Advanced Data Analytics

Ali Soofastaei

Introduction

The digital age with its possibilities and uncertainty confounds industries and economies, with substantial possible knowledge in each section. A new model for the company in this time is being generated by the tendency toward the data lake and drawing upon the hidden knowledge. The influence of information leads companies to be agile and to achieve their objectives. Big data analytics (BDA) allows companies to recognize, evaluate, anticipate, and administer covert opportunities for growth to attain commercial value [1]. In order to generate information from that data, BDA uses advanced analysis techniques that affect the decision-making process to reduce the complexity of the process [2]. In order for BDA to process, analyze, and perform highly precise analysis in real time, it needs a new and advanced algorithm. Computer and deep learning (DL) use this tool to distribute their complex algorithms and consider the problematic approach [3].

A vast amount of data, profound learning and algorithms, machine learning (ML), and similar approaches have been included in this investigation. This offers a theoretical model for the algorithm's relationship, which facilitates the implementation of IoT data for BDA by researchers and practitioners.

Figure 2.1 illustrates the process of thinking about DL and ML approaches.

Big Data

The creation of a large number of raw data is one of the major implications of the digital world. The manager's role is concerned with the distribution of such important resources in various forms and sizes based on the needs of the organizations. Big data have the ability in every part of society to influence the social aspects of education. The subject of raw data management is becoming far more important as data volumes increase, in particular, in the

FIGURE 2.1

Big data analysis approaches.

technology-based companies. Through contrast, advanced techniques can overcome their complexity, through comparison with raw data features such as variety, velocity, and the volume of big data. Therefore, for "experiment," "simulation," "data analysis," "monitoring," BDA were suggested. As one of the BDA approaches, ML provides a system that allows predictive analysis dependent on supervised and unattended data input. In mutual partnerships, the influence of computer science analysis and data entry exists. The better and more reliable suggestions, the analyses will be. DL is also used as an input of the machine to learn from secret data patterns [3].

 
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