Decision Framework for Engaging Cloud-Based Big Data Analytics Vendors

Introduction

The proliferation of mobile devices and the ability of almost any electronic device to connect to the Internet have significantly increased the amount of data generated by businesses daily. This increase in the magnitude of data is called big data (Hashem et al., 2015); it is difficult to store, process, and analyze using traditional tools, such as relational databases. Big data is distinguished from traditional data by volume, velocity, variety, veracity, and value (Marr, 2015). These characteristics help business managers to make important decisions in real time (Höchtl, Parycek, & Schöllhammer, 2016). The nature and origin of these characteristics can be explained by the data life cycle where a business collects, stores, processes, and makes meaning out of the data at their disposal from generation to insight. Figure 1 illustrates a typical data life cycle where a business uses the insights obtained from the processed data to gather more data. The data life cycle process leads to challenges that typical businesses do not face in their daily operations in dealing with big data, often prohibiting insights if the business is unprepared to handle them.

Many organizations are unable to manage their existing smaller data, and big data adds a layer of complexity, as capabilities are necessary with analytics and storage (Troester, 2012). Thus, despite the pervasiveness of big data technologies, many e-business firms are unable to achieve the elusive status of success (Gupta & George, 2016).

Figure 1.

Data life cycle

This study posits that one explanation for organizations missing out on the success of big data relates to the nature and effect of the contract between vendors providing cloud-based data analytic services and clients receiving those services. Among the opportunities for big data and analytics in the cloud is an ecosystem conceptually referred to as a Service-Oriented Decision Support System (SODSS). Demirkan and Delen (2013) suggest that value can be created through the implementation of accrued knowledge from the interactions of service systems that involve people, technology, organizations, and shared information. There are challenges as vendors, usually third parties, are required to manage these processes unless the core competency of the organization is technology, particularly related to big data, analytics, and the cloud. This study complements prior studies such as Pakath (2015) and Yu (2016) that provide insights for businesses to create value from big data analytics. This study seeks to enhance understanding of economic benefits of analytics literature by investigating the following research questions:

• Question 1: What challenges do e-business organizations face in using cloud-based big data analytics?

• Question 2: What decision factors should e-business organizations consider in their contracts with agents regarding cloud-based big data analytics to achieve competitive parity?

• Question 3: How should e-business organizations manage their contracts with cloud-based big data analytics vendors?

In response to these questions, this study develops a theoretical framework to understand the opportunities and challenges of big data analytics in cloud computing for e-businesses (Amit & Zott, 2001) from an economic perspective to maximize competitive parity (Mata, Fuerst, & Barney, 1995). The findings provide insights to e-business firms into how they can make the most from the potential data available to them and understand the challenges in the process, particularly their decision-making regarding the type of vendor for big data. The rate of adoption of data analytics tools suggests that most firms would continue to use the services of third-party vendors. An examination of client decisions and issues in contracting vendors reduces uncertainties in adopting analytics.