It is important to recognize areas that share a connection with machine learning but cannot themselves be considered part of machine learning. Some disciplines may overlap to a smaller or larger extent, yet the principles underlying machine learning are quite distinct:
Business intelligence (BI) and reporting: Reporting key performance indicators (KPI's), querying OLAP for slicing, dicing, and drilling into the data, dashboards, and so on that form the central components of BI are not machine learning.
Storage and ETL: Data storage and ETL are key elements in any machine learning process, but, by themselves, they don't qualify as machine learning.
Information retrieval, search, and queries: The ability to retrieve data or documents based on search criteria or indexes, which form the basis of information retrieval, are not really machine learning. Many forms of machine learning, such as semi-supervised learning, can rely on the searching of similar data for modeling, but that doesn't qualify searching as machine learning.
Knowledge representation and reasoning: Representing knowledge for performing complex tasks, such as ontology, expert systems, and semantic webs, does not qualify as machine learning.