1. Introduction

Big data analytics (BDA) represents an important part of an information system for development and evolution within organisations. Looking into the literature, a variety of definitions exist for big data analytics, including: "a help to predict future volumes, gain insights, take proactive actions, and give way to better strategic decision-making". Other definitions provide similar interpretations of the term. Whereas Big Data Analytics described BDA as "advanced analytic techniques operating on big data sets to help discover what has changed and how we should react". Furthermore, Medical big data: Promise and challenges stated that BDA differ from traditional analytical approaches as instead of tracking care quality and outcomes in a retrospective view by using deductive reasoning, it uses inductive reasoning for prospective analysis of data. The definition in Big Data Analytics considered data analytics as data analytic techniques, which indicates a vague understanding of the difference between data analytics methods and data analytics techniques.

There are many challenges in big data, the different types of big data challenges were discussed by Big Data Computing and Critical analysis of big data challenges and analytical methods. The broad challenges of big data (BD) were grouped into three main categories, based on the data life cycle: data, process and management challenges. However, suggesting how BDA methods and techniques could address these challenges is out of the paper's scope.

The purpose of this research is to design a correlation taxonomy of the existing methods and its associated techniques. This taxonomy is derived from the limitation in the existing literature, which will be further discussed below after finding the correlation between the methods and the associated techniques of BDA. The research is constructed according to a design science research (DSR) approach used to design the taxonomy. Furthermore, this paper presents useful budding research in the field of BDA to give a clear understanding of the basic data analytics concepts, to early stage and interested researchers.

The remainder of this paper is structured as follows: Section 2 includes the necessary background with an overview of related literature for big data characteristics definitions and BDA usability, which is followed by Section 3 which includes definitions, different classification of BDA methods based on literature review and the authors' classification preference. Section 4 highlights the need for this research and constitutes of the research methodology, literature review, analysis for the correlation between BDA methods, correlation between BDA methods, the associated techniques, and finally analysis for techniques used in research papers. Section 5 is discussion of the authors' proposed taxonomy and Section 6 highlights the conclusions and outlines the research limitations and potential for further research.