Data-Driven Decision-Making

Abstract

Addressing barriers to inclusion requires understanding the nature of the problem at the institutional level. Data collection and assessment are both crucial for this aim. In this chapter, we describe two important classes of data: (1) data on diversity that define the potential nature of the issues at stake and the need for change, and (2) data on assessing the usefulness of new programs, processes, and policies in creating a more diverse institution. Both sets of data are important for effective decision-making. At the same time, data analyses can be challenging because issues of equity and inclusion are complex and determining the basis of comparison or the “ideal” diversity target can be difficult. Nevertheless, data gathering and analysis are critical to assess progress and to provide a basis for both accountability and efficacy. Moreover, the ability to document that a problem indeed exists will help justify the need for change and, ideally, spur corrective action.

Leveraging AI to Enhance Marketing and Customer Engagement Strategies in the French Market

“…Companies may use AI to adjust their plans based on customer feedback and engagement indicators, ensuring they match changing consumer demands and preferences. Data-driven decision-making involves making informed business choices based on data analysis and interpretation [46]. It underlines the need to rely on reliable and relevant data to make strategic and operational choices.…”

“…This gap in talented workforce can impede the effective implementation of AI technologies, limiting their potential benefits. Furthermore, there is a risk of data quality issues since AI systems rely heavily on high-quality, accurate data to operate effectively [46]. Data quality can lead to accurate predictions and suboptimal resource allocation, reducing AI-powered operations' overall efficiency and effectiveness.…”

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“…Looking at data-driven decision-making, according to a survey of more than 1,000 senior executives conducted by PwC, highly data-driven organizations are three times more likely to report significant decision-making improvements compared to those relying less on data. Data-driven decision-making (sometimes abbreviated as DDDM) uses data to inform your decision-making process and validate a course of action before committing to it (Stobierski, T., 2021) (Barbu, S.J., et al, 2022) (Namvar, M., & Intezari, A. 2021).…”