Difference Between Data Analytics and Predictive Analytics

Data Analytics: It is the process of deducing the logical sets and patterns by filtering and applying required transformations and models on raw data. The following steps can be followed to explore the behavioral pattern of data and draw the necessary conclusions.
The top tools available for data analytics in the market are R Programming, Python, SAS, Tableau Public, KNIME, Apache Spark, Excel, QlikView, and OpenRefine.

Predictive Analytics: It 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 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.


Below is a table of differences between Data Analytics and Predictive Analytics:

Features Data Analytics Predictive Analysis
Definition Inspecting and refining data to draw conclusion from dataset. Examining and operating the current and past data trends to infer pattern for making predictions based on it.
Objective Utilized to make data driven decsions. Utilized for risk evaluation and prediction of future outcomes.
Approach Traditional Algorithmic and mechanical processes are used to build deep insights on data. Advanced computational models and algorithms are used for building a forecast or prediction platform
Procedure Raw data is collected, cleaned, structured and transformed to derive data product. Clean data is used to build predictive model which is later deployed and monitored to check progress.
Outcome The outcome is based on customer requirements. It may or may not be predictive. The outcome is a reliable predictive model generated by testing hypothesis and assumptions.
Prerequisite Data Analyst requires strong statistical knowledge. Predictive analytics requires strong technical and fundamental statistical knowledge.
Industry Application Fraud and Risk Detection, Delivery Logistics, Customer Interactions, Digital Advertisement etc. Sales Forecasting, Crisis Management, Analytical customer relationship management, Clinical decision support systems (CRM)etc.
Application for Data Scientists Utilized to verify models, theories and hypothesis. Utilzed to build confidence in predictions by using specailzed models.
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