Introduction
Big Data analytics performs predictive analytics for extracting information from the available set of data so as to analyze the patterns and trends to make predictions about results and outcomes that can emerge in the future. The prediction of the future does not happen with surety. The analytical model predicts the result with a scored probability.
Predictive analytics and the future
Everyone is familiar with Data Mining. In the modern era, data mining is associated with retrieving information for large datasets and mapping the relationship between the future variables from the historical data available. However, the accuracy and efficiency of the predictive analytics totally depends upon 2 parameters,
- The quality of data and level of analysis
- Efficiency of the algorithm models
Predictive analytics is not just forecasting, it is a technology that enables businesses to learn from the history and experience to predict the behavior of the targeted market for enabling better decision making. Predictive analytics will help in predicting and preventing threats to void the risk factor and hence collaborated with prescriptive analytics which will boost the value of the business.
Predictive analytics associates with many statistical techniques such as Machine Learning, Data mining, and predictive modeling that enhance with efficiency and accuracy of analysis of existing facts along with the trends and patterns to predict the future.Since predictive analytics completely explores the huge amounts of past data and observes the entire pattern, businesses that deal with big data extensively approach predictive analytics techniques for predicting their future business opportunities and threats.
The predictive models establish relationships between all possible determinants that are almost impossible by humans in practical scenarios which minimize the effort and difficulty of future prediction in all conditions and hence the forecasting is done in the most efficient manner. Purchasing trends and buying behavior of the customer and social media data etc are the oil used in the business predictions and the realistic way of prediction leads to progressive changes in the business.
Big Data and Predictive Analytics
Predictive analytics uses both stored data and real-time data as the fuel for business predictions. The crucial decision taken with the reflection of predictive analytics is capable enough to minimize the business threats since big data - ie huge amounts of data sets -- are fed to the model. As the data increases the quality of the prediction increases. Since big data analytics came into the picture, even though millions of bytes of structured, semi-structured, and unstructured data is created every second, the storage and processing is no longer a threat.
After deploying big data techniques such as Hadoop, MapReduce, Storm, Spark, etc., businesses get valuable insights in real-time.
Since running predictive models on streaming data are possible (Kafka is used for real-time data streaming), the real-time data is combined with the historical data by the model. In terms of application, predictive analytics is volatile and versatile in nature. It has been implemented in various verticals and has shown highly positive results in areas such as stock exchange, healthcare, CRM, fraud detection, etc.