Predictive Analytics

- Overview

Data science is a broad field that includes predictive analytics, a quantitative discipline that uses data to predict future outcomes: 

Data Science is the field that uses algorithms, data mining, and machine learning (ML) to extract insights from data and build predictive models. Data scientists use statistical and computational methods to develop new algorithms and build models that can predict future outcomes.

- Predictive Analytics

Predictive analytics is a branch of data science that uses data analytics, machine learning (ML), and statistical models to find patterns that predict future behavior. Predictive analytics can be used to help organizations streamline operations, increase revenue, and reduce risk.

Predictive analytics can help organizations identify trends and predict future scenarios. For example, in marketing, predictive analytics can be used to predict sales trends and plan campaigns. Predictive analytics can also be used to improve security by triggering specific security procedures when suspicious user behavior is detected.

- Augmented Analytics

Augmented analytics is a data analytics approach that uses machine learning (ML) and other technologies to automate tasks that were previously done by data scientists or specialists:  

• Automating processes: ML can automate complex analytics processes, such as data preparation and insight generation.
• Improving decision-making: Augmented analytics can help users make better data-driven decisions by providing insights that would otherwise be difficult to obtain.
• Increasing accuracy: Machines can perform repetitive tasks and calculations extremely well, which can help users avoid confirmation bias. 

- Predictive Analytics vs Augmented Analytics

Predictive analytics and augmented analytics are both data science techniques that use data to gain insights, but they differ in their focus and capabilities:

• Predictive analytics: Uses historical data and statistical and ML techniques to predict future outcomes. Predictive analytics can help businesses streamline operations, increase revenue, and reduce risk.
• Augmented analytics: Uses ML and natural language processing to help users understand large amounts of data and make decisions. Augmented analytics can automate repetitive tasks and make it easier for users to uncover insights.

Here are some more details about predictive analytics and augmented analytics:

• Predictive analytics uses cases: Predictive analytics can be used for fraud detection, customer segmentation, risk reduction, and more.
• Augmented analytics uses cases: Augmented analytics can help users make recommendations based on data, such as recommending data sources to join or chart types to use.
• Augmented analytics capabilities: Augmented analytics can include automated data preparation, natural language processing, and AI-driven recommendations.
• Augmented analytics and predictive analytics together: Predictive analytics and augmented analytics can be used together to improve decision-making and problem-solving.