Software & Technology

The Internet of Things (IoT) and Industry 4.0 are changing business paradigms for software and technology companies. Connected software and hardware devices generate enormous data sets.  To gain a strategic advantage successful companies use advanced analytics to reveal valuable insight from this large corpus of data.

Elder Research has helped software and technology companies leverage data science to solve tough business challenges, inspire product and service differentiation, and uncover profitable competitive advantages. Here is a brief overview of our analytic services for software and technology firms:

Product Usage & Log Analytics

Commercial product logs are a very rich source of information about how customers interact with products and software. Data mining, predictive analytics, and data visualization can reveal robust quantitative measures of software quality and precise user interactions. This insight can be used to: streamline software processes, improve software stability, and enhance user experience and retention.

IoT & Sensor Analytics

IoT is creating opportunities for industries to take advantage of big data to improve product usage, reduce maintenance costs, and avoid equipment failures. We have extensive experience helping our clients use predictive analytics to filter through the noise of big data from sensor networks to reveal actionable insight.

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Case Studies

SolidWorks: Product Usage Analytics Improves User Experience

Our data scientists successfully ingested terabytes of SolidWorks log data to build user segmentation models. Creating a custom visualization tool provided SolidWorks novel ways to visualize and comprehend their log data.

Results: User segments defining unique user personas were reproducible with 92% accuracy. These personas helped SolidWorks better understand, communicate with, and serve their users—facilitating more effective customer engagement and outreach.

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Using Sensor Analytics to Predict Natural Gas Well Failure

Our team harnessed 20 years of detailed sensor readings from hundreds of wells to characterize transient well states. The client challenged us to predict gas well shut-ins (blockages preventing production) 4 to 6 months in advance to effectively mitigate or prevent risks of loss.

Results: Elder Research’s data science model was 3x more accurate than the existing heuristic models (that did not have to predict as far in advance). Our client now has access to clusters of well pads that are prioritized for treatment or preventative maintenance. Payback for this engagement was one year.

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