n.Data warehousing

Data warehousing used to imply storing organized data in relational database management systems so that it could be analyzed for business intelligence, reporting, and simple dashboarding. Cloud data centers and data warehouses in semi-structured and unstructured data stores, such as Hadoop, are available today. Although data warehouses have historically been used to store and analyze historical data, many new approaches can now provide in-depth, real-time data analysis.

o.Machine Learning and Artificial Intelligence

Some of the most advanced advances in data mining are machine learning and artificial intelligence (AI). When operating with large amounts of data, advanced machine learning techniques such as deep learning provide extremely accurate predictions. As a result, they can be used to process data in AI applications such as computer vision, speech recognition, and advanced text analytics using Natural Language Processing. These data mining techniques work well with semi-structured and unstructured data to determine meaning.

## **3.4 Data mining software for optimization**

With so many methods to use during data mining, it's important to have the right resources to get the most out of your analytics. For proper implementation, these methods usually necessitate the use of many different tools or a tool with a broad set of capabilities.

While organizations can use data science tools like R, Python, or Knime for machine learning analytics, it's critical to use a data governance tool to ensure compliance and proper data lineage. Additionally, in order to conduct analytics, companies would need to collaborate with repositories such as cloud data stores, as well as dashboards and data visualizations to provide business users with the knowledge they need to comprehend analytics. All of these features are available in tools, but it's critical to find one or more that meet your company's requirements [16].
