# Data Analysis

> We analyze and monitor design, site and operational data to support the client's strategic choices, with analysis dashboards that can be linked to the model.

Data Analysis picks up where visualization stops. Data extracted from BIM models are cross-referenced with other sources, such as schedules, cost records, sensors or management systems, and monitored over time. The aim is to answer specific questions: where we are losing time, which work package is over budget, which system uses more energy than expected.

BIMON builds data discretization and enhancement strategies around the client's needs. We get different sources talking to each other to generate new data, and make them accessible to non-technical audiences in a clear, intuitive way. Analysis dashboards can be purely numerical or linked to 3D model navigation.

At Villa Serena and for MIND Milano Innovation District, data analysis and monitoring supported the client's strategic choices. For the new Mexico City Airport the BIM model was used to verify every quantity in the tender estimate and, through time and cost simulations, to optimize the proposed solutions. At Prato hospital the available data were analyzed and connected to the hospital's management system.

You need it when you must choose between options on verifiable data, when you want to monitor an indicator across the whole project or when an operating asset produces data nobody analyzes.

## Deliverables

- Dashboard Data Analysis
- Dashboard Data Analysis & Model Navigation

## Process

1. **Goals** — We set with the client the questions the analysis must answer and the indicators to track.
2. **Source integration** — We connect model data, schedules, costs, sensors and management systems.
3. **Analysis** — We process the data, look for deviations and trends and make them readable in dashboards.
4. **Monitoring** — We update the indicators over time and flag when a value goes out of range.

## FAQ

**Which decisions does Data Analysis support in a BIM project?**

At tender stage, quantity checks and comparison between options, as for Mexico City Airport. On site, control of progress, costs and non-conformities. In operation, energy use and maintenance. At every stage the value lies in comparing data from different sources.

**Do the data need to be structured already?**

It helps, but it is not essential. If models do not follow common rules, the first step is to normalize parameters and classifications with dedicated extractions and scripts. For future projects these rules are best set in the information requirements.

**Is Data Analysis useful for existing buildings too?**

Yes. In operation, data come from the BMS, IoT sensors and maintenance systems, and analyzing them is the basis of an Asset Digital Twin. At Prato hospital, data analysis with AI algorithms supports predictive maintenance and energy savings.