THOUGHT LEADERS
The Opportunity for Engineers as Domain Experts in Closing the Data Scientist Gap
Wensi Jin Automo ve Industry Manager MathWorks Wensi Jin is responsible for strategic planning and technology rollout. His focus is to foster industry adop on of ModelBased Design and MathWorks tools. Prior to joining MathWorks, he worked on realme simula on and hardware in-the-loop test systems. He also worked on automa c transmissions control systems at General Motors Powertrain. Wensi holds a degree in Electrical Engineering from University of Texas at Aus n.
Ge ng data from test vehicles into the hands of end users is a common barrier for engineers who need data to formulate requirements for new products, troubleshoot field problems, and come up with new technologies. Connec vity technologies such as CAN and high-speed mobile communica on removed this barrier in many situa ons. With more and more streaming data from vehicles, we are faced with a data science challenge. We need to ensure that the speed of data analysis is keeping pace with data intake and, equally important, provide the capability to zoom into and extract insight from stored data throughout the engineering community. To address this new challenge, one o en looks for those who have computer science skills, knowledge of sta s cs, and domain exper se relevant to their specific engineering problems. Such PG.36 | Smart Automo ve | Mar - Apr 2018
ins nct is not wrong, but these types of candidates are rare. You may find success by focusing on domain exper se. Domain exper se is o en overlooked, yet it is essen al for making judgement calls during the development of an analy c model. It enables one to dis nguish between correla on and causa on, and between signal and noise. Domain knowledge is hard to teach. It requires on-the-job experience, mentorship, and me to develop. This type of exper se is o en found in engineering and research departments that have built cultures around understanding the products they design and build. These teams are in mately familiar with the systems they work on. They use sta s cal methods and technical compu ng tools as part of their design processes, making the jump to the machine learning algorithms and big data tools of the data analy cs world manageable. Instead of searching for elusive data scien sts, companies can stay compe ve by enabling their engineers to do data science with a flexible tool environment like MATLAB that enables engineers to become data scien sts. Engineers with domain knowledge need flexible and scalable environments to do data science. They need tradi onal analysis techniques such as sta s cs and
op miza on, data-specific techniques such as signal processing and image processing, as well as new capabili es such as machine learning algorithms. In par cular, machine learning with big data leads to a host of different technologies that support the itera ve process of building a data analy cs algorithm. It’s this beginning stage of the itera ve process of building the algorithm that can set a business up for success. This itera ve process involves trying several strategies like finding other sources of data and different machine learning approaches and feature transforma ons. Given the poten ally unlimited number of combina ons to try, it is crucial to iterate quickly. Domain experts are well suited to iterate quickly, as they can use their knowledge and intui on to avoid approaches that are unlikely to give strong results. The faster an engineer with domain knowledge can apply their knowledge with the tools that enable quick itera ons, the faster the business can gain a compe ve advantage. According to Gartner, engineers with the domain exper se “can bridge the gap between mainstream self-service analy cs by business users and the advanced analy cs techniques of data scien sts. They are now able to perform
Big Data Analy cs and Machine Learning plus signal processing, neural networks & more
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