Data Overhaul for AI & Analytics at WM NZ
WM New Zealand has deployed Fivetran to centralise operational, SaaS and high-volume IoT data into Snowflake, aiming to underpin AI and analytics across its fleet and waste management operations.
The company - New Zealand's leading waste and environmental services provider - operates more than 900 trucks and 100 sites nationwide. Data transformations are managed via dbt Cloud from dbt Labs.
Fivetran says the deployment enabled the company to unify a backlog of priority data sources and deliver reliable, timely data at scale, enabling WMNZ to meet a 12-week delivery target.
The deployment addresses a common challenge for asset-intensive organisations undertaking digital transformation: fragmented data environments where operational systems, SaaS platforms and IoT sensor streams exist in silos, preventing consistent analytics at scale.
WMNZ is targeting AI applications including route optimisation, predictive maintenance and progress toward decarbonisation and circular economy goals.
The architecture centres on automated pipelines feeding Snowflake as a central data store, with dbt Cloud handling transformations. Fivetran says the approach eliminates the need for bespoke pipeline development.
“Speed and reliability were non-negotiable for us,” said Lena Jenkins, Chief Digital & Customer Success Officer, WMNZ.
“Fivetran gave us the speed we needed to stand up our data foundation in weeks rather than months. Our team can now focus on data and AI enablement: applying AI and advanced analytics to improve how we operate, how we serve our customers, and how we deliver on our decarbonization and circular economy ambitions.
“That foundation is critical as we apply advanced analytics and AI to improve operational efficiency and environmental outcomes across New Zealand.”
George Fraser, CEO of Fivetran, said many organisations investing in AI remain constrained by fragmented systems and brittle pipelines. He said automating data movement across complex operational and IoT environments was a prerequisite for building a trusted data foundation.