数据智能:趋势与挑战

随着大数据和人工智能的兴起,数据智能(data intelligence)逐渐成为学术界和产业界共同关注的焦点.数据智能具有显著的大数据驱动和应用场景牵引两大特征.其融合场景内外的多源异质大数据,利用大规模数据挖掘、机器学习和深度学习等预测性分析方法和技术,提取数据中蕴含的有价值的模式,并用于提升复杂实践活动中的管理与决策水平.本文指出了推动数据智能实现迭代发展的三维要素:数据、算法和场景,然后围绕这三大要素介绍了数据智能的前沿热点、发展趋势和存在挑战,特别对数据智能与管理学交叉的研究与应用问题进行了较为深入的探索.论文还尝试给出一些具有前瞻性的观点或评论,一来希望为有兴趣进入数据智能领域的读者提供指引,二来希望能够在管理同行中起到抛砖引玉之效.

Data intelligence: Trends and challenges

With the unprecedented development of big data and artificial intelligence, data intelligence has emerged as a focal point in both academia and industry. It features in a set of predictive data analytics methods gathered in a big-data driven and applications oriented manner, including data mining, machine learning, deep learning, etc. It aims to extract valuable patterns from big data generated inside and outside targeted application scenarios so as to enhance real-life management and decision-making levels. This paper thus focuses on introducing the recent advances in data intelligence, which is formulated as a cyclic system including three naturally integrated and mutually functional dimensions: Data, algorithms, and scenarios. We discuss the hot topics, growing trends, as well as research challenges in data intelligence, with our own comments and opinions aiming to provide guidance for entering the area of data intelligence and arouse peer discussions on this exciting field.