3 Benefits of Continuous Data
Consistent continuous data is a necessity for smarter operations and AI-powered automation.
Artificial Intelligence (AI) is rapidly becoming a routine aspect of business operations. A recent survey from Deloitte found 34% of respondents had already begun implementing AI systems to support intelligent automation, while 52% plan to implement these systems in the next three years. But implementing and operationalizing AI is often easier said than done, especially without continuous, reliable data about your operations.
AI and machine learning (ML) systems depend on data to deliver results. For organizations with large facilities, remote sites, or hundreds or even thousands of assets, collecting high-frequency, structured data that AI can use to generate actionable insights is a challenge. One survey from the National Association of Manufacturers found that 58% of respondents reported only a moderate ability to collect meaningful data for their business needs.
Continuous operational data capture is crucial to your AI program from implementation to routine use. How do you overcome the existing obstacles to reliable, continuous data to reap the full benefits of AI?
OVERCOMING THE DATA GAP
Manual scans or inspections are typically resource intensive, conducted infrequently, and reliant on an individual’s knowledge and experience to flag issues. On the other hand, fixed IIoT sensors offer ongoing structured data, but they are expensive, have limited perspective, and are difficult to adapt when circumstances change.
Agile mobile robots equipped with custom payloads offer the best of both worlds: autonomously capturing the data that matters most for your operations and interfacing directly with your existing software solutions to process data on the edge. This dynamic approach to data helps overcome gaps and unlock the full benefit of AI.
START WITH ACCURATE MODELS
The first major benefit of continuous data capture is in starting with an accurate, well-trained model. Implementing an AI system requires a lot of data up front: building and training a computer vision or anomaly detection model takes a large baseline data set. In order to identify an abnormal input, the system needs to know what normal looks like.
For example, imagine you want to use computer vision to automate gauge reading. Images taken at different times of day or in different lighting could result in false positives or other noise. Rather than cobbling together legacy data or wasting hours on manually capturing hundreds or thousands of new images, you could automate the process. Plan a route through your facility to take a picture of each gauge; the robot can then repeat this mission as frequently as needed to get a representative data set. This continuous, dynamic approach speeds up the time it takes to assemble your data set, and offers a more comprehensive sample, ultimately resulting in more accurate results down the line.
GENERATE MORE RELIABLE INSIGHTS
Whether you build your own AI model or integrate one off-the-shelf, you still need to feed frequent, high-quality data into the system. Variations in the data captured—for example, it’s difficult for a person to take a photo at exactly the same spot and angle every day—or missing data due to a lack of coverage from fixed sensors reduce the overall reliability of the system.
Deploying a robot on regular autonomous rounds of your facility—gathering data on equipment and assets routinely and reliably—helps ensure that your computer vision or enterprise asset management (EAM) system is always running off up-to-date information. Additionally, mission scheduling enables you to collect data around the clock to avoid surprises during off hours and maximize productivity during core hours.
MAKE BETTER DECISIONS
More data—and more accurate data—means it’s easier to make data-driven decisions, minimizing the risk of unplanned delays or downtime. At the same time, automated sensing frees up your teams from taking tedious, time-consuming scans. People shouldn’t be feeding data into the system; they should be making decisions and putting the insights surfaced into action.
A continuous stream of accurate data feeding your existing software systems makes it easy to identify issues before they escalate. You are able to automate alerts and work orders to act on the insights surfaced, ensuring operators and maintenance teams can easily prioritize their response and act with all the details. Perform maintenance when needed, minimize downtime, and maximize the efficiency of your site and team.