论文标题
优先考虑业务分析的数据项：框架和对人力资源的应用
Prioritising data items for business analytics: Framework and application to human resources
论文作者
论文摘要
在过去的十年中，商业智能（BI）系统支持业务分析的流行已大大增加。确定应该存储在BI系统中的数据项对于确保组织业务分析策略的成功至关重要。扩展常规的BI系统通常会导致内部生成，清洁和维护新数据项的高成本，而在许多情况下，额外的数据存储成本在许多情况下是对大数据系统的概念差异。因此，BI系统中新数据项产生的潜在其他见解需要与通常很高的数据创建成本保持平衡。尽管文献承认了这个决策问题，但迄今未提出任何基于模型的方法来告知该决定。本研究描述了一个规范性框架，以优先考虑业务分析的数据项，并将其应用于人力资源。为了实现这一目标，拟议的框架在全面的过程图中捕获了核心业务活动，并通过多标准决策分析评估其相对重要性和可能的数据支持。
The popularity of business intelligence (BI) systems to support business analytics has tremendously increased in the last decade. The determination of data items that should be stored in the BI system is vital to ensure the success of an organisation's business analytic strategy. Expanding conventional BI systems often leads to high costs of internally generating, cleansing and maintaining new data items whilst the additional data storage costs are in many cases of minor concern -- what is a conceptual difference to big data systems. Thus, potential additional insights resulting from a new data item in the BI system need to be balanced with the often high costs of data creation. While the literature acknowledges this decision problem, no model-based approach to inform this decision has hitherto been proposed. The present research describes a prescriptive framework to prioritise data items for business analytics and applies it to human resources. To achieve this goal, the proposed framework captures core business activities in a comprehensive process map and assesses their relative importance and possible data support with multi-criteria decision analysis.
