Types of Data Analytics in Engineering 

The world generates massive amounts of data – equivalent to a billion two-hour movies on Netflix each year. In fact, the amount of data generated worldwide continues to increase exponentially. That’s a lot of information! 

This data deluge has transformed industries, including engineering, where professionals utilize big data to drive innovation and efficiency. In this article, we’ll explore types of data analytics used in engineering, from descriptive analytics to spatial analysis. Let’s dive in! 

The Four Pillars of Data Analytics 

Imagine if you could predict equipment failures before they happen, optimize processes and maintenance schedules and minimize downtime. By analyzing incoming data, you’d be able to  propose a course of action with significant impact.  

Engineers begin with the same four pillars of data analytics used by data scientists and analysts across other industries. Those are:  

#1 Descriptive Analytics 

Descriptive analytics asks, “What happened?” This involves summarizing historical data from a source to gain insights into past performance and trends. 

How is it used in engineering?  

Engineers use descriptive analytics to understand the current state of an operation or task, identify patterns based on what had happened before and visualize trends. 

#2 Diagnostic Analytics 

Diagnostic analytics asks the question, Why did this happen?” It uses analyzed data from descriptive analytics to determine why certain events or trends occurred. 

How is it used in engineering?  

Engineers use this tool to investigate the root causes of certain events, such as equipment failure. For example, in automotive engineering, this technique may reveal why an engine or sensor may have failed, which might lead to a proposal to improve the design. 

#3 Predictive Analytics 

Predictive analytics asks, “What happens next?” This step involves using data analyzed via descriptive and diagnostic analytics to spot patterns, anticipate future outcomes and provide recommendations. 

How is it used in engineering?  

Engineers utilize predictive analytics to predict performance and possible failures, as well as streamline processes and optimize maintenance and inspection. For example, an engineer working on a power grid may want to find out when energy demands surge, then make sure to allocate resources and ensure the stability of the grid. 

#4 Prescriptive Analytics 

Prescriptive analytics leverages the results from descriptive, diagnostic and predictive analysis and provides recommendations or a course of action for a problem. It asks, “What is the next step?” 

How is it used in engineering?  

Engineers use prescriptive analytics to inform their decision-making around scheduling tasks, material selection, optimizing operational costs and improving overall energy consumption. 

Data Analytics Specific to Engineering 

Engineers use a combination of the four pillars of analytics and engineering-specific analytics to address the unique challenges and requirements of their daily tasks. There are numerous types of analytics data engineers use, depending on their specific discipline or field. Let’s explore some of those below. 

#1 System Analytics 

This data-driven approach empowers engineers to gain insights into system performance, identify areas for improvement and optimization, and predict future trends. Some of the tools they use are:  

  • Machine learning frameworks for predictive analytics and anomaly detection. 
  • Analyzing feedback loops to enhance or counteract a system’s behavior.

#2 Predictive Maintenance Analytics 

Engineers use this powerful tool to predict possible defects and failures in machinery and equipment. Knowing when an asset may break down allows engineers to alleviate problems early. Engineers gather data from sensors, logs and records and then use machine-learning algorithms to build: 

  • Regression models, which can predict when equipment is likely to fail. 
  • Decision trees, which can be utilized to define risk categories and help create maintenance schedules.  
  • Neural networks to predict when a machine may fail. 

#3 Quality Control Analytics 

Quality control analytics aims to improve production quality and optimize production processes. For example, an engineer in the manufacturing industry might use this type of analysis to reduce waste while increasing production yield. Since quality control analytics is a predictive tool, it strengthens risk management and minimizes the chance of equipment failure. Engineers may use tools like: 

#4 Structural Health Monitoring 

Although structural health monitoring (SHM) is a field of engineering rather than analytics, SHM is driven by data. The primary goal of SHM is using data to detect, assess and predict changes in the structural integrity of infrastructure like buildings and bridges. This is done by: 

  • Sensor Data Analytics: The analysis of data derived from sensors attached to infrastructure. 
  • Time Series Data Analytics: The analysis of data collected within a specific timeframe. 

#5 Energy Data Analytics 

Energy production, consumption and efficiency is vital in today’s engineering sectors to promote sustainability and conserve our environment. To optimize energy usage and minimize waste and pollution, engineers use data and machine-learning algorithms to build models like:  

  • Decision trees to help predict energy consumption patterns. 
  • Neural networks for forecasting consumption and optimizing systems. 
  • Markov models to predict future events based on current state probabilities.  

#6 Spatial Analytics 

Spatial analytics involves the analysis of geographical or spatial data to gain insights into patterns, trends and relationships within spatially referenced data and make informed decisions. Engineers utilizing this technique might use the following tools: 

  • Geospatial Information Systems (GIS) to manipulate spatial or geographical data. 
  • Remote sensing, which involves catching remote data from satellites or aircraft. 

#7 Reliability Analytics 

Reliability analytics focuses on a product’s lifespan and performance. Engineers use this technique to identify failure rates and predict where they may occur in the future. This allows engineers to make impactful decisions on operational equipment and maintenance. To do this, engineers may: 

  • Perform real-time monitoring. 

Become Part of the Data-Driven Future 

If your passion lies in big data and making big-impact decisions in engineering, consider honing your skills and expertise with the University of Wisconsin–Madison’s online master’s degree in data analytics. Our 30-credit program blends data science education with project leadership skills specific to engineering, equipping you with an interdisciplinary education that prepares you to lead any engineering company to success. Apply here!