With the rise of the Internet of Things (IoT), industries are awash in petabytes of Big Data from an increasing array of wired and wireless sensors. Gartner estimates that 20.8 billion connected things will be in use worldwide by 2020. These sensor networks are continuously monitoring and reporting on everything from heart rates, to production flow, to turbine wear, creating opportunities for industries poised to take advantage of this wealth of data to reduce maintenance costs, predict equipment failures, and drive transformation in their business operations.
Sensor Data Analytics predicts the behavior of devices in service allowing manufacturers to track the usage and performance of their products in nearly real time. As IoT-enabled products infiltrate the market, embedded software and device interconnectivity are blurring lines between the physical and the digital in terms of monitoring device health, capability, and serviceability.
At Elder Research, we have helped clients in the following areas to unlock value from their sensor and IoT data:
- Healthcare & Medical Devices
- Industrial Automation
- Defense and Intelligence
Sensor Data Analytics Applications
Elder Research has extensive experience helping our clients use predictive analytics to filter through the noise of high volume, fast moving big data from sensor networks to reveal actionable business insight. Examples of our Sensor Analytics applications include:
PREDICTIVE MAINTENANCE AND RELIABILITY ANALYSIS
Equipment downtime can be costly, last minute repairs are often difficult to schedule, and replacement parts may be difficult to find. Elder Research Sensor Data Analytics can predict when your equipment is likely to need maintenance before it may be evident to operators, allowing you to proactively schedule preventative maintenance and repairs before a failure occurs to prevent costly equipment downtime and improve operational efficiency and productivity.
ANOMALY DETECTION & DIAGNOSIS
Sensors data analytics can be the first line of defense in detecting anomalous behavior or readings before a fault degrades user experience or leads to costly device failures. Sensor readings unusual for normal operating conditions can trigger alerts and enable intervention by experienced technicians.
CUSTOMER BEHAVIOR ANALYTICS
Sensor data is a rich information source that can be used to understand user behavior, segment populations for targeted marketing, personalize customer interactions, and track product usage through log data. Elder Research has experience providing value for our clients by creating predictive models, helping visualize usage trends, and improving user experience. Elder Research sensor analytics solutions can enhance customer experience, reduce churn, and increase customer lifetime value.
SENSOR APPLICATIONS FOR INFORMATION TECHNOLOGY
While the potential value derived from sensor data analytics appears limitless, the process for unlocking this value can be challenging. All predictive analytics efforts involve some data cleaning, however, the volume and variety of data involved with sensor analytics place an even greater burden on data storage, preparation, and infrastructure design.
We have experience with all phases the process—from raw sensor data to actionable predictive results. Whether your data and analysis takes place remotely in the cloud, in a centralized analytics service, or at the site of the device, Elder Research can work with you to find solutions.
Sensor Data Analytics CASE Studies
With the ubiquity of sensor data from connected devices, software usage logs, and equipment monitors, there are ample opportunities to deliver value from these vast data sets using advanced sensor analytics. Whether you are new to sensor analytics or looking to augment existing capabilities, Elder Research can provide support where you need it most. Examples of our sensor analytics solutions include:
Using Sensor Analytics to Predict Natural Gas Well Freezing
Elder Research harnessed 20 years of detailed sensor data from hundreds of wells to characterize transient well states. The goal was to predict gas well shut-ins (blockages preventing production) due to hydrate formations four to six months in advance in a North American field, where winter months allowed only very limited access. Our model was far more accurate than the existing heuristic models and it allowed operations to know which clusters of well pads should be prioritized for treatment. A second model was created which clearly showed when in the new well life cycle a plunger pump system should be installed. The estimated return on investment for each model was less than one year. Read the Case Study
Product Usage Analytics Improves Software User Experience
At Elder Research, our analytics consulting expertise enabled us to successfully use over 1TB of log data to build a user segmentation model for a major software client. These segments have proved hugely valuable in helping understand user needs and behavior. Additionally, we were able to create a custom visualization tool which allowed the client to visualize their log data in a way that was previously impossible, helping them to better understand and reach out to their customers. Read the Case Study or Read the Blog