应用科学领域的数字化转型策略

S. Bentum, D. Wild
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摘要

当今具有数字化思维的组织的关键标志体现在它们的快速发展、全球化、创新和适应变化的能力上。希望蓬勃发展的公司必须准备好适应新的数字现实。数字化思维并不意味着实施新技术、投资工具和升级现有系统。这些阶段很关键,但它们并不是全部。如果一家公司想要保持竞争力,它不仅必须能够适应变化,还必须预见并推动创新。为了实现这一愿景,公司必须提前计划,成为未来的积极建筑师。这就是数字化转型战略至关重要的地方。数字化转型战略有助于组织领导层应对业务挑战,例如当前的数字化水平和数字化成熟度路线图。尽管存在多种数据捕获技术和数据生成资产,但材料/化学科学领域(如研发和制造团队)仍在努力利用其数据的全部力量。一个典型的行业将有大量的数据源生成大量的数据,这些数据存储在孤立的数据库中,很少甚至不存在串扰。这在一定程度上为研究人员创造了能够在一组数据中进行深入研究的场景,但无法共同填充和利用不同数据集之间的相互依赖或关系。本文旨在定义、区分、汇总并提出一种综合方法,以利用研究人员在其材料科学研究领域中经常遇到的各种类型的不同数据源。在这些行业寻求拥抱数字化转型的力量之际,这里的主要重点是制定战略,利用综合数据的洞察力来帮助研发组织进行有效的研究。虽然这里描述的原则与应用科学领域的行业有关,但所提出的一般策略也可以根据具体情况应用于其他行业。
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Digital transformation strategies for applied science domains
The key hallmark of a digitally minded organisation today is seen in their rapid advancement, globalisation, innovation and resilience to change. Companies that wish to thrive must be prepared to adapt to the new digital reality. Being digitally minded does not mean implementing new technology, investing in tools and upgrading current systems. These stages are critical, but they are not the entire picture. If a company wants to remain competitive, it must not just be able to adapt to changes, but also anticipate and drive innovation. Companies must plan ahead and be proactive architects of their future in order to achieve this vision. This is where a digital transformation strategy is crucial. A digital transformation strategy assists organisational leadership in addressing challenges about their business, such as the present level of digitisation and a digital maturity roadmap. Although diverse data capturing technologies and data-generating assets exist, material/chemical science domains, such as R&D and Manufacturing groups, struggle to harness the full power of their data. A typical industry will have significant data sources generating large amounts of data stored in siloed databases with minimal to non-existent cross-talk. This in part creates scenarios for researchers to be able to perform a deep dive in one set of data, but unable to co-populate and harness the interdependences or relationships amongst the different datasets. This paper seeks to define, distinguish, aggregate and propose an integrative approach to utilising the various types of disparate data sources commonly encountered by researchers in the field of their material science research. The main focus here is defining strategies to harness insights across integrative data to aid in efficient research in R&D organisations as these industries seek to embrace the power of digital transformation. Although the principles described here relate to industries in the applied science domain, the general strategies proposed can be applied to other industries on a case-by-case basis.
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