Data reporting is the process of collecting, analysing and presenting data in a format that is easy to understand, so that it can be used to make informed business decisions.

Data discovery is the process of finding, collecting, and analysing data to identify patterns and relationships that can provide valuable insights.

Machine learning is a subset of artificial intelligence that involves using algorithms and statistical models to enable computers to learn from data and make predictions or decisions without being explicitly programmed.

Automation is the use of technology to perform tasks without human intervention, it can be used to automate repetitive tasks, improve efficiency, and reduce errors.

Data analytics can be applied to a wide range of data types, including structured data (such as numbers, dates, and text) and unstructured data (such as images, videos, and social media posts).

Data analytics can be used to identify patterns, trends, and opportunities in business data, which can help to improve decision making, increase efficiency, and drive growth.

Data reporting provides businesses with valuable insights that can help them make better decisions, identify areas for improvement, and track performance over time.

Data discovery is a more exploratory process, it is used to identify patterns and relationships in data, while data analytics is focused on extracting specific insights and making predictions.

Traditional data analysis is based on pre-determined rules and hypotheses, while machine learning algorithms can learn from the data and make predictions or decisions without being explicitly programmed.

Some common use cases for automation in data analytics include data cleaning, data processing, data visualization, and report generation. Automation can also be used to streamline and improve the accuracy of machine learning models.