Unified Data Analytics Platform For Financial Sector Using Big Data

Abstract

Organizations in today's data-driven digital economy are seeking ways to leverage the immense value of massive amounts of information so as to make more informed decisions. They are able to not only uncover new prospects, but also learn more and improve their performance thanks to big data analytics. Although many businesses have poured resources into big data analytics projects, most have failed to reap the benefits. While there has been a lot of research into the topic of big data analytics, relatively little is known about the strategies that organizations use to merge the various aspects of this discipline. The authors of this paper attempt to solve the problem by developing a comprehensive big data analytics maturity model that can be used by executives to evaluate their own proficiency levels and plan for future development. In this research, we combined traditional quantitative techniques with qualitative meta-synthesis. To begin, we conducted an extensive literature search to catalog the skills and procedures associated with big data analytics maturity. Later, using a quantitative survey method, experts' perspectives on the proposed fundamental skills and practices were analyzed and graded. Finally, a focus group was used to allocate the capabilities to maturity levels according to their priority of deployment, taking into account the architecture of the big data analytics maturity model. We propose a model with four primary capabilities, nine key dimensions (KDs), and five stages of development. Its framework is CMMI-based, which stands for "capability maturity model integration." Questionnaires and focus groups were used to illustrate the big data maturity model. As a guide for effective adoption of big data analytics, we provide the capabilities and KDs, together with their suggested deployment order and weight in the maturity model. Using the offered methodology, businesses may assess their current big data analytics capabilities and steer themselves toward the most effective paths for improvement. Because of this, it enables managers to assess their strengths and weaknesses and establish investment objectives. This work uses a meta-synthesis, which hasn't been done in this subject before, to create an extensive maturity model. The proposed method makes substantial contributions to large data research, and it is both descriptive and prescriptive. A framework is presented in this paper for evaluating different big data analytics projects and settling on a coherent plan of action for their future growth and development.