Home > Workload Solutions > Data Analytics > White Papers > White Paper—Cloud Native Splunk Enterprise with SmartStore—Predictive Maintenance for IT Operations > Challenges in predictive maintenance
While it can seem difficult to get started with predictive maintenance, the challenges are well understood, and organizations can learn from existing best practices. Here, for example, are several of the challenges in implementing predictive maintenance that can be overcome using Splunk.
It can be challenging to discern real business value from large volumes of data across IT, operational technology (OT), and Internet of Things (IoT) assets. Data is often isolated in informational and operational silos with little opportunity for users to correlate or collaborate across teams and domains. The analytics required to perform effective predictive maintenance requires convergence and some democratization of data.
Managing IT operations is a balance. It takes years of experience to be able to detect anomalies in asset behavior and prioritize the alarms and alerts that matter. To support predictive maintenance, organizations can use three methodologies. They can choose to incorporate their own expertise into the analytics technology. They can adopt technologies preprogrammed with analytics to support predictive maintenance. Or, they can adopt intelligent and adaptive approaches using machine learning and artificial intelligence.
The ability to diagnose issues without affecting production is critical to optimize operations and minimize unplanned downtime. In most IT environments, it is challenging to combine and correlate information across systems that use disparate communications protocols and a wide range of data formats. Organizations may also have solutions from multiple technology vendors rolled out on a site-by-site or project-by-project basis. All these factors result in data silos and swivel-chair integrations. The ability to leverage data across these silos in real time is imperative to accurately predict future conditions.
Operations teams often struggle to catch up with their IT counterparts in their technology stack. Despite the factory floor’s interest in taking advantage of transformative technology trends, modernization often comes with potential risk that impacts security, compliance, and availability SLAs. For example, integrating OT systems with IT environments could expose both to new security threats.
Finally, a strong resistance to change may be prevalent in personnel who are highly trained and entrenched in operating procedures. Digitally transformed industrial operations benefit from pilot projects outside the production environment, analytics best practices, and training programs that emphasize value to the operators and positive business outcomes.