Use Case
Predictive Maintenance with Alteryx One
Reduce unplanned downtime with automated, AI-supported analytics that bring together sensor, IoT, and equipment data for faster, smarter maintenance decisions.
Reduce unplanned downtime with automated, AI-supported analytics that bring together sensor, IoT, and equipment data for faster, smarter maintenance decisions.
Manufacturers invest heavily to keep assets productive and reduce downtime. Predictive maintenance can reduce maintenance costs by 40% and unplanned downtime by up to 50% (Sockeye). However, many programs rely on rigid IoT platforms and disconnected data, making early fault detection difficult. Alteryx One unifies sensor data, machine logs, and maintenance records in a single workflow so engineers can identify warning signs sooner, predict equipment failures, and improve reliability across production lines.
Equipment, sensor, and maintenance data live in silos, slowing analysis and preventing accurate performance tracking.
Incomplete or delayed sensor data reduces accuracy and makes failure predictions inconsistent.
Teams rely on spreadsheets and historical guesswork to identify maintenance needs.
Lack of traceability across logs and IoT data creates uncertainty in AI-driven failure forecasts.
Maintenance and engineering teams use different thresholds, making comparisons across plants unreliable.
Alteryx One unifies sensor data, machine logs, ERP inputs, and maintenance records into a single, automated workflow. Engineers can analyze performance trends, identify early warning signs, and predict equipment failures — without coding. Governance features help ensure every prediction is traceable and validated. Teams collaborate across operations and maintenance to reduce downtime, optimize service schedules, and boost productivity.
Integrated data access
Connects IoT, ERP, and maintenance systems into one governed analytics environment.
Automated workflows
Cleanses and blends data for consistent analysis across lines, assets, and plants.
Advanced analytics & AI
Applies predictive models to detect failure patterns and optimize maintenance schedules.
Governance
Tracks data lineage and assumptions for reliable, auditable maintenance insights.
Shorter maintenance planning cycles across production lines.
Lower unplanned downtime and fewer emergency repairs.
Standardized reporting for asset performance and reliability.
Governed data that improves collaboration between maintenance and operations.
300
telemetry sensors on each race car generate 100K data parameters
“The difference between winning and losing last year was 2% in points. Without partners like Alteryx that have helped us raise the bar in many areas such as reliability, this would never have been possible.”
Dan Keyworth, Director of Business Technology
McLaren Racing
Unifies sensor, machine, and maintenance data for a full picture of equipment performance.
Builds AI-supported failure models that forecast downtime and optimize service timing.
Automates repetitive data prep and monitoring tasks for continuous performance tracking.
Embeds auditability across predictive models so stakeholders can validate every assumption.
Enables engineers, operators, and data teams to share governed workflows across plants, scaling predictive maintenance enterprise-wide.