Augmented analytics is a field of data analytics that utilizes machine learning and artificial intelligence (AI) algorithms to enhance human intelligence. It is designed to automate data analysis and simplify the decision-making process for businesses. The term "augmented" signifies the combination of human and machine efforts to achieve better results.


In essence, augmented analytics is all about making data analysis easier and more accessible to a larger audience. It offers a range of tools and techniques that help organizations extract insights from large datasets with minimal human intervention. The core objective of augmented analytics is to empower business users with easy-to-use tools that can help them make informed decisions based on data.


One of the key features of augmented analytics is its ability to identify patterns and anomalies in data. This is done by using machine learning algorithms that can detect correlations and relationships between different data points. By doing so, augmented analytics can help businesses uncover insights that might have gone unnoticed with traditional data analysis methods.


Another important aspect of augmented analytics is its ability to generate insights in real-time. This is crucial for businesses that need to make quick decisions based on changing market conditions. With augmented analytics, businesses can quickly identify trends and patterns that can be used to develop strategies for growth and expansion.


In conclusion, augmented analytics is a powerful tool for businesses of all sizes. It simplifies data analysis and empowers business users with the tools they need to make informed decisions based on data insights. With the help of machine learning and AI algorithms, augmented analytics is poised to revolutionize the way we make sense of data.