Navigating Sensor Data Analytics | Expert Guide

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A Guide To Sensor Data Analytics

Data, data, data….it’s everywhere! Humans and our machines are now generating more amounts of real-time data than ever before, due to our increasingly connected networks of consumer mobile devices, industrial equipment, major business systems, and more. With this development, it is not surprising to find industry projections for the growth of sensor data estimated to reach $28.5 billion by 2028. 

All kinds of manufacturing equipment, new vehicles, industrial machines, appliances, wearable devices, and other objects are embedded with sensors that connect to the internet and transmit data. This sensor data provides valuable information about the performance and physical condition of the objects in which the sensors are embedded.

In this article, we’ll show you what sensor data is and why it’s important. You’ll read what sensor data analytics entails and how it enables organizations to maximize the value of their connected devices and sensors. You’ll learn about data collection and pre-processing, the type of data you’ll get with different sensors, and how to use advanced data analytics with an IoT network.    

What Is Sensor Data and Why Is It Important?

Sensor data comes from the data generated by IoT sensors and devices. Sensor data analytics refers to the data-driven techniques and processes that are used to collect, process, analyze, and extract meaningful business insights. 

To enable sensor readings, manufacturers embed sensors into machines, vehicles, energy meters, wearable devices, and more. These sensors generate huge amounts of important data around performance indicators like temperature, pressure, location, vibration, energy usage, and other metrics.

For example, vehicle manufacturers design built-in predictive maintenance systems into their vehicles. With these sensors in place in the vehicle, owners can see if there are any flare-ups with the vehicle’s mechanical operation. The vehicle’s driver sees a notice about an issue with the system and learns about the importance of the issue and how to handle the problem. 

The sensor data is then collected and fed into predictive analytics systems. This is where sensor data analytics takes over. Various analytical techniques are then used to analyze the data and try to identify certain patterns, trends, and correlations in the data. 

Manufacturers can use all this data from sensor technologies to find ways to develop new products, increase operational efficiency, improve service quality, reduce the amount of worker downtime, and automate overall decision-making.

Types of Sensors and Data They Generate

Different types of sensors are used to gather data in different areas. For example, in a recent blog post, we covered temperature sensors, humidity sensors, and chemical sensors, and described the areas in which they were best utilized.

In essence, sensor data analytics unlocks the value in data produced by IoT devices to solve real-world problems. It turns raw sensor data into meaningful, actionable information to benefit businesses and customers.

Environmental Sensors and Their Data

Let’s start with environmental data sensors. These types of sensors measure different aspects of the environment like temperature, humidity, air quality, noise levels, and more. The key benefit of environmental sensors is that they provide for real-time monitoring and collecting of environmental data.

A few examples of environmental sensors and the data they collect are:

  • Air quality sensors monitor levels of pollutants like particulate matter, nitrogen dioxide, and ozone in a confined or open area. Air quality sensors are an important part of many manufacturing operations. 
  • Noise sensors placed near airports, highways, or urban areas measure decibel levels and monitor noise pollution.
  • Sensors used in the agricultural industry can help workers monitor soil moisture, crop health, and weather-related growing conditions.

For many business owners, having this information collected and analyzed can help identify specific issues or trends. Companies can use environmental sensor data to gain a stronger handle on policy-making, as well as to guide research projects and any other environment-related decision-making.

Industrial Sensors and Their Data

Industrial IoT sensors are mainly used in manufacturing or industrial facilities to measure things like temperature, pressure, flow, levels, and other areas related to industrial processes and equipment.

These IoT smart sensors are critical to maintaining good equipment status and preventing any downtime issues because of faulty parts or processes in the setup of these machines. Industrial sensors help to improve quality control and can also be used to protect workers by detecting dangerous conditions like gas leaks or excessive vibration.

For instance, at Attune, we employ a wide variety of sensors in industrial operations to monitor indoor air quality, outdoor air quality, energy consumption, equipment status, and more. Those companies that work with us are able to use our sensor data to obtain a clear view of the air quality in the workspace. 

Healthcare Sensors and Their Data

Healthcare sensor readings are a critical part of personalized patient care. These sensors measure vital health-related conditions to monitor patient health and enable personalized care. With healthcare sensors, physicians and their teams can get early warnings about certain health conditions and improve patient communications for medical needs. They can also now do at-home monitoring of patients, reducing the number of hospital visits. 

Recently, some patients have swallowed ingestible sensors to allow physicians to get a closer look at internal operations within the body. A pill-sized capsule device travels through the body, armed with a camera to capture images throughout the body. Using ingestible sensors has been instrumental for healthcare executives to gain more compliant data collection to diagnose celiac disease, Crohn’s disease, and other gastrointestinal cancers

The Process of Sensor Data Analysis

Sensor data analysis can be a tricky task for some companies. Here’s how we see the process for collecting data from advanced sensors.

Data Collection and Preprocessing

Collecting data and preprocessing all this data from sensors can help organizations grasp the potential of using sensor data in their operations. Here are some ways that data moves from raw numbers and measurements to actionable work.

  1. Data Acquisition - Sensor data must be securely acquired from the source devices and collated in a central data repository. 
  2. Data Processing - Raw data is processed to clean data, using smoothing techniques and normalization methods, and into a usable format for analysis. 
  3. Data Exploration - Visualization and descriptive statistics help data analysts "get to know" the data and form hypotheses.
  4. Predictive Modeling - Predictive modeling helps find data patterns, categorize data, and make predictions about future events.
  5. Operations - Sensor data is then integrated into real-time monitoring dashboards and business workflows.

Data Analysis Techniques

Harnessing all this sensor data requires a sophisticated data analytics approach. By analyzing this vast amount of sensor data, organizations can gain actionable insights from raw measurements. Among these insights include data-driven ways to improve business processes, reduce costs, and identify new revenue streams. 

There are a variety of data analysis methods to collect the raw data from sensors to gain these valuable insights. Some of these include: 

  • Signal processing - Filtering, amplifying, and conditioning the raw sensor signals to remove noise and prepare the data for analysis
  • Feature extraction - Identifying and extracting key features from the sensor data that have important information for the application
  • Segmentation - Segmenting sensor data into various segments based on the activity level and signal patterns
  • Classification - Using AI and ML algorithms like SVM, logistic regression, and neural networks to classify sensor data into categories

Put Your Data To Work With a True IoT Platform

AI, machine learning, and the growth of IoT platforms all point to a data-centric future in which sensor data analytics will become an indispensable practice for businesses. Sensor data analytics will grow more sophisticated by leveraging AI and ML. Inevitably, organizations will form fully automated, self-optimizing intelligent systems powered by sensor data analytics. In the future, all business decisions will be driven by data from connected devices and sensors.

Thus, it’s important that you find the right analytics tool solution for your needs. Whether your business is in manufacturing and industrial operations, environmental monitoring, or even within the healthcare space, it’s critical to have the right sensors working on your behalf. 

That’s why Attune can help. When you contact Attune to schedule a demo, we’ll discuss with you the various ways in which sensors can be used for your overall operations. Contact us today and we’ll work together to make your sensor data analysis become an integral part of your business.

Book a Demo here!

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