In order to fully exploit the potential of Big Data applications, aerospace companies must be willing to combine the expertise of their maintenance engineers with insights from data scientists and more advanced machine learning techniques, according to speakers at the Global Connected Aircraft Summit in Washington, D.C. last week. During the \u201cManaging Aircraft Big Data\u201d panel, a group of experts concluded that this is one of the reasons why the aerospace industry has lagged behind other industries in unlocking the value potential of connected assets.<\/p>\n
Arun Saksena, global vice president of data science and analytics for HCL America<\/strong>, warned that if analytics is left to data scientists or engineers by themselves, it\u2019s easy to reach conclusions that are either inaccurate or irrelevant. \u201cData science is a team sport,\u201d he said. \u201cYou need cross-functional teams.\u201d<\/p>\n \u201cIn aerospace, we don\u2019t have a lot of data scientists. We\u2019re starting to pull data scientists into the engineering groups and it\u2019s starting to give us interesting insights,\u201d said John Craig, chief engineer of cabin and network systems for Boeing<\/strong>. \u201cSomebody who doesn\u2019t have any domain knowledge sometimes interjecting is actually extremely valuable.\u201d<\/p>\n While aircraft maintenance experts may have a better understanding of the systems themselves, data scientists are more adept at finding patterns within the data and can also help draw connections between different categories of information, argued Rahul Ghai, managing director of flight operations and in-flight technology for United Airlines<\/strong>. Currently, he said, airlines are already collecting the data they need and must only improve the ways they analyze it.<\/p>\n \u201cUltimately, I think we\u2019ve got access to more than enough data already,\u201d he said. \u201cIt\u2019s getting the data out of the silos they\u2019re in and starting to look for what might not be so obvious.\u201d<\/p>\n Rodrigo Navarro, director of data platform engineering at Global Eagle<\/strong>, spoke similarly, stating that airlines may even have more data than they know what to do with. \u201cThey are capable of [collecting] more data than they can actually chew,\u201d he said, to the point where much of it is not being used correctly, if at all. \u201cThings without analytics have no value,\u201d he added.<\/p>\n One of the ways airlines can maximize the value potential of the data it collects is by adjusting the framework of an asset\u2019s sensors, Saksena said. \u201cIn IoT, a very common architecture approach we take is having sensors at the edge of the network. You can\u2019t stream data from these sensors because you\u2019re going to consume bandwidth \u2014 essentially competing for the same bandwidth as 100 customers trying to make Skype calls,\u201d he said. \u201cWe try to do analytics at the edge, closer to where the data is being generated, and only that summary information comes back into the cloud. This is still being developed from a connected aircraft perspective.\u201d<\/p>\n Companies born in the digital age such as Google<\/strong> and Netflix <\/strong>already leverage these more advanced machine learning techniques such as artificial neural networks, Saksena said, as well as industrial manufacturing companies in sectors such as energy, healthcare, and oil and gas.<\/p>\n If airlines take cues from these other verticals, \u201c[they] start to get better abilities to predict,\u201d Ghai said. \u201cThat\u2019s a powerful word to use because at this point we are still just reacting to a situation.\u201d<\/p>\n To be fair, airlines in general have been using data effectively for years, Ghai added, both for operational purposes and to make sense of consumer trends. However, the industry still faces a hurdle in changing its culture to rely more on what the data is saying. \u201cNow that we are getting connectivity that is more reliable, it\u2019s making the leap of faith to trust what a machine is telling you,\u201d he said.<\/p>\n For Saksena, the disappearance of Malaysia Airlines\u2019<\/strong> Flight 370 airplane in 2014 was a wake-up call that the industry needed to accelerate its adoption of new data applications. \u201cI remember thinking to myself that in this day and age, how can we lose an asset that\u2019s so big and so prominent? It was mind-boggling that we could not even trace it,\u201d he said. \u201cA lot of international supply chains rely upon air freight. And if you can\u2019t track the airplane itself, how are you going to track packages and other materials on the plane?\u201d<\/p>\n Tracking assets along the supply chain has become a significant part of nearly every industrial sector \u2014 even maritime, where connectivity is arguably difficult to achieve. And yet the airline industry has been slow to adopt new technology that could enable similar applications.<\/p>\n Moving forward, Abhi Seth, senior director of data analytics at Honeywell<\/strong>, said he hopes to see a shift in the \u201clegacy mindset\u201d of airline operators to be more receptive to new machine learning techniques. \u201cPeople have been doing their jobs on the maintenance and operations side in a certain way for so many years,\u201d he said. \u201cIt\u2019s hard for people to embrace that change.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":" In order to fully exploit the potential of Big Data applications, aerospace companies must be willing to combine the expertise of their maintenance engineers with insights from data scientists and more advanced machine learning techniques, according to speakers at the Global Connected Aircraft Summit in Washington, D.C. last week. During the \u201cManaging Aircraft Big Data\u201d […]<\/p>\n","protected":false},"author":1,"featured_media":68055,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[2],"tags":[],"class_list":["post-68053","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news"],"acf":[],"_links":{"self":[{"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/posts\/68053"}],"collection":[{"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/comments?post=68053"}],"version-history":[{"count":0,"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/posts\/68053\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/media\/68055"}],"wp:attachment":[{"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/media?parent=68053"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/categories?post=68053"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/starpath.global\/blog\/wp-json\/wp\/v2\/tags?post=68053"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}