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Data science for business: benefits, challenges and opportunities

机译:商业数据科学:利益,挑战和机遇

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Purpose - This paper aims to identify the benefits of data science (DS) for organizations, highlighting the challenges and opportunities related to developing this capability. Design/methodology/approach - Initially, a literature review was performed. Later, empirical data were collected through a structured electronic interview answered by 211 informants, who are most experienced managers of medium and large organizations from different economic sectors, and data were submitted to content analysis. Findings - The most frequently observed benefits are as follows: support for data analysis and insight generation with agility; creation of a data-driven culture; improvement of data quality; facilitating the understanding of the business environment, opportunity sensing; and organizational performance management. The most observed challenges are as follows: data-driven culture; DS training; allocation of investments in analytical technologies; and data governance and strategy. Research limitations/implications - In addition, to mapping the state of the art on the subject, it contributes to the expansion of scientific knowledge through the identification and disclosure of 11 benefit indicators and 16 challenge indicators associated with analytical capabilities. Practical implications - To transform data into information and add value to the business, organizations need to make efforts to enable executive mindset change, the formulation of strategies and governance mechanisms gave the renewal of workforce competencies and the allocation of investments in information technology. Originality/value - A vast body of empirical evidence is gathered that consolidates different views on the benefits and challenges associated with DS for business.
机译:目的 - 本文旨在确定数据科学(DS)对组织的好处,突出了旨在开发这种能力的挑战和机遇。设计/方法/方法 - 最初,进行了文献综述。后来,经验数据通过由211名线人回答的结构化电子访谈收集,他们是来自不同经济部门的中型和大型组织的最有经验的管理者,数据提交到内容分析。调查结果 - 最常见的福利如下:支持数据分析和洞察力的敏捷性;创建数据驱动的文化;提高数据质量;促进对商业环境的理解,机会传感;和组织绩效管理。最受观察到的挑战如下:数据驱动的文化; DS培训;分析技术的投资分配;和数据治理和策略。研究限制/影响 - 此外,为了将最先进的技术映射到主题,它通过鉴定和披露11个益处指标和与分析能力相关的16个挑战指标有助于扩大科学知识。实际意义 - 将数据转化为信息并向业务增加价值,组织需要努力使策略和治理机制的制定能够更新劳动力竞争力和信息技术的投资分配。创意/价值 - 收集了庞大的经验证据,巩固了对与DS的福利和挑战不同的看法。

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