Why Informed Decision-Making Requires a New Set of Solutions
Faced with relentless competitive and economic pressures, businesses today must be able to make rapid, effective decisions, from how to prioritize limited resources to how to best balance costs and risk. In the context of operations and asset management, informed decision-making means knowing exactly when and how to take action to prevent failures, safeguard worker safety, and keep production running.
While condition monitoring provides important insights for these types of decisions, machine data doesn’t necessarily translate into actionable intelligence. To move from reactive firefighting to proactive, informed decisions, organizations need to break down roadblocks to connecting, understanding, and acting on their data.
Why operations and maintenance teams struggle to make fast, effective decisions
Many barriers to informed decision-making relate to how data is captured, processed, and shared. This is especially true for operations and maintenance teams, who face several roadblocks in gaining a complete and accurate view of asset health.
Data silos and integration gaps
For many operations teams, the daily reality involves navigating a fragmented digital landscape where data sits in silos across asset health, maintenance strategy, and work execution tools. This makes it difficult to maintain a clear line of sight from asset strategy to actual execution.
The integration gap is mirrored at the machine level. To monitor asset health, organizations frequently deploy a patchwork of solutions, capturing data via manual portable data collectors, continuously wired sensors, and newer wireless sensors. Because these tools often come from multiple vendors, the information remains trapped in isolated pockets rather than coming together to provide a holistic, plant-wide view.
When these disparate condition monitoring systems fail to integrate to CMMS or EAM platforms, teams are forced to rely on manual workarounds to share data and coordinate activities. This not only slows down decision-making but also severs the digital feedback loop between identifying a machine fault and executing the repair.
Constraints on resources and expertise
Overcoming data silos is a crucial step, but even organizations that successfully connect their data streams often hit another wall: sheer volume.
Operations frequently lack the human resources and specialized expertise required to review and realize value from the massive influx of condition monitoring data now available. It is incredibly difficult for human analysts to manually sift through millions of data points to understand what matters. Without the right tools to automatically triage and analyze this information, this sheer mass of data can cause "analysis paralysis" and hinder effective decision-making rather than enabling it
Disjointed teams and ineffective collaboration
Fast, effective decision-making is impossible in a vacuum; it requires seamless collaboration across the organization. However, it is hard to collaborate when teams use tools that were not built for shared workflows. Engineers, planners, and operators often rely on entirely different systems, leading to duplicated efforts and inconsistent practices.
Without a unified platform to share insights, track progress, and align priorities, it is difficult to translate data into coordinated, timely decisions.
The path to data-driven decision-making
Getting to a place where operations and maintenance teams can make timely, informed decisions relies on a fundamental shift in both how asset data is connected and analyzed.
Consolidation, integration, and collaboration
You cannot make informed decisions if you cannot see the whole picture. Instead of relying on a rigid patchwork of tools, organizations need a modular and composable solution that enables them to unify data and processes for better collaboration and decision making.
Cordant™ addresses this by offering an integrated suite of capabilities across hardware, software, and services that work together seamlessly to fit specific operational needs. Because it is a composable platform, it connects directly with existing EAM and CMMS systems, allowing data to flow freely and ensuring maintenance strategy is directly linked to field execution.
By unifying these workflows, the platform builds collaboration right into its design. Role-specific workspaces allow engineers to co-develop strategies while planners track approvals in the same shared environment. Additionally, with multilingual support in over 20 languages, global teams can work in their preferred language, breaking down regional communication barriers and driving consistency across worldwide operations.
Prescriptive Diagnostics via Purpose-Built AI
To overcome data overload and turn raw inputs into informed decisions, organizations need systems that can connect and analyze data at scale. Cordant™ Machine Health powered by Augury achieves this by combining robust hardware for plantwide remote condition monitoring with purpose-built AI to deliver powerful, actionable insights on asset health. These include:
- Anomaly Detection and Early Warning: Identifying and alerting teams well in advance of a potential failure. This early warning gives facilities crucial time to prepare and manage risk. So instead of reacting to a sudden breakdown, they have time to order necessary spare parts, coordinate field operations, and schedule interventions before there is any impact on production.
- Fault Detection: Pinpointing what is wrong, what is causing it, and how to fix it. Thanks to its massive database of component-specific issues, the AI achieves 99% fault detection accuracy and 95% diagnostic accuracy on rotating equipment.
- Severity Analysis: Categorizing the severity of the fault so leadership knows exactly when to take action and how to prioritize resources.
- Expert Intelligence: Providing an additional layer of support by routing complex data to an on-demand team of expert vibration analysts and reliability engineers who vet the anomalies.
While integrating AI into your asset management and maintenance practices may seem complex, solutions like Cordant™ Machine Health come with pre-trained, purpose-built AI models which makes it possible to deploy faster and detect failures early on. This results in short Time-to-Value and higher ROI vs general-purpose AI which can take months to deploy and years to deliver value.
Empowering informed decisions at scale
You cannot run a modern reliability program on disconnected data and isolated workflows. As the demands on production continue to rise, achieving plant-wide reliability requires a fundamental shift in how information is shared and analyzed. This means moving beyond the limitations of legacy systems and embracing composable, modular solutions like Cordant™ with purpose-built AI that empowers teams to better align their efforts and make confident, informed decisions at scale.
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