Data Analytics: Challenges and Opportunities for the Family Business

Introduction

This research refers to the key success factors (FCE) necessary for the development of data analytics within the firm, certain resources that require quality information for marketing decision making. Information quality is understood as technological knowledge through information technologies, which is used as the basis for good decision making and positive results (Ji-fan et al., 2016). The main characteristic of the FCE is that it allows the entrepreneur to achieve the objectives of quality information for commercial decision making, and what distinguishes them from the competition; as an imperfectly imitable analytical resource due to its unique processing, causal ambiguity or social complexity (Barney & Clark, 2007).

The main interest of the research is to propose to the family firm (FF), an FCE configuration to implement data analytics within the organization. This responds to the fear that the FF have expressed for maintaining satisfactory growth rates. According to PriceWaterhouseCoopers (2018), a series of factors that are expected to be relevant to promote the growth of FFs have been identified, including the difficulty in accessing adequate talent, and the generation of information for decision-making. On the other hand, it is our interest to establish what type of organizational structure presents better results when there are data analysts within the firm. Also, from the academic point of view, we are interested in deepening the conceptual comparison between FF and non-family members (NFF). It is specifically pertinent that this type of study has rarely attracted scholars in technology (Alberti & Pizzurno, 2013). There are only studies on knowledge management and that, according to Massaro et al. (2016), are long and alone are raised from the perspective of the processes.

Within the framework of the data analytics literature, the research was conducted with a series of surveys of 140 FF and NFF marketing executives based in Spain. The items were outlined with topics on direct management support, professional talent, alignment of information technology (IT) with the commercial strategy, quality information for decision making, and the organic structure of data analysts. We apply a sampling technique for convenience, where the interrelation of the FCE associated with a data plan and its relationship with organizational performance is explored. A structural equation model was used to process the survey, and mediation and moderation variables were included. During the survey, the biggest obstacle was to contact the executive director. However, we declare the following objectives of the chapter:

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  Position data analytics as FCE in the FF, for decision making in marketing.

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  Fill the conceptual gap on IT alignment in the FFs business strategy.

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  Propose a conceptual model for a data analytics project, in the context of FF.

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  Demonstrate what type of analyst organization gets the best results for data analytics.

After this introduction, the review of the proposed background, methodology and relationships, solutions, and recommendations and, finally, the conclusions of the study are presented.

Background

This study proposes an FCE configuration to implement data analytics within the firm, which is capable of generating information for commercial decision making. The proposal was made based on the issues that the FFs currently declare relevant to foster growth (talent and organizational structure) (PriceWaterhouseCoopers, 2018). These issues are complemented by the factors that the FF literature mentions as important for the growth and development of the FFs, that is: the direct and explicit support of the administration (Soliman & Janz, 2004), the level of alignment of the information systems and information technology in commercial strategy (Young et al., 1999), the level of professional (Heck, 1998; Premkumar, Ramamurthy, & Nilakanta, 1994), and organic structure (Tung et al., 2000). We have also incorporated some comparisons between FF and NFF, in order to generate greater pragmatic understanding between both property structures. Next, we review the data analytics literature linked to the topics above, and at the end of this section, the hypotheses related to the study are raised.