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Maximizing the usefulness of big data and the Internet of Things (IoT)

Maximizing the usefulness of big data and the Internet of Things (IoT)

Continuous advancements in sensor technologies allow organizations to collect large amounts of data ever more easily and inexpensively. Pairing near-real-time monitoring with data science and machine learning is opening new views into system operations and troubleshooting. 

Maximizing the usefulness and value of ballooning volumes of data, however, requires expertise in instrumentation, data infrastructure, and reporting. 


We use state-of-the-art technologies that deliver high levels of accuracy and precision.

Our instrumentation and monitoring specialists help clients measure, analyze, and reduce risk on projects involving structures (like dams, wind turbines, storage facilities) or threats such as landslides and avalanches. For instance, we can install sensors that collect data during construction activities and facility operations and alert engineers and operators of anomalies or safety risks before schedule delays or failures occur.

From manual to near-real-time monitoring, analysis, and response—and from system design and installation to management, action plans, and asset management—Barr does it all. 

Near-real-time, secure instrumentation is ideal for collecting data on: 

  • Geotechnical and structural integrity, including displacement (landslides), strain, porewater pressure (landslides and dams), and earth pressure (retaining walls) 
  • Temperature, wind speed, and other ambient weather parameters 
  • Water and wastewater volume and quality
  • Remediation and mitigation systems, including vapor intrusion
  • Water-level changes in wells and aquifers
  • Process upsets and pollutant concentrations in air emissions
  • Low- and high-voltage electrical systems

Data science

After collecting data, Barr scientists and engineers employ statistical tools as well as machine learning to unlock patterns and relationships in complex data sets. That analysis gives our clients new insights into their projects and helps them respond to (and manage) changes and risks. Data-science algorithms also let organizations make informed predictions about trends and events.


Data architecture

Barr’s near-real-time, big-data systems offer clients power and precision. Working with Microsoft, we recently developed a proof-of-concept data architecture to support streaming IOT data, machine learning, and big data. We’re continuing to build out that cloud-based architecture to provide clients with speedy, secure, scalable information solutions. Innovative combinations of tools and programs let users connect to their project data at any point on its journey—from collection and warehousing to analysis and visualization.

Proof-of-concept data architecture to support streaming IOT data, machine learning, and big data


Barr designs project dashboards to report direct measurements, calculations, and key data trends in near real time. The dashboards—which interface with our data architecture and can be viewed via computer or mobile device—enable continuous tracking of operations and site conditions, as well as faster responses to events and deeper understanding of incoming data.


For your instrumentation, data infrastructure, and reporting needs, contact our team of technology experts.

Related projects

Statistical analysis of coal-silo vibrations

At its coal mine near Underwood, North Dakota, Falkirk Mining noticed unusual vibrations occurring in its silo during certain periods. Concerned about the silo’s structural stability, the company asked Barr to investigate the cause.

Remote sensors for relaying vibration data.

Analysis of microbial ecology in selenium bioreactor

Our client’s goal was to assess correlations between microbial-ecology and system-operations data to learn whether modifying the microbes’ environment had the potential to boost treatment system effectiveness. Before committing significant amounts of money to make operational changes, though, the company asked Barr to perform a preliminary statistical analysis.

Chemical element symbol for selenium from the periodic table of the elements.


Brian Fitch, Business Systems Support Manager.
Brian Fitch
Business Systems Support Manager


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