7 steps to Predictive Analytics
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The document outlines a structured approach to predictive analytics, including steps such as defining project outcomes, data collection, analysis, modeling, and deployment. It emphasizes the importance of data validation and model monitoring to ensure accuracy and relevance in decision-making processes. Predictive analytics is characterized as a means for organizations to forecast events, assess risks, and enhance automated decision-making using historical data.
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7 steps to Predictive Analytics
- 1. PROJECT DEFINITION DATA COLLECTION STEP 1 What are the project outcomes? Do they relate to business goals? What sets of data will be used during the project? Ingest data from the various sources in one place. Depending on the types of data, a data lake might be the best option. STEP 2 DATA ANALYSIS STEP 3 Inspect, clean and transform your data. Explore this data to identify trends and patterns MODEL MONITORING STEP 7 Regularly monitor your models to check performance and ensure you have the desired results. STATISTICS Use statistical methods to validate your findings, and test your hypothesis before you apply a data model. STEP 4 MODELLING STEP 5 DEPLOYMENT Deploy the analytical model into your decision- making processes. STEP 6 Once validated, develop your model to predict future patterns. The model needs to be evaluated for accuracy. PREDICTIVE ANALYTICS PROCESS Predictive Analytics enables organisations to forecast future events, analyse risks and opportunities, and automate decision making processes by analysing historic data. www.whishworks.com