Predictive analytics is one of the subdivisions of advanced analytics that seeks to predict future events by referencing historical events. With the advent of disparate machine learning methods and statistical methods, the predictive analytics system significantly captures the potential insights from the large volume data chunks to predict behavior patterns and future trends. In order to improve the bottom line, optimize the processes, discover opportunities and risks, and guide decision making, most organization relies on predictive analytics.
It is the crucial data-driven methodology to recognize the concealed pattern from the unprecedented data for predicting probabilities of forthcoming according to the past occurrence knowledge. Leveraging predictive analytics on big data brings tangible business benefits to the organization in which predictive analytics enable the in-depth insight of customer behavior. The predictive analytics shows their supremacy of performance in various fields, including the banking sector, fraud detection, risk management system, health care, dynamic product pricing, sales forecasting, weather forecasting, and direct marketing.
Most organizations seek to take advantage of predictive analytics to gain significant benefits through the faster identification of risks and opportunities. Even though they are lacking in some cases, the significant one is the inadequacy of up-to-date data that severely impact the efficiency of the results. Some enterprises have scientific experts to overcome the circumstance of predictive analytics and make an accurate prediction. However, the lack of resources, including a budget, intends to degrade the accuracy of the system.
The fusion of predictive analytics with big data brings valuable insights from large-scale data. However, it often suffers from issues while getting the real, adequate, and perfect data to discover the efficiency of its model. Moreover, some of the prediction models do not bring the relevant results owing to the lack of selection knowledge of the variables and algorithm that negatively impact the accuracy of prediction. In business applications, predictive analytics need to incorporate efficient data management strategies to integrate and standardize the incoming heterogeneous data. The lack of efficient data management misleads the prediction.
• Predictive Analytics is an advanced analytics province of interest to almost all communities and organizations by utilizing intelligence data for forecasting with data mining algorithms and predictive modeling.
• Prescriptive analytics is contemplated as progress towards increasing data analytics sophistication and contributing to making optimized decisions beforehand for performance improvement in redistricted range of applications. • In the healthcare sector, predictive analytics aid in the prevention of patients emergency health conditions at a low cost in the long term.
• Recently, predictive analytic algorithms have achieved accurate and timely anomaly predictions with the help of healthcare mining, deep learning, and data abstraction techniques.
• Intelligent predictive maintenance has gained much attention from researchers as it improvises production efficiency by predicting pending failures and diminishing unexpected breaks with the satisfaction of instant maintenance demands of industrial facilities.
• Although predictive analytics has grown more prevalent in the past few years, its implementation is complex and comes with challenges.
• More advanced technological improvement with big data needs to address the challenges of ensuring predictive analytics dispatch on their promise of better value and outcomes in a wide range of application domains.