title: Big Data Analytics in Chemical Engineering.
abstract: Big data analytics is the journey to turn data into insights for more informed business and operational decisions. As the chemical engineering community is collecting more data (volume) from different sources (variety), this journey becomes more challenging in terms of using the right data and the right tools (analytics) to make the right decisions in real time (velocity). This article highlights recent big data advancements in five industries, including chemicals, energy, semiconductors, pharmaceuticals, and food, and then discusses technical, platform, and culture challenges. To reach the next milestone in multiplying successes to the enterprise level, government, academia, and industry need to collaboratively focus on workforce development and innovation.
authors.affiliation: The Dow Chemical Company, Freeport, Texas 77541; email: hchiang@dow.com.
authors.forename: Leo
authors.initials: L
authors.lastname: Chiang
authors.affiliation: The Dow Chemical Company, Freeport, Texas 77541; email: hchiang@dow.com.
authors.forename: Bo
authors.initials: B
authors.lastname: Lu
authors.affiliation: The Dow Chemical Company, Freeport, Texas 77541; email: hchiang@dow.com.
authors.forename: Ivan
authors.initials: I
authors.lastname: Castillo
mesh_terms: D002616:Chemical Engineering / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D055808:Drug Discovery / Q000379:methods; D004345:Drug Industry / Q000379:methods; D004581:Electronics / Q000379:methods; D019649:Food Industry / Q000379:methods; D006801:Humans; D000069550:Machine Learning; D012666:Semiconductors
keywords: Industry 4.0; Internet of things; big data analytics; data-driven modeling; machine learning