Synthesizing Data Analytics towards Intelligent Enterprises

Abstract

In today's world the amount of data available to organizations every day continues to proliferate at a staggering volume. Using them in an efficient way enterprises will be able to forecast revenues more accurately, improve overall business and make better decisions about new product investment. Data analytics plays a key role to use these datas effectively and can help enterprises to be more resilient, profitable and sustainable. The data driven from enterprises naturally fall into four different kinds of data analytics namely Descriptive, Diagnostic, Predictive & Prescriptive depending on the question it helps to answer. These can equip the decision makers to describe past results, diagnose past results occurrence, predict future happenings and recommend the necessary actions for the organization's next steps. Armed with deeper insights and recommendations the enterprises can gain a better understanding of their performance as a whole and can make better decisions as a result are termed as Intelligent enterprises. In this work, we will apply a mix of machine learning algorithms like Stacked LSTM model and Tf-idf vectorizer which have been utilized for different types of prediction. The core idea is to showcase of these types of algorithms can effectively predict various kinds of outcomes.