Difference Between Data Analytics and Predictive Analytics Last Updated : 30 Sep, 2022 Comments Improve Suggest changes Like Article Like Report 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: FeaturesData AnalyticsPredictive AnalysisDefinitionInspecting 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.ObjectiveUtilized to make data driven decisions.Utilized for risk evaluation and prediction of future outcomes.ApproachTraditional 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 platformProcedureRaw 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.OutcomeThe 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.PrerequisiteData Analyst requires strong statistical knowledge.Predictive analytics requires strong technical and fundamental statistical knowledge.Industry ApplicationFraud 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 ScientistsUtilized to verify models, theories and hypothesis.Utilized to build confidence in predictions by using specailzed models. Comment More infoAdvertise with us Next Article Difference Between Data Analytics and Predictive Analytics S shreyanshisingh28 Follow Improve Article Tags : Data Analysis data-science Data Analytics Similar Reads Difference Between Business Analytics and Predictive Analytics Business Analytics: Business Analytics is a branch of Business Intelligence which primarily focuses on the capacity to gain an accurate and deep understanding of business performance based on data and statistical methods. It makes substantial and comprehensive use of analytical modeling and numerica 3 min read Difference between Data Analytics and Data Analysis 1. Data Analytics : Analytics is a technique of converting raw facts and figures into some particular actions by analyzing those raw data evaluations and perceptions in the context of organizational problem-solving and also with the decision making. Analytics is the discovery and conversation of sig 2 min read Difference Between Customer Analytics and Web Analytics Customer Analytics: Customer Analytics can basically be defined as the process which makes the use of analytics to study the customer behavior which helps in effective business decision making. So it can be defined as a process of studying customer data and information for understanding and interpre 3 min read Difference Between Data Mining and Data Analysis 1. Data Analysis : Data Analysis involves extraction, cleaning, transformation, modeling and visualization of data with an objective to extract important and helpful information which can be additional helpful in deriving conclusions and make choices. The main purpose of data analysis is to search o 2 min read Difference Between Data Analysis and Data Interpretation Data analysis and Data Interpretation come pretty close; the only difference is in their roles in the data-driven process. In the process, it is all about the systematic inspection, cleaning, transformation, and modelling of the data to discover useful information, patterns, or trends—it mainly diss 6 min read Like