What Is a Data Warehouse?

A Data Warehouse is a large-scale data storage system specifically designed for analytics and reporting — not for recording daily transactions.

The concept was introduced by Bill Inmon in the 1990s, who defined a Data Warehouse as:

"A subject-oriented, integrated, non-volatile, and time-variant collection of data in support of management's decisions."

- Bill Inmon, Father of Data Warehousing

In simple terms, a Data Warehouse has 4 key characteristics:

Characteristic	Meaning	Government Example
Subject-Oriented	Data is organized by subject of interest, not by the system that created it	"Budget disbursement" data from all departments and programs combined in one place
Integrated	Combines data from multiple sources into a unified format	Data from ERP + GFMIS + e-GP + HR systems consolidated into one consistent format
Non-Volatile	Once data enters, it is not modified or deleted — preserved as history	FY2025 disbursement data remains unchanged even if the source system is updated in the current fiscal year
Time-Variant	Stores data across time periods for trend analysis	Compare capital expenditure disbursement rates over the past 5 fiscal years by quarter

How Is a Data Warehouse Different from an ERP Database?

Many agencies wonder: "We already have a database in our ERP — why do we need a Data Warehouse?" The answer is that they are designed for entirely different purposes:

Aspect	OLTP Database (ERP)	Data Warehouse (OLAP)
Purpose	Record daily transactions (disbursements, procurement, receipts)	In-depth analytics, executive reporting
Data Nature	Current data, constantly changing	Historical data accumulated over multiple fiscal years
Query Pattern	Read/write individual records, fast	Read large volumes, aggregate across tables
Schema Design	Normalized (3NF) — minimizes redundancy	Denormalized (Star/Snowflake Schema) — optimized for reads
Users	Operational staff (finance, procurement, inventory)	Executives, policy planners, auditors, analysts
Example Query	"What is the status of PO-2026-001?"	"How does Q1 FY2026 capital disbursement compare to the past 3 years?"

Core Components of a Data Warehouse

A Data Warehouse is not just "another database" — it is an entire architecture consisting of:

1. Data Sources

For government agencies, data typically comes from multiple systems:

• ERP System — Accounting, procurement, inventory, budgeting, HR data
• GFMIS — Government Fiscal Management Information System
• e-GP — Electronic Government Procurement system
• HR / Document Management Systems — Personnel and document data
• External Data — KPIs from the Bureau of the Budget, OPDC, State Audit Office

2. ETL Process (Extract, Transform, Load)

This is the heart of the Data Warehouse — the process of pulling data from various sources, transforming it into a consistent format, and loading it into the warehouse:

Step	What It Does	Government Example
Extract	Pull data from source systems	Extract disbursement data from ERP and GFMIS every night at 2:00 AM
Transform	Clean, reformat, calculate new values	Standardize agency codes, calculate disbursement % vs. target, group by program/activity
Load	Load transformed data into the Data Warehouse	Insert new records into the Fact_Budget_Execution table daily

3. Data Warehouse Storage

The main storage layer, designed with schemas optimized for analytics:

• Star Schema — A central Fact table (numeric data like disbursement amounts) surrounded by Dimension tables (e.g., fiscal year, agency, budget program)
• Snowflake Schema — Similar to Star but with further normalized Dimensions, suitable for complex hierarchies

4. BI Tools (Analytics Tools)

Tools that query the Data Warehouse to create dashboards and reports:

• Apache Superset — Free, open source — ideal for agencies that want to minimize licensing costs
• Metabase — Open source, user-friendly — create reports without writing SQL
• Power BI — By Microsoft, easy integration with various systems
• Tableau — Beautiful visualizations, ideal for executive presentations

Star Schema — The Most Popular Design

Star Schema is the most widely used Data Warehouse design because it is easy to understand and fast to query. It consists of 2 parts:

Table Type	Purpose	Government Example
Fact Table	Stores measurable values (Measures) such as disbursement amounts, contract counts	Fact_Budget_Execution: date, program_id, agency_id, disbursement_amount, allocated_budget
Dimension Table	Stores context (descriptive data) used for filtering and grouping	Dim_Agency: agency_id, department_name, division, ministry

With this structure, you can instantly answer executive questions such as:

• "What percentage of capital budget did Department A disburse in Q1 vs. the Bureau of the Budget target?"
• "Which project had the highest procurement value in FY2026?"
• "Rank all departments by operating expenditure disbursement rate, highest to lowest"
• "Compare capital disbursement across the last 3 years, broken down by quarter"

Data Warehouse vs. Data Lake

Another term often heard alongside Data Warehouse is Data Lake. Both store large amounts of data, but they are fundamentally different:

Aspect	Data Warehouse	Data Lake
Data Types	Structured (clearly defined schema)	Structured + Semi-structured + Unstructured
Schema Approach	Schema-on-Write (define structure before writing)	Schema-on-Read (define structure when reading)
Data Quality	High — cleaned through ETL	Varies — raw, untransformed data
Best For	Executive reports, dashboards, internal audit	Machine learning, deep analytics, raw document storage
Cost	Higher (requires schema design + ETL)	Lower (store raw data first, transform later)

For government agencies that need executive reporting and audit-readiness for the State Audit Office, a Data Warehouse is the more targeted choice since the data is organized, high-quality, and ready to use immediately.

Why Should Government Agencies Have a Data Warehouse?

1. Cross-Department Executive Reports in Real-Time

Executives at the Director-General or Deputy Director-General level need to see the big picture across the entire organization — not just one department. But in an ERP, each module has its own reports. If you want to see "disbursement results for all departments + procurement status + staffing levels + remaining budget — all on one screen" — you need a Data Warehouse.

2. Support KPIs from the Bureau of the Budget / OPDC

Many government performance indicators relate to budget disbursement rates for both operating and capital expenditure. A Data Warehouse can calculate these KPIs automatically — no need to wait for staff to manually compile data from multiple sources.

3. Multi-Year Trend Analysis

ERP systems typically store the current and previous fiscal year — but a Data Warehouse stores data spanning many years for trend analysis:

• Is the capital disbursement rate improving or declining each year?
• Which types of projects are most likely to be delayed?
• Which expenditure categories consistently have the highest unspent returns?
• How does e-bidding compare to specific procurement methods over time?

4. Audit Readiness for the State Audit Office

A Data Warehouse stores data as Non-Volatile — once entered, data is never modified, with a complete audit trail. This allows agencies to retrieve historical data quickly when the State Audit Office or internal auditors need to review records.

5. Reduce ERP System Load

Running large analytical queries against the ERP database slows down the ERP for daily users. A Data Warehouse solves this by offloading analytics to a separate system, ensuring that daily operations remain unaffected.

Real-World Use Cases in Government

Scenario	Without Data Warehouse	With Data Warehouse
Executive asks: "What is the capital disbursement rate?"	Staff must pull data from ERP + GFMIS, compile in Excel — takes 1-2 days	Open the dashboard, see it instantly — filterable by department, program, quarter
Auditors request 3 years of procurement data	Search through multiple systems and Excel files — may take weeks	Query the Data Warehouse instantly — complete, accurate, auditable
Preparing next year's budget request	Only 1-2 years of data available, lacks supporting evidence	Analyze 5-year spending trends by program and activity, creating data-backed budget proposals
Tracking strategic plan performance	Each department reports in different formats, impossible to consolidate	Real-time dashboard showing actual vs. target for every department and KPI

How to Get Started — For Government Agencies

1. Start with policy questions

   Don't start with "we want a Data Warehouse." Start with "what questions do executives need answered?" — e.g., disbursement rates by quarter, consolidated procurement status, remaining budget by program.

2. Assess ERP data quality

   A Data Warehouse is only as good as its source data. If ERP data is incomplete, inconsistent, or inaccurate (e.g., some departments don't enter all records, some entries are miscategorized), fix the source first.

3. Choose tools appropriate for your agency

   For government agencies concerned about software licensing costs, strong open-source options include:

   • PostgreSQL — Free, open source, handles mid-sized Data Warehouses with no license fees
   • ClickHouse — Column-oriented DB designed specifically for analytics, open source
   • Apache Superset + Metabase — Free, open-source BI tools
4. Design the ETL pipeline

   Define how data will be extracted from the ERP, how it will be transformed, and how frequently it loads into the Data Warehouse (daily? hourly?).

5. Start small — 1 subject area

   Don't try to do everything at once. Start with the subject area of highest demand — such as "budget disbursement" — since every agency must report on it. Then gradually expand to procurement, inventory, and HR.

Saeree ERP and Data Warehouse

Saeree ERP uses PostgreSQL as its core database, which is a key advantage for government agencies looking to build a Data Warehouse:

Advantage	Details
Open Source — No License Fees	Both the ERP database and Data Warehouse can run on PostgreSQL — no duplicate licensing costs, saving budget
Open Data Format	PostgreSQL data can be easily exported — no vendor lock-in, aligned with government Open Data policies
SQL Standard	Uses standard SQL, compatible with all ETL and BI tools — Superset, Metabase, Power BI, and more
Logical Replication	Stream data in real-time from ERP to the Data Warehouse without impacting ERP performance for daily users
Complete Data Structure	Budget, procurement, inventory, accounting, and HR modules have clear relationships — ready to serve as Data Warehouse sources

A Data Warehouse is not an IT project — it is an investment in enabling government agencies to make data-driven policy decisions, report transparently, maintain audit readiness, and align with the country's Digital Government direction.

- Saeree ERP Team

Summary

• Data Warehouse is a data store designed for analytics, not for recording transactions
• Different from a database — stores historical data from multiple systems (ERP, GFMIS, e-GP), optimized for fast reads
• ETL Process is the heart — Extract, Transform, Load data from various sources into the warehouse
• Star Schema is the most popular design — Fact Tables (disbursement amounts) + Dimension Tables (fiscal year, agency, program)
• For government — enables executive reporting, KPI tracking, audit readiness, and data-backed budget planning
• Start with policy questions, not technology — what do executives need to know? Then design accordingly

If your agency is looking for an ERP system with an open database architecture ready to connect with a Data Warehouse, you can schedule a demo or contact our consulting team to assess your organization's readiness.