As the process of analyzing raw data to find trends and answer questions, the definition of data analytics captures its broad scope of the field. However, it includes many techniques with many different goals. The data analytics process has some components that can help a variety of initiatives. By combining these components, a successful data analytics initiative will provide a clear picture of where you are, where you have been and where you should go. The work of a data analyst involves working with data throughout the data analysis pipeline. This means working with data in various ways. The primary steps in the data analytics process are data mining, data management, statistical analysis, and data presentation. The importance and balance of these steps depend on the data being used and the goal of the analysis. Data mining is an essential process for many data analytics tasks. This involves extracting data from unstructured data sources. These may include written text, large complex databases, or raw sensor data. The key steps in this process are to extract, transform, and load data (often called ETL.) These steps convert raw data into a useful and manageable format. This prepares data for storage and analysis. Data mining is generally the most time-intensive step in the data analysis pipeline. Data management or data warehousing is another key aspect of a data analyst’s job. Data warehousing involves designing and implementing databases that allow easy access to the results of data mining. This step generally involves creating and managing SQL databases. Non-relational and NoSQL databases are becoming more common as well.