How a state government secretariat created a data platform consolidating R$ 8 billion in municipal works commitments to support strategic decisions.
Data scattered across multiple government systems was consolidated into a single reliable base, with BI for visualization and support of political and technical decisions on budget commitments for municipal works.
About the project
A state government secretariat faced the challenge of making strategic decisions about budget commitments for municipal works without having a consolidated and reliable view of data. Information was scattered across multiple government systems, making it difficult to track commitments in hundreds of municipalities and creating high reputational and financial risk from decisions based on incomplete data.
The problem that motivated the project
Main challenges faced
- Data scattered across multiple government systems, without consolidated view
- Difficulty tracking works commitments in hundreds of municipalities
- Lack of single reliable base to support political and technical decisions
- High reputational and financial risk from decisions based on incomplete data
- Need for updated information for different profiles (management, technical, political)
How we solved the problem
We developed a complete data and BI platform: automation of data capture from government systems using RPA and integrators, sanitization and organization of data into a structured relational single base, decision-oriented data modeling with clear dimensions (municipality, public agent, work, values, deadlines) and construction of a BI platform with dashboards and reports for different decision profiles in government.
Architecture Highlights
- Data capture automation using RPA/integrators of legacy government systems
- Data sanitization and organization into single relational base for analytics
- Decision-oriented data modeling (dimensions: municipality, public agent, work, values, deadlines)
- BI platform with dashboards and reports for different profiles (management, technical, political)
- Formal process of requirements mapping and data architecture
- Visualization layer that translates complex data into actionable information
The project journey
A structured and predictable process, with incremental deliveries and continuous validation.
Discovery and Mapping
4 weeksWorkshops with stakeholders to understand decision needs, mapping of government systems as data sources, identification of manual processes and analysis of existing data quality.
Data Architecture
6 weeksDesign of decision-oriented data architecture, definition of single relational base, dimensional modeling and specification of ETL/ELT processes for extraction and transformation.
Capture Automation and Sanitization
10 weeksDevelopment of integrators and RPA for data capture automation, implementation of sanitization and validation processes, structuring of single base with consolidated data.
BI Platform
8 weeksConstruction of BI layer with dashboards and reports, creation of visualizations for different user profiles (technical, management, political) and validation with stakeholders.
Delivery and Support
4 weeksPlatform deployment, user training, initial support and adjustments based on feedback. Platform ready to support government strategic decisions.
Results and metrics
Measurable impact of the project in numbers.
Concepts in 30 seconds
Important technical concepts explained simply and connected to the project.
Data architecture for decision making
Structure that transforms scattered raw data across multiple systems into reliable and actionable information for government decision makers.
ETL from legacy systems
Extraction, transformation and loading of data from diverse government systems. Automation that eliminates manual consolidation and ensures continuous updates.
Data sanitization
Process of cleaning, standardizing and validating data. Essential when sources have inconsistent formats, duplications and historical errors.
BI for public management
Dashboards and reports that translate complex data into clear visualizations for different profiles: technical, managers and political authorities.
Lessons and learnings
Reflections and recommendations based on the experience of this project.
Government data has very variable quality — sanitization is half the project
Government decision makers need simple visualizations, not technical complexity
Capture automation avoids dependence on manual processes that change with each administration
Data modeling oriented to business questions, not to source system structure
Related solutions
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