Realistic interplays between data science and chemical engineering in the first quarter of the 21st century, part 2: Dos and don’ts

Abstract

Under various names, such as, data science, Industry 4.0, or smart manufacturing, digital technologies are transforming our world. Although value statements and promises are published in a steady stream, uptake in the chemical and process industries has been moderate. Successful transformations are not confined to tasks, the “whats”. They also require great care in how they are carried out. This overview, aimed at all participants in the digital transformation of the chemical industry, presents “dos and don’ts” method recommendations for three successive steps: strategy development to define goals, (organisational) mobilisation for implementation, and project delivery.Successful strategy development requires assembling an empowered and skilled team; truly understanding the data science and digital transformation topics; accepting emergence and iteration; and focusing on real needs.Mobilising an organisation is essential so that it can translate strategy to tactics and value. Within organisations, one must therefore: enable project identification; set up a supportive organisational structure and skilful people within it. Looking outside, participation in partnerships is essential to access external resources.Delivery of valuable projects is the end goal. A diverse portfolio is needed, as well as effective collaborations between subject matter experts and data scientists. Technically, the use of software best practice is beneficial, and care must be taken of the data themselves.In the longer term, data science opportunities will extend beyond merely improving traditional analytics to make them faster, better, and more user-friendly. The early identification of beneficial future trends requires encouraging those individuals who have an interest in disruptive currents, and the perceptiveness to sense their areas of application.