouse through automated ETL pipelines, scheduled refreshes and standardized datasets for reporting.
Our actual experience delivering great tech and talent.
Engagement overview
- Industry
- International development
- Reach
- Country offices and headquarters
- Engagement model
- Embedded data engineer
- Source system
- Siebel application
- Engagement status
- Live, with ongoing support
1. The client
Our client is a United Nations organization that delivers programs and services worldwide, and depends on accurate, timely and reliable data to do it.
Its core operational system, a Siebel application, captures critical information about program implementation and service delivery across country offices and headquarters.
That data is essential for monitoring performance, ensuring accountability and making evidence-based decisions across the organization.
2. The challenge
Operational data that was never built for reporting
The Siebel application's data was complex and highly normalized, built for running operations and not optimized for analytical reporting. Getting clean, consolidated data out of it was slow and inconsistent.
Six challenges shaped the work.
Complex data model
Siebel data was complex, highly normalized and not optimized for analytical reporting.
Limited access
Teams had little direct access to clean, consolidated data for reporting and analysis.
Manual extraction
Manual or semi-automated extraction led to delays and inconsistencies.
Accuracy and history
Keeping data accurate, tracking history and staying consistent across reports was difficult.
Rising demand
End users wanted timely, standardized and self-service reports.
Strict governance
Everything had to meet UN data governance, security and confidentiality requirements.
The organization needed a dependable way to turn operational Siebel data into reporting-ready information, on a schedule and within its security rules.
3. The solution
A secure data warehouse fed by automated ETL
BEEU TECH's data engineer built a centralized pipeline that extracts data from the Siebel application, transforms it into analytical formats and stores it in a secure data warehouse.
Standardized datasets and reusable dataflows, refreshed on a schedule, give report builders consistent and reliable data to work from.
Six pillars of the warehouse pipeline
-
Siebel data model analysis
Analyzed the Siebel data model in detail and identified the entities that matter for reporting.
-
ETL and ELT pipelines
Built pipelines that extract data from Siebel and load it into the data warehouse.
-
Reporting-ready datasets
Transformed and optimized raw data into structured datasets ready for reporting.
-
Scheduled refreshes
Built automated dataflows that refresh the data at scheduled intervals.
-
Quality and reconciliation
Set up data quality checks, validation rules and reconciliation processes to keep the warehouse accurate.
-
Security and access control
Kept every step within UN security, access control and data privacy standards.
4. The engineering team
BEEU TECH's data engineer worked with the organization's reporting teams to make sure every dataset met business and program requirements.
-
Data Engineer
Analyzed the Siebel data model, built the ETL pipelines and warehouse models, and set up quality checks and scheduled refreshes.
5. The implementation journey
Five phases, from data assessment to go-live
Each phase built on the last, from understanding the Siebel data to supporting the reports that use it.
-
Phase 1
Requirements and data assessment
The work began with the reporting needs and a review of the Siebel data structures behind them.
-
Phase 2
Pipeline and warehouse development
Next came the extraction processes and the data warehouse models.
-
Phase 3
Dataset and dataflow creation
Reusable datasets were developed, along with the automated mechanisms that refresh them.
-
Phase 4
Testing and validation
Data quality was validated, and stakeholders signed off before go-live.
-
Phase 5
Deployment and support
After go-live, the work continued with monitoring, optimization and enhancements.
6. The business impact
Trusted, up-to-date data for every report
Automated pipelines replaced manual extraction, and the warehouse became the central source for reporting on Siebel data.
Before and after the data warehouse
| Before | After |
|---|---|
| Highly normalized operational data | Structured, reporting-ready datasets |
| Manual or semi-automated extraction | Automated ETL pipelines and scheduled refreshes |
| Delays and inconsistencies | Reliable, timely access to Siebel data |
| Hard to trace figures across reports | Accuracy, consistency and traceability across reports |
| Limited self-service reporting | Standardized dashboards and reports for end users |
| Manual effort and operational risk | Less manual effort and lower operational risk |
7. Long-term value
Reporting that keeps pace with the organization
With Siebel data flowing into the warehouse on a schedule, stakeholders work from trusted, up-to-date information.
Validation rules and reconciliation keep figures consistent from one report to the next, and traceability shows where each one came from.
The engagement shows how disciplined data engineering can turn an operational system into a reliable source of insight for program decisions.
8. Data architecture
- SiebelSource operational application
- ETL and ELT pipelinesExtraction and scheduled refresh
- Data warehouseSecure analytical store
- Dashboards and reportsStandardized, for end users
9. Engineer perspective
“Through robust data engineering and automation, the solution improved reporting efficiency, data quality, and transparency, enabling stakeholders to make informed decisions in support of UN program objectives.”
BEEU TECH · TECHNOLOGY DELIVERY
Get critical data out of operational systems and into reports.
Is your reporting data locked inside an operational system like Siebel? BEEU TECH's data engineers build the pipelines and warehouses that turn it into reliable, timely reports.
Build the data team your reporting needs.
