No matter what field or role an engineer is in, they will routinely come into contact with data. This data can be used in a variety of ways to optimize or improve on systems or processes.
Analytics is one of the most in demand skills for engineers right now, making any applicant immediately more attractive. It’s a skill that can learned, improved on, and incorporated into daily tasks to increase productivity and preparedness.
Thanks to the Internet of Things, we have access to more data than ever. We can see how a process works in terms of raw numbers from start to finish, and where there might be areas to improve.
First, sufficient high-quality data must be gathered. This requires placing sensors and scanners at strategic or high-importance locations, which then generates raw data.
Enabling engineers to work with data makes the entire workforce more efficient, and this can make a big difference to the bottom line. But first, engineers must learn how to analyse the data and create actionable objectives from it.
Any engineer can learn basic analytics skills. With sufficient data, patterns can be discerned, causes identified, and improvements made.
However, most engineering projects generate massive amounts of data, which can be daunting for new analysts. Engineers should learn how to segment data into usable and manageable streams, rather just a meaningless flood of numbers. A solid grounding in mathematics is obviously necessary to understand data, as well as the ability to apply insight into the causes of problems. Over time an analyst will develop an intuitive understanding of statistics, and begin factoring them into a wider strategy.
While software engineers will already know various coding languages that can help with data collection and analysis, mechanical and electrical engineers might not be so lucky. Learning basic Python will help engineers who don’t have a background in coding, and will give them an edge when it comes to finding jobs in such a competitive industry.
Interpreting meaning from raw data is a skill that can be learned over time. While engineers tend to have a variety of important responsibilities that can take up the majority of their time, there are a variety of analytics programs that can automate the bulk of the work.
From there, engineers can apply their knowledge to improve things, utilising their expertise to maximum effectiveness.
Any engineer that has experience with analytics will be more likely to get a job. For example, there are usually very few plant-level engineers that have analytics skills, despite the fact that that is where a massive amount of data generated.
An engineer that can identify areas to improve, implement the means to do so, and enable continued growth will be any asset to any business or project.
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