Conclusions and research directions
In this article, a theoretically sound framework for value creation by using Big Data Analytics is presented. The objective of this article is to bridge the gap by focusing on value-discover, value-creation, and value-realization by using big data management, processing and advanced analytics. Because, there are numerous challenges for traditional analytics in terms of scalability, adaptability, and usability, presenting new opportunities for inspiring enterprises to adopt BDA for it for decision-making. The answers to RQs used in our paper are as follows: By considering the first research question, BDA explored the most important seven characteristics namely Volume, Velocity, Variety, Valence, Veracity, Variability, and Value. A large research is being done in defining BDA in terms of Vs for data challenges. BDA has been prospected to raise the economic returns by gaining deeper insights from mountains of existing data. Our response to second research question has provided an overview of the architecture of BDA-DM framework including six components, namely (i) data generation, (ii) data acquisition, (iii) data storage, (iv) advanced data analytics, (v) data visualization, and (vi) decision-making for value creation. Our third research question has presented the detailed information of BDA tools, techniques and technologies. This field has received much attention due to its wide application as multi-purpose tools, borrowing techniques from Natural Language Processing (NLP), Data Mining (DM), Machine Learning (ML), Deep Learning (DL) etc. Currently, benchmarking software technologies such as e.g. (Hadoop/Map-Reduce based processing frameworks), NoSQL databases, graph data-bases and analytical frameworks have been developed for BDA.
