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Frequently Asked Questions about Analytics
Predictive analytics is an advanced analytics technique that uses historical data, machine learning, data science and statistics to predict the likelihood of an action or event occurring.
Predictive analytics is a branch of advanced analytics that makes predictions about future outcomes using historical data combined with statistical modeling, data mining techniques and machine learning. Companies employ predictive analytics to find patterns in this data to identify risks and opportunities.
Predictive analytics is vital for detecting fraud, optimizing marketing, reducing risk, and improving operations.
Using predictive analytics, organizations can:
- Gain a 360-degree of the customer based on past and present behavior
- Determine which customers are most likely to be profitable
- Optimize marketing campaigns so that they’re more targeted to the individual customer
- Forecast future demand for different products and services
- Engage in more proactive risk management
- Strategically allocate resources in order to generate the greatest returns
- Stay on top of the latest trends and gain a competitive advantage
Predictive analysis is a five-step process that defines requirements, explores data, develops models, deploys models, and validates results.
- Problem identification: Before you start your data analytics project, you should identify what problem you are trying to solve.
- Data collection: In this step, you identify the sources for your data and collect data from them.
- Data validation: You may need to prepare data for analytics by transforming it and correcting data quality issues.
- Data analysis: Once your data is ready, you can deploy an analytics tool to create models and data visualizations. Data modeling is the process of describing the structure, associations, and constraints relevant to available data, eventually encoding these rules into a reusable standard. It’s an integral part of the planning stage for any analytics deployment or business intelligence project.
- Data interpretation: After you’ve completed analysis, you can interpret the results. Visualizations allow you to make sense of complex analytics results.
- Decision and deployment: The final step is evaluating the results of your analytics and making decisions based on them.
It is used for many things like predicting the weather, targeting ads, developing pricing strategies, managing budgets and forecasting the impact of external events.
Predictive analysis is used in fraud detection, marketing, predicting required maintenance, and supply chain management.
- Sales Forecasting
- Marketing Analysis
- Product Maintenance
- Credit Risk and Fraud Prevention
Diagnostic analytics looks at the “why” to determine what caused past events to occur.
Diagnostic analytics is a form of advanced analytics that examines data or content to answer the question, “Why did it happen?”
Diagnostic Analytics uses data drilling to uncover more detailed data surrounding a specific trend, data mining that finds patterns and associations in large amounts of data, and correlation analysis that looks at how specific variables are linked to determine their importance.
It enables companies to make more-informed decisions about how to remediate problems and drive continued success.
It allows you to ask the right questions and search for the answers, test hypothetical evidence about past events, determine cause and effect, and understand what mistakes caused events.
Diagnostic analytics enables you to extract value from your data by posing the proper questions and conducting in-depth investigations into the responses.
The main use cases are healthcare analytics, marketplace analysis, and fraud prevention.
Financial teams can use diagnostic analytics to compare revenue growth or decline to help find patterns across initiatives