What Is Really Driving Your Programs?

Introduction

In today’s globalized market, Mechanical Integrity (MI) and Reliability programs are becoming increasingly more visible due to the impact they can have on a facility and, in turn, an organization. When effectively adopted and utilized, MI and Reliability programs result in safer, more efficient operations for facilities.  In addition to becoming more visible in the market, these programs have also grown larger and more complex. Risk-Based Inspection (RBI), Integrity Operating Windows (IOWs), special emphasis programs, Reliability-Centered Maintenance (RCM) and Predictive Maintenance technologies are implemented throughout facilities around the world. Couple these programs with the ever-expanding innovations in data science, computational power, and new technologies and it’s easy to see why more organizations are looking to these analytics to improve availability and safety and lower risk and cost.

However, as most people in industry can attest, not all MI and Reliability programs are created equal. These programs vary based on several determining factors, such as implementation strategies and levels of maturity.  Regardless of how a program is structured, there is one common denominator they all require to function – data.  For each of these programs to be effective, they must be fueled by quality and up-to-date data. High quality and up-to-date data are important not only because they help these programs function, but also because these programs produce additional data that is often used by other programs and business processes. For example, IOWs should be developed based on an effective degradation assessment and in partnership with an RBI program. As the program matures, the facility should utilize these IOWs to continually update the RBI program to ensure its effectiveness over time.

What are challenges with data?

You’ve probably heard the phrase “Data is the new oil,” which was coined in 2006 by mathematician Clive Humby. This mindset is becoming more prevalent throughout the world with large technology companies whose business models and products are based on collecting, analyzing, and learning from massive amounts of data to provide value to their customers.

However, if raw data is not properly collected, organized, and processed into useful information, then...