工业大数据应用的四大挑战
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摘要
以数据为关键要素驱动工业转型升级,不仅成为宏观层面的行业共识,也正在微观层面为企业带来实际收益。然而,工业大数据的发展,还面临数据资源不丰富、数据管理滞后、孤岛普遍存在以及应用深度不足等四大挑战。为此,需要在企业层面夯实数据基础,抓住技术创新机遇,在行业层面建立数据互操作与流通的标准与规则。
Big data analytics plays the key factor to drive industrial transformation and upgrading. More and more enterprises benefit from industrial big data analytics applications. However, the development of industrial big data still faces four major challenges: insufficient data resources, poor data asset management, data silos, and insufficient depth of application. To overcome the challenges, we need to consolidate the foundation of industrial big data management and seize the opportunity of technological innovation at the property level. We also need to establish standards and rules to forester data interoperability and data share between companies.
Big data analytics plays the key factor to drive industrial transformation and upgrading. More and more enterprises benefit from industrial big data analytics applications. However, the development of industrial big data still faces four major challenges: insufficient data resources, poor data asset management, data silos, and insufficient depth of application. To overcome the challenges, we need to consolidate the foundation of industrial big data management and seize the opportunity of technological innovation at the property level. We also need to establish standards and rules to forester data interoperability and data share between companies.
引文
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