Critical success factors for Big Data adoption in the virtual retail: Magazine Luiza case study.

AutorFelix, Bruno Muniz

I Introduction

Technological development in the past decades increased significantly the volume of available data for organizations, impacting on decision making processes and results. Systems related to Big Data have acquired greater importance to business in recent years, as organizations face a bigger need to analyze abundant and diverse data (Chen, Chiang, & Storey, 2012).

Expected benefits derived from the use of Big Data include integration of data from varied sources, the ability to capture and analyze substantial volume of structured and unstructured data, the enhancement of general analytical capabilities, analysis of information in real time, reduction of analysis and data processing costs, more transparency of information used in the decision making process, faster products and services customization, and the creation of new business models (Davenport, Barth, & Bean, 2012).

Virtual retailing, for example, witnessed an exponential growth with the use of Big Data (Davenport, 2014). The reason behind this is that Big Data introduced techniques in this environment that allowed for a number of actions to be made to enhance the customer experience for those who access a website in search of a product. Moreover, it provided the companies with a greater ability to obtain and analyze consumer

information (Davenport et al., 2012; McAfee & Brynjolfsson, 2012).

Several studies focused on the understanding of critical success factors in the adoption of information systems, including Business Intelligence (BI) systems. Nevertheless, because Big Data systems are relatively new and academic literature still presents insufficient systematized knowledge on the topic, it becomes necessary to explore the critical success factors for the adoption of Big Data.

This study is, therefore, guided by the following research question: are the critical success factors for Big Data adoption the same factors that proved to be relevant for the adoption of previous data analysis systems, such as BI systems? As virtual retail is one of the main users of Big Data in the country (International Data Corporation [IDC], 2014; E-Bit, 2015), an exploratory investigation of the sector was conducted. The research's objective is to identify critical success factors for Big Data adoption by virtual retailers. More specifically, this research is based on the Magazine Luiza case study, a leading company regarding the use of Big Data tools with the purpose of better knowing its customers, optimizing customer loyalty, and generating financial revenue.

This article is organized as follows: first, we present the concept of Big Data and its benefits. We explore factors identified in the literature as potentially relevant for Big Data adoption. Next, we provide a description of the method applied and the case study. In the final considerations we summarize the propositions regarding the critical success factors for Big Data adoption by virtual retailers and point out suggestions for future research.

2 Literature review

2.1 Big Data and its benefits

Big Data concerns datasets whose size is beyond the capacity of typical database software tools for capturing, storing, managing, and analyzing (Bruce, Lenita, & Paul, 2013; Ohlhorst, 2013). The main attributes related to the concept of Big Data are volume, speed and variety (Simon, 2013; Castro, 2014; Macada & Canari, 2014). Volume regards the increasing amount of data which directly impacts organizational processes and influence predictive and statistical methods. Variety concerns the ability to analyze an extensive and diverse range of data and sources, including structured, semi-structured and unstructured data (Ohlhorst, 2013). Finally, speed encompasses the ability for fast data analysis, often in real time (McAfee & Brynjolfsson, 2012).

According to Novo and Neves (2013) and McAfee and Brynjolfsson (2012), the defining characteristics of Big Data are those that differentiate it from traditional BI systems. Big Data has a different approach: the process of storage and analysis is structurally modified so that it leads to the gathering and interaction of all data generated by an organization, enabling a subsequent decision on the use of such data (Ohlhorst, 2013).

From a managerial perspective, the organizations that benefit from Big Data focus on data flow as opposed to stocks--reflected in the processes of how they gain value from data--and they no longer manage based solely on historical data sets and start to put more emphasis on predictive models (Davenport, 2014).

The main benefits of Big Data for management are: (1) cost reduction and revenue increase; (2) enhancement of operational efficiency; (3) better decision making; (4) improvement of products and services (5) improvement in the innovation processes and development of new products and markets. (Leeflang, Verhoef, Dahlstrom, & Freundt, 2014; Silva & Campos, 2014; Minelli, Chambers, & Dhiraj, 2013; Novo & Neves, 2013; Ohlhorst, 2013).

2.2 Relevant factors for Big Data adoption

The adoption of Big Data faces challenges not yet properly covered in academic literature. Since this is a recent set of technologies, we reached for literature covering the managerial aspects of Big Data, as well as literature regarding information systems, especially BI systems. We chose this option because similarly to BI systems, technologies associated to Big Data aim the improvement of decision making process (Davenport, 2014; Yeoh & Koronius, 2010). Second, because Big Data works as an umbrella for several technologies, methodologies and concepts of analysis that are not new, among them the vast majority of traditional BI systems (Ohlhorst, 2013). Finally, because the literature already noticed that some of the Big Data and data analysis projects success factors are similar to those regarding the adoption of BI systems (Davenport, 2014).

2.2.1 Factors related to strategy, processes and leadership

The adoption of a Big Data system may evolve into unpredictable paths (Yeoh & Koronius, 2010). The strategy for Big Data adoption needs to be linked to the organizational strategy, and it also requires a decision on which analytical resources are needed and how they should be applied (Novo & Neves, 2013). Priorities and problems to be solved must be defined, besides measurable standards (Minelli et al., 2013).

The engagement of the top management is important to gain the support and approval from executives to the endorsement of Big Data; the decision making based on analytics; and the identification of the major customers to comprise the data analysis (Novo & Neves, 2013). It also facilitates access to the required operational resources, such as financing and professional skills, aside from its relevance for overcoming change barriers and for reformulation paradigms (Yeoh & Koronius, 2010).

2.2.2 Factors related to human resources

A challenge that Big Data may impose to organizations is the lack of professionals with the required skills and knowledge to deal with data analysis, something defined by Leeflang, Verhoef, Dahlstrom and Freundt, (2014) as talent gap. Several solutions have been proposed to address it, such as the investment in formal education and internal talent training. Talent retention is also a challenge, on account of market demand (Davenport, 2014).

2.2.3 Relevant factors related to implementation management

Some structures and functions are frequently found in IT implementations. Super user groups that engage in the implementation and later return to their original job functions is a well-known practice (Sumner, 1999; Somers, Nelson, & Ragowsky, 2001).

There are two other aspects that should be highlighted regarding the implementation: the project management method and the use of a phased and evolutionary approach. Fast development methodologies are a better fit for larger data analytical applications if compared to conventional approaches. When fast approaches are used, minimum time is wasted in the prior identification of the system and more emphasis is placed on the quick delivery of smaller results. They also accommodate better quick changes in the development plans, having a superior performance in scenarios of uncertainty (Davenport, 2014). The use of a phased approach is viewed as a critical success factor in the adoption of information systems. Splitting the project into phases, through a gradual execution, facilitates the implementation (Gupta, Gupta, & Singhal, 2014).

2.2.4 Factors related to ethics and privacy

Big Data raises unsettling issues regarding ethics, such as what data should be used in an analysis (George, Hass, & Pentland, 2014). Users are not necessarily aware of all the gains derived from information they have posted. It is necessary to debate when and which data may be used in the Big Data strategy given that the difficulty to ensure data safety and privacy can make the project infeasible (Wigan & Clarke, 2013). It is crucial to have a constant ethical questioning not only regarding the use, but also the data collection, the storage and access control (Simon, 2013).

Privacy is among Big Data's main concerns. It refers to information personally identifiable (PII), i.e., information that may be used to identify an individual. The issue of anonymity has raised several debates. Minelli, Chambers and Dhiraj (2013) believe that data collected for a specific purpose can become anonymous and later used for other purposes, such as the identification of collective patterns. The challenge is that the more anonymous the data, the less useful it becomes.

3 Methodological procedures

3.1 Case study: Magazine Luiza

Magazine Luiza is one the major retail chains focused on durable goods and is widely present among Brazilian popular social classes. It holds a base of 36 million registered customers, 30% of them still active. The group is comprised of 736 stores, more than 24 thousand...

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