Data to Decisions: Creating Actionable Insights from Industrial Equipment Telemetry
Data to Decisions: Creating Actionable Insights from Industrial Equipment Telemetry
Jun 5, 2025
—
TagoIO Team
Industrial companies today face a paradox. They collect vast amounts of equipment data through sensors and IoT systems, yet many struggle to translate this information into tangible business results. The gap between data acquisition and financial outcomes represents one of the most significant challenges in industrial operations today.
This challenge isn't just about technology—it's about establishing a clear pathway from equipment telemetry to measurable business impact. While industrial organizations have increased their IoT investments over the past three years, only a small percentage achieve significant financial returns from these investments.
In this article, we'll explore how industrial companies can move beyond data collection to achieve measurable business outcomes through a structured value realization framework. The approach transforms how organizations think about equipment data—not as a technical achievement but as a business asset with quantifiable returns.
The Data Collection Gap
Industrial enterprises have enthusiastically embraced the potential of connected equipment. Modern factories and plants often feature thousands of sensors monitoring everything from temperature and vibration to energy consumption and production rates. This proliferation of monitoring capabilities has created unprecedented visibility into operations.
However, this visibility hasn't automatically translated into value. Consider these common scenarios:
Scenario 1: A manufacturing plant collects terabytes of equipment performance data but can't connect this information to product quality variations.
Scenario 2: A utility company monitors its field equipment in real-time but still experiences preventable outages because warning signals get lost in data noise.
Scenario 3: An industrial processor tracks dozens of process variables but cannot determine which adjustments would maximize yield and minimize energy use.
These situations illustrate what researchers call the "data-to-value gap." Organizations have mastered data collection but struggle with data utilization.
Value Creation vs. Value Realization
To address this gap, we must first understand a critical distinction highlighted by Professor Barbara Wixom in her book "Data Is Everybody's Business." Wixom makes a compelling argument that organizations need to differentiate between two related but distinct concepts:
Value Creation occurs when data improves operational decisions or processes. Examples include detecting equipment anomalies earlier or optimizing maintenance schedules based on actual conditions.
Value Realization happens when these improvements deliver measurable financial returns. This might manifest as extended equipment lifespans, reduced downtime costs, or lower warranty claims.
<WE COULD RECREATE AN IMAGE SIMILAR TO THE BOOK WITH THE TREE EXAMPLE)
This distinction explains why many industrial companies claiming to be "data-driven" cannot demonstrate bottom-line impact. They create value through better operational visibility but fail to realize this value in financial terms.
For industrial equipment telemetry, this framework reveals that the ultimate goal isn't better data collection or even better insights—it's better business outcomes stemming from these insights.
The Industrial Telemetry Journey
To transform equipment telemetry into financial outcomes, organizations must understand the complete journey from raw signals to business impact. This progression involves several distinct stages:
Data Acquisition: Collecting signals from equipment through sensors and connectivity solutions
Data Processing: Converting raw signals into standardized, usable formats
Information Creation: Transforming processed data into meaningful information through contextualization
Insight Development: Analyzing information to discover patterns, anomalies, and opportunities
Decision Support: Presenting insights in actionable formats for human or automated decisions
Operational Change: Implementing different actions based on these decisions
Business Impact: Measuring the financial effects of these changed operations
Value Accounting: Attributing financial outcomes specifically to data-driven decisions
Most industrial IoT implementations focus heavily on stages 1-3 while underinvesting in stages 4-8. Without this complete chain, companies create interesting dashboards and reports but fail to generate measurable returns.
Consider a conceptual framework we can call the "Equipment Telemetry Value Chain." At each stage, specific capabilities and responsibilities must be fulfilled:
Technical Domain (Stages 1-3)
Sensor deployment and reliability
Connectivity infrastructure
Data storage and normalization
Basic visualization and alerting
Business Domain (Stages 4-8)
Pattern recognition and anomaly detection
Decision frameworks and governance
Operational process modification
Financial impact tracking
The most successful industrial companies establish clear handoffs between these domains, ensuring that technical achievements translate into business results.
Implementation Framework
How can industrial organizations systematically move from data to decisions to dollars? Based on successful implementations across manufacturing, energy, and utilities, the following framework offers a structured approach:
Step 1: Begin with Business Objectives
Rather than starting with available data, begin with specific business outcomes your organization needs to improve. Examples include:
Reducing unplanned downtime by 20%
Extending equipment lifecycle by 15%
Decreasing maintenance costs by 18%
Improving energy efficiency by 12%
These objectives provide direction for your telemetry program and establish clear targets for measuring success.
Step 2: Map Decision Points to Outcomes
Identify the specific operational decisions that influence your targeted outcomes. For each business objective, determine:
Who makes decisions affecting this outcome?
What information would improve these decisions?
How frequently are these decisions made?
What constraints limit decision quality today?
This mapping creates a "decision inventory" that connects business goals to daily operations.
Step 3: Define Information Requirements
Based on your decision inventory, determine what information would enable better choices. This includes:
Required data types and sources
Necessary data quality and frequency
Contextual information needed
Format and timing requirements
By working backward from decisions to information needs, you ensure collected data serves a specific purpose.
Step 4: Establish Measurement Systems
Develop mechanisms to measure both the implementation of data-driven decisions and their business impact:
Decision compliance metrics (Are insights being acted upon?)
Operational improvement indicators (Are processes changing?)
Financial outcome tracking (Are changes delivering value?)
These measurement systems create accountability and enable continuous improvement.
Step 5: Build Technical and Process Infrastructure
With clear requirements established, develop the technical and organizational capabilities required:
Sensor deployment and connectivity solutions
Analytics and visualization tools
Decision support systems
Process modification mechanisms
Skill development programs
TagoIO's IoT platform provides an ideal foundation for this infrastructure, enabling seamless data collection, analysis, and action triggering across industrial environments.
Step 6: Create Feedback Loops
Establish mechanisms to improve both data quality and decision execution continuously:
Regular reviews of decision quality
Validation of causal links between decisions and outcomes
Refinement of analytics models
Adjustment of measurement systems
These loops ensure your telemetry system evolves as your organization learns.
Practical Next Steps
For industrial organizations looking to advance their equipment telemetry programs, consider these actionable recommendations:
Audit Current Value Realization
Conduct an honest assessment of your current telemetry program. Are you collecting data that never influences decisions? Are you making improved decisions that don't change operations? Are you changing operations without measuring financial impact? Identifying these gaps is the first step toward closing them.
Establish a Value Realization Office
Create a small cross-functional team responsible for tracking the journey from data to financial outcomes. This group should include both technical and business stakeholders with the authority to address barriers to value realization.
Start Small but Complete
Rather than expanding data collection broadly, select one high-value equipment type or process. Apply the complete value chain to this limited scope, from sensors to financial outcomes. This creates a template for broader application while delivering immediate returns.
Build Equipment Telemetry Use Cases
Develop detailed use cases that specify not just what data to collect, but how it will influence decisions, change operations, and deliver value. Document these cases as part of your program governance.
Modernize Your Telemetry Infrastructure
Consider whether your current technology stack supports the complete value realization journey. TagoIO's industrial monitoring solutions provide the complete infrastructure needed for data collection, analysis, and operational integration, with pre-built templates specifically designed for industrial equipment.
Conclusion
The true potential of industrial equipment telemetry lies not in data volume but in business impact. By implementing a structured approach that connects technical capabilities to financial outcomes, organizations can transform their IoT investments from technical experiments to strategic assets.
The distinction between value creation and value realization offers a powerful lens for evaluating telemetry programs. Creating dashboards and insights creates potential value, but only realized value—measured in financial terms—justifies continued investment.
As industrial operations become increasingly data-intensive, the organizations that thrive will be those that establish clear pathways from signals to decisions to dollars. They'll recognize that the purpose of equipment telemetry isn't better monitoring—it's better business performance.
Industrial companies today face a paradox. They collect vast amounts of equipment data through sensors and IoT systems, yet many struggle to translate this information into tangible business results. The gap between data acquisition and financial outcomes represents one of the most significant challenges in industrial operations today.
This challenge isn't just about technology—it's about establishing a clear pathway from equipment telemetry to measurable business impact. While industrial organizations have increased their IoT investments over the past three years, only a small percentage achieve significant financial returns from these investments.
In this article, we'll explore how industrial companies can move beyond data collection to achieve measurable business outcomes through a structured value realization framework. The approach transforms how organizations think about equipment data—not as a technical achievement but as a business asset with quantifiable returns.
The Data Collection Gap
Industrial enterprises have enthusiastically embraced the potential of connected equipment. Modern factories and plants often feature thousands of sensors monitoring everything from temperature and vibration to energy consumption and production rates. This proliferation of monitoring capabilities has created unprecedented visibility into operations.
However, this visibility hasn't automatically translated into value. Consider these common scenarios:
Scenario 1: A manufacturing plant collects terabytes of equipment performance data but can't connect this information to product quality variations.
Scenario 2: A utility company monitors its field equipment in real-time but still experiences preventable outages because warning signals get lost in data noise.
Scenario 3: An industrial processor tracks dozens of process variables but cannot determine which adjustments would maximize yield and minimize energy use.
These situations illustrate what researchers call the "data-to-value gap." Organizations have mastered data collection but struggle with data utilization.
Value Creation vs. Value Realization
To address this gap, we must first understand a critical distinction highlighted by Professor Barbara Wixom in her book "Data Is Everybody's Business." Wixom makes a compelling argument that organizations need to differentiate between two related but distinct concepts:
Value Creation occurs when data improves operational decisions or processes. Examples include detecting equipment anomalies earlier or optimizing maintenance schedules based on actual conditions.
Value Realization happens when these improvements deliver measurable financial returns. This might manifest as extended equipment lifespans, reduced downtime costs, or lower warranty claims.
<WE COULD RECREATE AN IMAGE SIMILAR TO THE BOOK WITH THE TREE EXAMPLE)
This distinction explains why many industrial companies claiming to be "data-driven" cannot demonstrate bottom-line impact. They create value through better operational visibility but fail to realize this value in financial terms.
For industrial equipment telemetry, this framework reveals that the ultimate goal isn't better data collection or even better insights—it's better business outcomes stemming from these insights.
The Industrial Telemetry Journey
To transform equipment telemetry into financial outcomes, organizations must understand the complete journey from raw signals to business impact. This progression involves several distinct stages:
Data Acquisition: Collecting signals from equipment through sensors and connectivity solutions
Data Processing: Converting raw signals into standardized, usable formats
Information Creation: Transforming processed data into meaningful information through contextualization
Insight Development: Analyzing information to discover patterns, anomalies, and opportunities
Decision Support: Presenting insights in actionable formats for human or automated decisions
Operational Change: Implementing different actions based on these decisions
Business Impact: Measuring the financial effects of these changed operations
Value Accounting: Attributing financial outcomes specifically to data-driven decisions
Most industrial IoT implementations focus heavily on stages 1-3 while underinvesting in stages 4-8. Without this complete chain, companies create interesting dashboards and reports but fail to generate measurable returns.
Consider a conceptual framework we can call the "Equipment Telemetry Value Chain." At each stage, specific capabilities and responsibilities must be fulfilled:
Technical Domain (Stages 1-3)
Sensor deployment and reliability
Connectivity infrastructure
Data storage and normalization
Basic visualization and alerting
Business Domain (Stages 4-8)
Pattern recognition and anomaly detection
Decision frameworks and governance
Operational process modification
Financial impact tracking
The most successful industrial companies establish clear handoffs between these domains, ensuring that technical achievements translate into business results.
Implementation Framework
How can industrial organizations systematically move from data to decisions to dollars? Based on successful implementations across manufacturing, energy, and utilities, the following framework offers a structured approach:
Step 1: Begin with Business Objectives
Rather than starting with available data, begin with specific business outcomes your organization needs to improve. Examples include:
Reducing unplanned downtime by 20%
Extending equipment lifecycle by 15%
Decreasing maintenance costs by 18%
Improving energy efficiency by 12%
These objectives provide direction for your telemetry program and establish clear targets for measuring success.
Step 2: Map Decision Points to Outcomes
Identify the specific operational decisions that influence your targeted outcomes. For each business objective, determine:
Who makes decisions affecting this outcome?
What information would improve these decisions?
How frequently are these decisions made?
What constraints limit decision quality today?
This mapping creates a "decision inventory" that connects business goals to daily operations.
Step 3: Define Information Requirements
Based on your decision inventory, determine what information would enable better choices. This includes:
Required data types and sources
Necessary data quality and frequency
Contextual information needed
Format and timing requirements
By working backward from decisions to information needs, you ensure collected data serves a specific purpose.
Step 4: Establish Measurement Systems
Develop mechanisms to measure both the implementation of data-driven decisions and their business impact:
Decision compliance metrics (Are insights being acted upon?)
Operational improvement indicators (Are processes changing?)
Financial outcome tracking (Are changes delivering value?)
These measurement systems create accountability and enable continuous improvement.
Step 5: Build Technical and Process Infrastructure
With clear requirements established, develop the technical and organizational capabilities required:
Sensor deployment and connectivity solutions
Analytics and visualization tools
Decision support systems
Process modification mechanisms
Skill development programs
TagoIO's IoT platform provides an ideal foundation for this infrastructure, enabling seamless data collection, analysis, and action triggering across industrial environments.
Step 6: Create Feedback Loops
Establish mechanisms to improve both data quality and decision execution continuously:
Regular reviews of decision quality
Validation of causal links between decisions and outcomes
Refinement of analytics models
Adjustment of measurement systems
These loops ensure your telemetry system evolves as your organization learns.
Practical Next Steps
For industrial organizations looking to advance their equipment telemetry programs, consider these actionable recommendations:
Audit Current Value Realization
Conduct an honest assessment of your current telemetry program. Are you collecting data that never influences decisions? Are you making improved decisions that don't change operations? Are you changing operations without measuring financial impact? Identifying these gaps is the first step toward closing them.
Establish a Value Realization Office
Create a small cross-functional team responsible for tracking the journey from data to financial outcomes. This group should include both technical and business stakeholders with the authority to address barriers to value realization.
Start Small but Complete
Rather than expanding data collection broadly, select one high-value equipment type or process. Apply the complete value chain to this limited scope, from sensors to financial outcomes. This creates a template for broader application while delivering immediate returns.
Build Equipment Telemetry Use Cases
Develop detailed use cases that specify not just what data to collect, but how it will influence decisions, change operations, and deliver value. Document these cases as part of your program governance.
Modernize Your Telemetry Infrastructure
Consider whether your current technology stack supports the complete value realization journey. TagoIO's industrial monitoring solutions provide the complete infrastructure needed for data collection, analysis, and operational integration, with pre-built templates specifically designed for industrial equipment.
Conclusion
The true potential of industrial equipment telemetry lies not in data volume but in business impact. By implementing a structured approach that connects technical capabilities to financial outcomes, organizations can transform their IoT investments from technical experiments to strategic assets.
The distinction between value creation and value realization offers a powerful lens for evaluating telemetry programs. Creating dashboards and insights creates potential value, but only realized value—measured in financial terms—justifies continued investment.
As industrial operations become increasingly data-intensive, the organizations that thrive will be those that establish clear pathways from signals to decisions to dollars. They'll recognize that the purpose of equipment telemetry isn't better monitoring—it's better business performance.
