Big Data Analytics for Predictive Maintenance Strategies

The Big Data Analytics for Predictive Maintenance Strategies Course gives maintenance and reliability professionals a structured, data-driven framework for making smarter asset management decisions. Built around the Decision Making Grid (DMG), this course connects Big Data capabilities with proven maintenance methodologies — from Condition-Based Maintenance (CBM) and Reliability Centered Maintenance (RCM) to Total Productive Maintenance (TPM) and Design Out Maintenance.

Participants work directly with existing operational data, applying tools and techniques to real-world maintenance contexts. Whether the goal is reducing unplanned downtime, improving Overall Equipment Effectiveness (OEE), or getting more value from your CMMS, this course provides a practical, results-focused path forward.

This is not a theoretical overview — it is a hands-on course designed to shift how professionals think about, prioritise, and act on maintenance decisions using Big Data.

The Big Data Analytics for Predictive Maintenance Strategies Course is designed to move participants from reactive thinking to a fully strategic, data-informed approach to maintenance management.

• Understand how Big Data features and decision-making intersect within the DMG framework
• Define and apply key performance indicators (KPIs) relevant to maintenance management
• Utilise CMMS data effectively to support maintenance strategy selection
• Identify and prioritise appropriate maintenance strategies using multiple criteria decision-making
• Apply best practice benchmarking and maintenance auditing methods
• Use reliability modelling tools including FMEA, RPN, FTA, RBD, and MCS
• Improve equipment performance through TPM, OEE, and root cause analysis
• Redesign and reconfigure maintenance structures for long-term operational excellence

This Course is designed for professionals involved in maintenance planning, reliability engineering, and asset management who want to apply Big Data for better predictive maintenance outcomes.

This course is suitable for:

• Maintenance managers and engineers seeking structured decision-making frameworks
• Reliability engineers responsible for equipment performance and uptime
• Asset managers working with CMMS and operational data
• Plant and operations managers looking to reduce maintenance costs
• Technical professionals involved in RCM or CBM implementation
• HSE and quality professionals supporting maintenance excellence initiatives
• Data and analytics professionals working within engineering or industrial environments
• Professionals pursuing excellence in Total Productive Maintenance (TPM)

This Course is delivered through a progressive, hands-on learning structure that combines proven frameworks with direct data application. Each day builds on the previous — from foundational strategy selection through to full maintenance system reconfiguration.

Participants are encouraged to bring data from their own organisations to apply the Decision Making Grid and reliability tools in a relevant, practical context.

Delivery methods include:

• Instructor-led sessions covering core concepts, frameworks, and methodologies
• Case study analysis drawing on industry examples of predictive maintenance in action
• Hands-on data exercises applying Big Data analytics to CMMS and maintenance datasets
• Group discussions and peer learning to share operational challenges and best practices
• Reliability modelling workshops using RCM techniques such as FMEA, FTA, and RBD
• Root cause analysis activities using the Ask Why 5 Times approach
• Integration exercises connecting TPM, OEE, and KPIs to real maintenance decisions
• Implementation planning to support direct application back in the workplace

• AZTech Certificate of Completion for delegates who attend and complete the training course