Using Data to Drive Maintenance Decisions

In many organizations, maintenance decisions are still driven by habit, intuition, or legacy practices. Work orders are executed because “that’s how it’s always been done,” preventive tasks are scheduled without clear justification, and failures are investigated only after they cause disruption.

But the industrial landscape is changing. Asset-intensive organizations now generate vast volumes of operational data—from sensors, condition monitoring systems, CMMS platforms, inspection reports, and operator observations. The question is no longer whether data exists. The real question is whether organizations are actually using that data to make better maintenance decisions.

Organizations that effectively leverage maintenance data consistently outperform those that do not. Research suggests that data-driven maintenance strategies can reduce unplanned downtime by up to 30–50% while lowering maintenance costs by 10–20% (McKinsey & Company, 2018). In short, data turns maintenance from a reactive cost center into a strategic contributor to operational performance.

The Shift from Reactive to Insight-Driven Maintenance

Historically, maintenance evolved through three broad phases:

1. Reactive maintenance – fix it when it breaks.
2. Preventive maintenance – scheduled tasks based on time or usage.
3. Predictive and data-driven maintenance
   1. Interventions triggered by condition, performance trends, and analytics.

Most organizations today operate somewhere between preventive and predictive maintenance. The problem is not technology—it is the effective use of information.

A modern maintenance function should continuously answer four core questions:

1. What assets fail most frequently?
2. What failures cause the greatest operational impact?
3. What maintenance tasks actually prevent those failures?
4. Where should we invest resources to deliver the greatest reliability improvement?

Without reliable data, these questions are answered by opinion. With data, they are answered with evidence.

The Foundations of Data-Driven Maintenance

Data-driven decision making begins with structured information. If failure data, work orders, and asset hierarchies are poorly defined, analytics will produce misleading conclusions. As the well-known management principle states: “You cannot manage what you cannot measure.” (Kaplan & Norton, 1996).

Several elements are critical:

1. Asset hierarchy and data structure
A well-defined asset hierarchy in the CMMS or EAM system enables accurate tracking of failures, maintenance costs, and reliability trends. Without this structure, organizations cannot reliably identify problem assets or systems.

2. Failure coding and reliability data
Standardized failure codes allow organizations to analyze failure patterns and identify root causes. Standards such as ISO 14224 provide guidance on collecting reliability and maintenance data for industrial equipment (ISO, 2016).

3. Data quality and discipline
Incomplete work orders, vague failure descriptions, or inconsistent coding undermine the entire process. Data quality is often the single biggest barrier to effective analytics.

4. Analytics and visualization
Dashboards, reliability models, and statistical analysis convert raw data into insights. These tools highlight trends that would otherwise remain invisible.

From Data to Action

Collecting data is only the first step. The real value comes when insights lead to action.

For example, consider a plant experiencing frequent pump failures. Maintenance teams may initially assume the pumps are poorly designed or operating outside specification. However, analysis of work order data might reveal that the failures occur shortly after maintenance interventions—indicating installation errors or alignment issues.

This insight changes the solution completely. Instead of replacing pumps, the organization invests in improved maintenance procedures and technician training.

This is the power of data-driven decision making.

Similarly, reliability analytics can help organizations:

• Prioritize critical assets using risk-based approaches
• Optimize preventive maintenance intervals
• Identify bad actors in the asset population
• Evaluate the effectiveness of maintenance strategies
• Support predictive maintenance and condition monitoring programs
  

The result is a maintenance strategy that is not based on guesswork, but on measurable performance and evidence.

The Role of Digital Transformation

Industry 4.0 technologies are accelerating this shift. Sensors, Industrial Internet of Things (IIoT) platforms, and advanced analytics now provide real-time insights into asset health and performance.

Predictive analytics can identify abnormal equipment behavior long before failure occurs. Machine learning models can analyze vibration, temperature, and process data to detect emerging issues. Digital twins can simulate asset performance under different operating conditions.

However, technology alone does not solve the problem. Successful organizations combine digital tools with strong maintenance fundamentals—structured data, reliability methodologies, and disciplined work management.

As Davenport and Harris argue, organizations that compete on analytics develop a culture where decisions are systematically informed by data rather than intuition (Davenport & Harris, 2017).

Building a Data-Driven Maintenance Culture

Adopting data-driven maintenance requires more than software implementation. It requires cultural change.

Maintenance teams must understand that accurate data capture is not administrative overhead—it is the foundation of better decisions. Leaders must encourage curiosity, asking not only what happened, but also why it happened and what the data is telling us.

Over time, this mindset transforms maintenance from a reactive function into a strategic reliability partner for operations.

💬 Discussion
How effectively is your organization using maintenance data today?

Consider these questions:

• Do you have reliable failure data in your CMMS or EAM system?
• Are maintenance strategies based on evidence—or historical practice?
• Which assets generate the most maintenance cost, and do you know why?
• Is your organization capturing the right data to support predictive maintenance?

Share your experience in the comments.

• What has been the biggest challenge—or biggest success—when using data to improve maintenance performance?

This article is part of a series exploring how data, digital technologies, and advanced analytics are reshaping modern maintenance and reliability management.

References

Davenport, T. H. and Harris, J. G. (2017) Competing on Analytics: The New Science of Winning. Boston: Harvard Business Review Press.

ISO (2016) ISO 14224: Petroleum, Petrochemical and Natural Gas Industries — Collection and Exchange of Reliability and Maintenance Data for Equipment. Geneva: International Organization for Standardization.

Kaplan, R. S. and Norton, D. P. (1996) The Balanced Scorecard: Translating Strategy into Action. Boston: Harvard Business School Press.

McKinsey & Company (2018) The Next Frontier of Maintenance: Predictive and Data-Driven Operations. McKinsey Global Institute.