Prioritising data items for business analytics: Framework and application to human resources

TL;DR

This paper presents a new framework for prioritizing data items in business intelligence systems, balancing potential insights against costs, with an application to human resources.

Contribution

It introduces a model-based, prescriptive framework using multi-criteria decision analysis to prioritize data items for business analytics.

Findings

Framework effectively prioritizes data items based on importance and cost.

Application to HR demonstrates practical utility.

Supports decision-making in expanding BI systems.

Abstract

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.