Difference Between Big Data and Predictive Analytics

Big Data is huge, large or voluminous data, information, or the relevant statistics acquired by the large organizations and ventures. Many software and data storage is created and prepared as it is difficult to compute the big data manually. It is used to discover patterns and trends and make decisions related to human behavior and interaction technology. 


Predictive Analytics encompasses making predictions about future outcomes by studying current and past data trends. It utilizes data modeling, data mining, machine learning, and deep learning algorithms to extract the required information from data and project behavioral patterns for the future. Some industry tools used for Predictive analytics are Periscope Data, Google AI Platform, SAP Predictive Analytics, Anaconda, Microsoft Azure, Rapid Insight Veera, and KNIME Analytics Platform.

Difference between Big Data and  Predictive Analytics


Big Data

 Predictive Analytics

1. Big Data is group of technologies. It is a collection of huge data which is multiplying continuously.  Predictive analytics is the process by which raw data is first processed into structured data and then patterns are identified to predict future events.
2. It deals with the quantity of data, typically in the range of .5 terabytes or more. It deals with the application of statistical models to existing data to forecast.
3. It’s a best practice for enormous data. It’s a best practice for data for future prediction.
4. It has a vast backend technology imports for Dashboards and Visualizations like D3js and some paid ones like Spotfire a TIBCO tool for reporting.  It has tool with built-in integrations of the reporting tools like Microsoft BI tools. So, no need to fetch it from source or from some outside vendors.
5. Its engines like Spark and Hadoop comes with built-in Machine Learning libraries but the incorporation with AI is still an R&D task for the Data Engineers.  It deals with the platform based on the probability and mathematical calculation. 
6. It has high level of advancement, its engines have eventually upgraded themselves throughout the development processes and level of cross-platform compatibility.  It has medium level of advancement, has a limited change of algorithmic patterns as they are giving them better score from the start with respect to their field and domain-specific work analysis.
7. It is used to make data driven decsions.  It is used for risk evaluation and prediction of future outcomes.


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