Building a safer and more sustainable world
Novade, a construction management software company, partnered with Databricks to modernize its analytics and ML infrastructure, moving from a homegrown Apache Airflow stack to the Databricks Data + AI Platform with Delta Lake, Unity Catalog and MLflow. The move reduced total cost of ownership by 60% and supported a 100% increase in new clients, while enabling ML models for predicting incident risk affecting worker safety and project delivery schedules.
Overview
Novade, a construction management software company, partnered with Databricks to modernize its analytics and ML infrastructure, moving from a homegrown Apache Airflow stack to the Databricks Data + AI Platform with Delta Lake, Unity Catalog and MLflow. The move reduced total cost of ownership by 60% and supported a 100% increase in new clients, while enabling ML models for predicting incident risk affecting worker safety and project delivery schedules.
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Inspect the highlighted sourceThe challenge
Novade relied on a proprietary analytics solution built on open source tools such as Apache Airflow, but as data volumes and use cases expanded, this infrastructure could not scale to deliver the actionable insights and predictive alerts Novade wanted to offer construction customers.
The solution
Novade partnered with Databricks, running a one-month proof of concept before adopting the Databricks Data + AI Platform. Delta Lake gave a unified, medallion-architecture (Bronze, Silver, Gold) view of data for both business reporting and ML model training, with time travel enabling data evolution insight and error correction. Databricks SQL empowered internal users, Power BI connected via Delta Sharing for project dashboards, and Unity Catalog enabled secure, granular-access data sharing with customers. With MLflow, Novade began training and deploying ML models, including initial models predicting incident risk affecting worker safety and project delivery schedules.
Reported business value
Novade reduced total cost of ownership by 60% and grew its new-client acquisition by 100%, freeing resources to explore more use cases, handle larger data sets and pursue more ambitious projects with a relatively small data team.
Sources
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