Final answer:
Big Data analytics is the process of analyzing vast amounts of data to improve business decision making. This can involve various technologies such as OLAP for data analysis, data warehousing for data storage, and OLTP for managing transaction-oriented applications which contribute to big data.
Step-by-step explanation:
The process to analyze vast amounts of data ('Big Data') for better decision making related to the business is referred to as Big Data analytics. This analytical method employs advanced analytic techniques against very large, diverse data sets that include structured, semi-structured, and unstructured data, from different sources, and in different sizes. Data analytics enables businesses and organizations to use their data to identify new opportunities, leading to smarter business moves, more efficient operations, higher profits, and happier customers.
Online Analytical Processing (OLAP) is another related concept which refers to a category of software tools that provides analysis of data stored in a database. OLAP tools enable users to analyze different dimensions of multidimensional data. It is an important part of business intelligence, which also encompasses relational databases, report writing, and data mining.
Data warehousing is a central repository of data where data from various sources is stored. This data is later used for reporting and data analysis and can be a significant part of the overall strategy for organizing and analyzing vast amounts of data for decision making.
Online Transaction Processing (OLTP), on the other hand, is a class of systems that facilitate and manages transaction-oriented applications, typically for data entry and retrieval transaction processing. Although OLTP is not directly involved in data analysis, it can contribute to Big Data when such transactions are gathered and integrated into a data warehouse for analytical purposes.