4 Problems That Engineering Data Analysts Solve 

If you’re interested in becoming an engineering data analyst, you might already be aware that they occupy dynamic roles in a variety of industries, from e-commerce and finance to healthcare and manufacturing. But are you familiar with some of the problems they work to solve on a day-to-day basis? 

No matter which industry they’re working in, these unsung heroes play a critical role in ensuring the long-term success of businesses. In this article, we’re exploring four typical problems data analysts address that contribute to saving time and costs for businesses of all sizes. 

#1 Incorporating Human-Machine Interaction in Industry 4.0 

Industry 4.0 is quickly becoming an essential paradigm in manufacturing. With the integration of advanced concepts such as the Internet of Things, cloud computing and artificial intelligence, we’re able to create smart factories that make production processes much more efficient. 

However, Industry 4.0 presents challenges, particularly in human-machine interaction (HMI). Engineers use data analytics and machine learning to enhance HMI, addressing human factors. 

Even with the manufacturing advancements linked to Industry 4.0 and the increased capability of machines to handle tasks, humans remain vital to their operation. Engineers take human factors, such as user interface design and safety prevention, into account to ensure that individuals can work safely and effectively with the equipment. They utilize feedback and machine learning algorithms to analyze data related to user interactions, pinpoint potential usability issues and enhance human-machine interaction. 

#2 Optimizing the Supply Chain  

Data analysts play a vital role in addressing a key challenge in supply chains: identifying and improving inefficient processes to reduce lead times. One common approach to achieving this goal is streamlining operations. 

But how does this process unfold? 

Data analysts meticulously study data to pinpoint inefficiencies. For instance, they may discover through production data that specific pieces of equipment are breaking down too frequently. Similarly, transportation data may reveal inefficient route patterns, leading to delays in delivery times. 

Armed with these insights from data analytics, organizations can take strategic actions to optimize their operations. They can streamline workflows by eliminating unnecessary steps and exploring innovative ways to automate certain tasks within their processes. These efforts contribute to enhancing the efficiency of both their supply chain and overall operations. 

#3 Mitigating Problems in E-Commerce  

With over five billion internet users worldwide, the e-commerce industry has become a prevalent choice for buying and selling goods, with sales surpassing $1 trillion in 2022. Data analysts play an essential role in improving operations in this lucrative industry, including: 

  • Algorithms 
    In e-commerce, algorithms analyze extensive customer data, including browsing history and purchase behavior, to personalize the shopping experience. Engineering data analysts can enhance these algorithms by carefully reviewing data, selecting suitable machine learning techniques and continuously monitoring performance. 
  • Visualization tools 
    In the context of e-commerce, visualization tools are graphic representations of data that help other stakeholders easily understand complex information. To help facilitate more informed decisions, data analysts create interactive dashboards that make it easier to grasp important insights, such as customer demographics, sales trends and website performance. 
  • Interfaces 
    It’s critical for the user-facing components of a website or mobile app to be navigable and accessible to potential customers. Engineering data analysts optimize user experience by using data-driven insights to identify user pain points and proposing design improvements to correct the issue.

#4 Predicting Maintenance Needs  

No matter how powerful or meticulously maintained a machine may be, it inevitably requires the occasional servicing to ensure it continues working efficiently. If not cared for, it could break down, potentially disrupting an entire production line and causing significant production delays. 

To avoid this, engineering data analysts often leverage machine learning algorithms to predict potential machinery failures. They do so by utilizing the equipment’s historical data and the pattern-identifying capabilities of machine learning. With that combined information, analysts can proactively schedule maintenance, prevent costly issues and reduce the unplanned downtime associated with unexpected machine failures. 

Become the Problem Solver Your Company Depends On 

If you’re an engineer who is considering expanding your professional horizons by becoming an indispensable behind-the-scenes data analyst, consider the University of Wisconsin–Madison’s online master’s in engineering data analytics. 

Designed with busy professionals like you in mind, our program offers flexible online courses that let you complete assignments at your convenience. In addition, we recognize the diversity of engineering roles and industries, and we believe that your degree should be tailored to your needs. 

Our 30-credit program consists of 15 elective credits, giving you the freedom to choose courses that align with your skill set and future career ambitions. With a wide variety of  elective courses spanning various interests—including leadership, manufacturing, sustainable systems, and more—you can tailor your education to suit your aspirations. 

Apply today to begin your journey toward a rewarding career in the quickly growing field of data analytics and engineering excellence.