Maintenance and reliability professionals are increasingly turning to data-driven approaches to enhance operational efficiency and asset longevity. This panel discussion delves into the two paramount challenges we're facing as we navigate the complexities of data utilization: data quality and integration, and the skill gap in data analytics.

Ensuring data quality and seamless integration still poses a significant hurdle. Maintenance and reliability operations generate vast amounts of data from diverse sources, including sensors, equipment logs, and maintenance records. However, inconsistencies, inaccuracies, and fragmentation in data can undermine decision-making processes. The panel will explore strategies for improving data quality, standardizing data formats, and integrating disparate data sources to create a cohesive and reliable data ecosystem.

The skills gap in data analytics also remains a critical issue. While advanced data analytics tools and techniques offer tremendous potential, the lack of personnel with the requisite expertise to leverage these tools effectively hinders progress. This discussion will address the need for upskilling existing staff, attracting new talent with data science proficiency, and fostering a culture of continuous learning within maintenance and reliability teams.

Join us as industry leaders share insights, experiences, and practical solutions to these pressing challenges, paving the way for a more data-savvy and resilient future in maintenance and reliability.

If it’s agreed that assets are important for the business and that data is important for the business to optimise these assets, then it follows that capturing, storing, and making data-led decisions should be of equal importance. However, organisations are now generating enormous amounts of data from their plants and assets, and we are drowning.