• MOHAMMED JUNED SHAIKH SHABBIR
• C. M. MANKAR

big data analytics is the new practice in Business Analytics today. However, recent industrial surveys find that big data analytics may fail to meet business desires because of lack of business context and lack of expertise to connect the dots, incorrect scope and cluster arranged framework. In this paper, we present a goal-oriented big data analytics framework for better business decisions, which consists of a conceptual model which connects a business side and a big data side providing context information around the data, a claim-based evaluation method which enables to focus the most effective solutions, a process on how to use proposed framework and an assistant tool which is a real-time big data analytics platform. In this framework, problems against business goals of the current process and solutions for the future process are explicitly hypothesized in the conceptual model and validated on real big data using big queries or big data analytics. As an exact examination, a shipment choice process is utilized to indicate how IRIS can bolster better business choices as far as extensive understanding both on business and information investigation, high need and quick choices.

Big Data Analytics, Goal-Orientated Approach, Business Process, Big Data, Conceptual Model.

"The role of predictive data analytics in retailing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 6, page no.618-623, June-2020, Available :http://www.jetir.org/papers/JETIR2006427.pdf

"The role of predictive data analytics in retailing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 6, page no. pp618-623, June-2020, Available at : http://www.jetir.org/papers/JETIR2006427.pdf