As businesses increasingly rely on IoT devices to run their operations and serve their customers, they’re inundated with large volumes of unstructured data. From marine fleets to food manufacturing, tracking large streams of data is challenging enough—but how do you monitor the health of your entire data infrastructure?
Data observability has emerged as the solution for many data operations teams. These systems enable data engineers to monitor, troubleshoot, and optimize data pipelines to improve the performance and integrity of IoT environments.
In this guide, we’ll unpack what data observability is, its relationship to data management in IoT systems, and explore best practices for selecting and implementing a data observability platform.
What is data observability?
Data observability is the practice of maintaining comprehensive visibility into the health, performance, and behavior of data across systems and pipelines in your IoT environment(s).
Put simply, data observability ensures your data is consistent, accurate, and flowing correctly through your system in real time.
For IoT developers and data engineers, data observability is instrumental in preventing data-related issues from slipping through the cracks, especially for teams that are managing large, interconnected systems.
The 5 pillars of data observability
Five core pillars make up data observability, offering a holistic understanding of the health and reliability of data as it flows through an IoT system:
- Data freshness: How up-to-date the data is and whether it’s reaching the system in a timely manner. Monitoring freshness helps teams detect lags or delays, ensuring a steady stream of current information across all connected devices.
- Data volume: The amount of data flowing through the system. Sudden spikes or drops could indicate a system malfunction, an interrupted data stream, or an underlying issue that needs attention.
- Data schema: The organization, format, and structure of data as it’s stored and processed. Schema observability makes sure that data structures remain consistent across the IoT system, helping avoid disruptions in downstream processes or data mismatches.
- Data distribution: Involves tracking patterns and statistical anomalies within data, helping teams understand if the data matches expected behaviors. Monitoring data distribution helps detect outliers, unexpected trends, or faulty readings and address potential problems before they escalate.
- Lineage and traceability: Lineage provides visibility into the journey of data, from its source to its current state, while traceability helps teams understand how data is modified to verify that any transformations are accurate and intended. Clear lineage allows you to confidently trace back issues to their source.
Data observability vs. data monitoring
It can be hard to differentiate between data observability and data monitoring when many people use the terms interchangeably, but they serve different purposes. Data monitoring involves tracking specific metrics and sending alerts reactively when issues occur, while data observability is a more holistic, proactive approach to understanding and managing the health of your entire data ecosystem.
Data observability builds upon and extends the capabilities of data monitoring to support more comprehensive management of data systems.
Imagine a fleet of sensors collecting temperature data across a facility:
- Data monitoring alerts you if a sensor stops sending data so you can troubleshoot the problem, which may require a facility shutdown.
- Data observability enables you to spot trends earlier—like a gradual delay in data transmission—that could predict a problem before it fully disrupts operations.
What about data quality and data governance?
Data observability also stands apart from related concepts like data quality and data governance:
- Data quality and data observability are complementary. Data quality evaluates whether a set of data meets standards for accuracy, consistency, reliability, completeness, and timeliness. Data observability can enhance data quality through real-time insights and proactive issue detection, helping make data assets more reliable.
- Data governance involves developing and enforcing rules that govern data usage and ownership in an organization, while data observability involves tracking the health of data in motion.
Why is data observability important for IoT systems?
When IoT environments are foundational to business operations, maintaining consistent, high-quality data systems is essential to the bottom line—whether your business depends on oven and inventory sensors in an automated restaurant kiosk or reliable HVAC equipment in storage facilities.
Here’s a look at the different ways that data observability helps facilitate effective data management in IoT:
Protecting data integrity and consistency
In IoT environments, data integrity means maintaining accurate and unaltered data as it moves from one device to another. Data observability helps preserve integrity by catching potential issues as they develop in real time, instead of after the fact.
This proactive approach minimizes data downtime—periods of time when data isn’t available or usable because of errors, delays, or corruption. By avoiding data downtime, companies can reduce operational delays or disruptions resulting from failing points in data pipelines.
Let’s see how this could play out in an actual IoT environment:
In a smart agriculture operation, an IoT network of sensors monitors soil moisture, temperature, and nutrient levels across different fields. One of the fields starts experiencing connectivity issues from a local network outage. Data observability tools monitoring data freshness and volume immediately notify the engineering team of a drop in data volume and a pause in fresh data from moisture sensors in that area. The team quickly reroutes network traffic to a backup channel and restores data flow before it affects irrigation and fertilization processes.
Supporting efficient troubleshooting
When data issues crop up, pinpointing the root cause can seem like finding a needle in a haystack. Data observability gives teams visibility into every stop in the data pipeline so they can troubleshoot more quickly and accurately.
In IoT environments where connectivity is often unstable and data flows can be interrupted by network issues, power fluctuations, or device malfunctions, this end-to-end visibility is critical.
Data observability tools can help data teams answer questions like:
- Where did data get delayed or interrupted?
- Are there patterns indicating recurring issues in data flow?
- How frequently are errors or anomalies occurring?
- Which devices or sensors are more likely to experience connectivity issues?
By answering these questions quickly, engineers can resolve issues faster, minimizing disruptions in the data flow and keeping systems running without a hitch.
Optimizing system performance
A data observability strategy also helps teams optimize data flow and bandwidth and improve resource allocation:
- With visibility into data volume and patterns, teams can manage and optimize data flow by avoiding unnecessary data transmission that could strain bandwidth. By adjusting data collection parameters, the system will only capture and transmit relevant data, which helps reduce network load and conserve resources.
- Teams gain insight into how resources like storage, processing power, and bandwidth are being used across the IoT network. This data can help redirect resources to areas where they’re most needed.
Challenges of implementing data observability in IoT
Implementing data observability in IoT systems can present a number of challenges for data teams, especially without a unified strategy or platform:
- Scale and complexity of IoT networks: These environments often include thousands of interconnected devices that each produce their own data streams. This sheer scale and complexity requires a high level of organization and technology.
- Data volume and velocity: When many devices are constantly collecting a massive volume of data at high velocity, it can strain resources—requiring robust infrastructure to handle this high throughput.
- Variety of devices and data types: Businesses often integrate diverse IoT devices that produce different types and formats of data—requiring highly adaptable data observability tools.
- Connectivity issues: IoT environments aren’t always in ideal, stable locations, and devices may face intermittent connectivity that disrupts data flow. It’s important for data observability tools to solve for these inconsistencies with features like data caching.
- Resource constraints of IoT devices: Many devices have limited storage, processing power, and battery life—restricting their capacity to run continuous data observability. Teams must take caution to avoid overburdening devices and compromising system functionality.
Best practices for implementing data observability in IoT systems
To overcome these challenges and achieve effective data observability in IoT systems, here are some best practices:
Universal compatibility and integration
Make sure the data observability solution you choose supports a wide range of hardware and sensors. Viam Data, for example, offers universal hardware and sensor compatibility, so you can easily retrieve data from diverse IoT devices.
Selective data capture
Not all data generated by IoT devices is equally valuable. Implement selective data capture to focus on the most critical information by:
- Using filtering mechanisms to capture only relevant data, reducing storage and bandwidth costs.
- Setting up anomaly detection to identify and prioritize unusual patterns or events.
Viam’s platform allows you to selectively capture data so that you only synchronize the information you need.
Efficient data synchronization
Develop and implement a comprehensive data synchronization strategy that involves:
- Storing data locally on IoT devices and syncing with the cloud whenever bandwidth allows.
- Leveraging specialized strategies for image data to ensure reliable performance even with intermittent connections.
Centralized data visualization
Implement a centralized dashboard for real-time data visualization across your pipelines:
- Customizable dashboards help you present IoT data in an easily digestible format.
- Connect tools like Tableau or Grafana to create powerful visualizations of your sensor data.
Viam’s platform offers centralized data management for IoT with integrations for data visualization, allowing data teams to monitor and act on IoT data in real-time.
Query optimization
Develop querying mechanisms to extract relevant and valuable insights by:
- Implementing a queryable cloud storage solution for your IoT data.
- Optimizing your queries to handle large volumes of time-series data efficiently.
Viam Data provides queryable cloud storage, allowing you to run powerful queries directly on your sensor data without complex data manipulation.
Data governance and security
Establish robust data governance and security measures by taking steps to:
- Ensure data integrity and organization on smart machines.
- Implement strict access controls and permissions.
- Adhere to international protocols for data security and compliance.
Make sure the data observability technology you choose includes built-in data governance and security features to protect your IoT data.
Scalability and flexibility
Choose a data observability solution that can scale with your IoT infrastructure. Consider things like:
- Opting for cloud-based solutions that can handle growing data volumes.
- Making sure your system can adapt to new types of sensors and data formats.
These best practices will enable your team to develop an exhaustive, all-encompassing data observability system for your IoT infrastructure. When making decisions about data observability and data management in IoT, always balance these recommendations with the unique needs and requirements of your environments and devices.
Data observability use cases
How does data observability work on the ground? Let’s explore three different use cases to get a better idea.
Optimizing IoT data for real-time analytics in marine fleet management
You’re the lead data engineer for a global shipping company that operates a fleet of hundreds of cargo vessels traversing major international routes. The company is facing unpredictable maintenance issues and inefficient fuel consumption, plus they’re struggling to meet environmental regulations.
Your main responsibility is to make sure the data pipeline can handle the volume and velocity of incoming data from various shipboard sensors while maintaining high accuracy. By implementing a data observability solution, you’re able to:
- Monitor real-time performance of shipboard systems to maintain optimal operation and early detection of potential issues.
- Track fuel consumption and emissions data across different vessels and routes to optimize efficiency and facilitate regulatory compliance.
- Identify potential maintenance needs before they lead to costly breakdowns or delays.
Monitoring data flow from IoT sensors in a smart factory
Imagine you run a large automotive manufacturing plant that recently implemented IoT sensors across its production line. The sensors monitor different aspects of the manufacturing process, from temperature, to vibration, and production speed.
As the plant manager, your main priority is to make sure the sensor data is flowing correctly and providing accurate information at all times. So you implement a data observability solution that allows you to:
- Monitor the data pipeline in real-time to verify that the system is receiving and processing data from all sensors without interruption.
- Set up alerts for anomalies like sudden temperature spikes or unexpected drops in production speed.
- Track data quality metrics, including completeness, accuracy, and freshness of the sensor data.
Ensuring data reliability for food and beverage manufacturing
You work for a food and beverage manufacturing company that operates automated facilities to produce packaged foods and beverages on a large scale. Your systems monitor various data points, from ingredient ratios and temperature levels to packaging pressure and quality checks, ensuring every product meets strict standards for safety and consistency.
As the senior engineer, you’re responsible for the reliability and accuracy of the data collected across production lines. You implement a data observability solution to help you:
- Monitor real-time data streams from sensors throughout the production line, covering factors such as temperature, humidity, and ingredient levels.
- Detect unusual patterns that could indicate issues like equipment malfunctions, incorrect ingredient ratios, or inconsistencies in packaging pressure.
- Track data consistency across different machines, batches, and production facilities.
During a routine check, you spot unusually high temperatures on a production line. The data observability system helps you trace the issue to faulty sensors from a supplier, allowing the company to halt production, replace the sensors, and prevent a quality recall.
This swift action upholds food safety standards, ensuring efficient operations and high-quality products while minimizing waste and downtime.
Get more clarity into your IoT system data
Data observability enables data teams to manage complex data flows effectively by constantly tracking the health and performance of their entire data systems. Monitoring the five pillars of data observability together—freshness, volume, schema, distribution, lineage and traceability—supports the effectiveness of this strategy.
For IoT developers and data engineers, investing in a comprehensive data observability strategy enables faster troubleshooting, better data quality, and more optimized system performance.
Viam offers a powerful suite of data observability tools to support reliable, real-time data insights across connected devices.
If you’re ready to enhance your IoT system’s data management capabilities, sign up for a free, no-obligation account or demo today.
